AI, and its integration with society, had an incredible acceleration in recent months. By now, it seems certain that AI will be the fourth GPT (General Purpose Technology) of human history: one of those few technologies or inventions that radically and indelibly change society. The last of these technologies was ICT (internet, semiconductor industry, telecommunications); before this, electricity and the steam engine were the first 2 GPTs.

All three GPTs had a huge impact on the overall productivity and advancement of our society with, of course, a profound impact on the world of work. Such an impact, though, was very different across these technologies. The advent of electricity and the steam motor allowed the displacement of large masses of workers from more archaic and manual jobs to their equivalent jobs in the new industrial era, where not many skills were required. The advent of ICT, on the other hand, has generated enormous job opportunities, but also the need to develop meaningful skills to pursue them.

As a result, an increasingly large share of the economic benefit deriving from the advent of ICT has gradually been polarized towards people who had (and have) these skills in society. Suffice it to say that, already in 2017, the richest 1% of America owned twice the wealth of the “poorest” 90%.

It is difficult to make predictions about how the advent of AI will impact this trend already underway. But there are some very clear elements: one of these is that quality education in technology (and not only) will increasingly play a primary role in being able to secure the best career opportunities for a successful future in this new era.

To play a “lead actor” role in this change, though, the world of education – and in particular that of undergraduate and postgraduate education – requires a huge change towards being much more flexible, aligned to today’s needs of students and companies, and affordable.

This model needs to be seriously challenged and adapted to the times: solid foundational learning remains an essential prerogative. But in a “fast” world in rapid change like today’s, knowledge acquired along this “linear” path will not be able to accompany people in their professions until the end of their careers. The “utility period” of the knowledge we acquire today reduces every day, and this emphasizes how essential continuous learning is throughout our lives.

The transition must therefore be towards a more circular pattern for learning. A model in which one returns “to the school desk” several times in life, in order to update oneself, and forget “obsolete” knowledge, making room for new production models, new ways of thinking, organizing, and new technologies.

In this context, Education providers must rethink the way they operate and how they intend to address this need for lifelong learning.

Higher Education Institutions, as accredited bodies and guarantors of the quality of education (OPIT – Open Institute of Technology among these), have the honor of playing a primary role in this transition.

But also the great burden of rethinking their model from scratch which, in a digital age, cannot be a pure and simple digital transposition of the old analog learning model.

The Institutions Universities are called upon to review and keep updated their own study programmes, think of new, more flexible and faster ways of offering them to a wider public, forge greater connections with companies, and ultimately provide them with students who are immediately ready to successfully enter the dynamics of production. And, of course, be more affordable and accessible: quality education in the AI era cannot cost tens of thousands of dollars, and needs to be accessed from wherever the students are.

With OPIT – Open Institute of Technology, this is the path we have taken, taking advantage of the great privilege of being able to start a new path, without preconceptions or “attachment” to the past. We envision a model of a new, digital-first, higher education institution capable of addressing all the points above, and accompany students and professionals throughout their lifetime learning journey.

We are at the beginning, and we hope that the modern and fresh approach we are following can be an interesting starting point for other universities as well.

Authors

Prof. Francesco Profumo, Rector of OPIT – Open Institute of Technology
Former Minister of Education, University and Research of Italy, Academician and author, former President of the National Research Council of Italy, and former Rector of Politecnico di Torino. He is an honorary member of various scientific associations.

Riccardo Ocleppo, Managing Director of OPIT
Founder of OPIT, Founder of Docsity.com, one of the biggest online communities for students with 19+ registered users. MSc in Management at London Business School, MSc in Electronics Engineering at Politecnico di Torino

Prof. Lorenzo Livi, Programme Head at OPIT
Former Associate Professor of Machine Learning at the University of Manitoba, Honorary Senior Lecturer at the University of Exeter, Ph.D. in Computer Science at Università La Sapienza.

A Practical Guide to Thriving in Today’s Job Market Powered by AI and Computer Science

Click this link to read and download the e-book.

Reinforcement learning is a very useful (and currently popular) subtype of machine learning and artificial intelligence. It is based on the principle that agents, when placed in an interactive environment, can learn from their actions via rewards associated with the actions, and improve the time to achieve their goal.

In this article, we’ll explore the fundamental concepts of reinforcement learning and discuss its key components, types, and applications.

Definition of Reinforcement Learning

We can define reinforcement learning as a machine learning technique involving an agent who needs to decide which actions it needs to do to perform a task that has been assigned to it most effectively. For this, rewards are assigned to the different actions that the agent can take at different situations or states of the environment. Initially, the agent has no idea about the best or correct actions. Using reinforcement learning, it explores its action choices via trial and error and figures out the best set of actions for completing its assigned task.

The basic idea behind a reinforcement learning agent is to learn from experience. Just like humans learn lessons from their past successes and mistakes, reinforcement learning agents do the same – when they do something “good” they get a reward, but, if they do something “bad”, they get penalized. The reward reinforces the good actions while the penalty avoids the bad ones.

Reinforcement learning requires several key components:

• Agent – This is the “who” or the subject of the process, which performs different actions to perform a task that has been assigned to it.
• Environment – This is the “where” or a situation in which the agent is placed.
• Actions – This is the “what” or the steps an agent needs to take to reach the goal.
• Rewards – This is the feedback an agent receives after performing an action.

Before we dig deep into the technicalities, let’s warm up with a real-life example. Reinforcement isn’t new, and we’ve used it for different purposes for centuries. One of the most basic examples is dog training.

Let’s say you’re in a park, trying to teach your dog to fetch a ball. In this case, the dog is the agent, and the park is the environment. Once you throw the ball, the dog will run to catch it, and that’s the action part. When he brings the ball back to you and releases it, he’ll get a reward (a treat). Since he got a reward, the dog will understand that his actions were appropriate and will repeat them in the future. If the dog doesn’t bring the ball back, he may get some “punishment” – you may ignore him or say “No!” After a few attempts (or more than a few, depending on how stubborn your dog is), the dog will fetch the ball with ease.

We can say that the reinforcement learning process has three steps:

1. Interaction
2. Learning
3. Decision-making

Types of Reinforcement Learning

There are two types of reinforcement learning: model-based and model-free.

Model-Based Reinforcement Learning

With model-based reinforcement learning (RL), there’s a model that an agent uses to create additional experiences. Think of this model as a mental image that the agent can analyze to assess whether particular strategies could work.

Some of the advantages of this RL type are:

• It doesn’t need a lot of samples.
• It can save time.
• It offers a safe environment for testing and exploration.

The potential drawbacks are:

• Its performance relies on the model. If the model isn’t good, the performance won’t be good either.
• It’s quite complex.

Model-Free Reinforcement Learning

In this case, an agent doesn’t rely on a model. Instead, the basis for its actions lies in direct interactions with the environment. An agent tries different scenarios and tests whether they’re successful. If yes, the agent will keep repeating them. If not, it will try another scenario until it finds the right one.

What are the advantages of model-free reinforcement learning?

• It doesn’t depend on a model’s accuracy.
• It’s not as computationally complex as model-based RL.
• It’s often better for real-life situations.

Some of the drawbacks are:

• It requires more exploration, so it can be more time-consuming.
• It can be dangerous because it relies on real-life interactions.

Model-Based vs. Model-Free Reinforcement Learning: Example

Understanding model-based and model-free RL can be challenging because they often seem too complex and abstract. We’ll try to make the concepts easier to understand through a real-life example.

Let’s say you have two soccer teams that have never played each other before. Therefore, neither of the teams knows what to expect. At the beginning of the match, Team A tries different strategies to see whether they can score a goal. When they find a strategy that works, they’ll keep using it to score more goals. This is model-free reinforcement learning.

On the other hand, Team B came prepared. They spent hours investigating strategies and examining the opponent. The players came up with tactics based on their interpretation of how Team A will play. This is model-based reinforcement learning.

Who will be more successful? There’s no way to tell. Team B may be more successful in the beginning because they have previous knowledge. But Team A can catch up quickly, especially if they use the right tactics from the start.

Reinforcement Learning Algorithms

A reinforcement learning algorithm specifies how an agent learns suitable actions from the rewards. RL algorithms are divided into two categories: value-based and policy gradient-based.

Value-Based Algorithms

Value-based algorithms learn the value at each state of the environment, where the value of a state is given by the expected rewards to complete the task while starting from that state.

Q-Learning

This model-free, off-policy RL algorithm focuses on providing guidelines to the agent on what actions to take and under what circumstances to win the reward. The algorithm uses Q-tables in which it calculates the potential rewards for different state-action pairs in the environment. The table contains Q-values that get updated after each action during the agent’s training. During execution, the agent goes back to this table to see which actions have the best value.

Deep Q-Networks (DQN)

Deep Q-networks, or deep q-learning, operate similarly to q-learning. The main difference is that the algorithm in this case is based on neural networks.

SARSA

The acronym stands for state-action-reward-state-action. SARSA is an on-policy RL algorithm that uses the current action from the current policy to learn the value.

Policy-Based Algorithms

These algorithms directly update the policy to maximize the reward. There are different policy gradient-based algorithms: REINFORCE, proximal policy optimization, trust region policy optimization, actor-critic algorithms, advantage actor-critic, deep deterministic policy gradient (DDPG), and twin-delayed DDPG.

Examples of Reinforcement Learning Applications

The advantages of reinforcement learning have been recognized in many spheres. Here are several concrete applications of RL.

Robotics and Automation

With RL, robotic arms can be trained to perform human-like tasks. Robotic arms can give you a hand in warehouse management, packaging, quality testing, defect inspection, and many other aspects.

Another notable role of RL lies in automation, and self-driving cars are an excellent example. They’re introduced to different situations through which they learn how to behave in specific circumstances and offer better performance.

Gaming and Entertainment

Gaming and entertainment industries certainly benefit from RL in many ways. From AlphaGo (the first program that has beaten a human in the board game Go) to video games AI, RL offers limitless possibilities.

Finance and Trading

RL can optimize and improve trading strategies, help with portfolio management, minimize risks that come with running a business, and maximize profit.

Healthcare and Medicine

RL can help healthcare workers customize the best treatment plan for their patients, focusing on personalization. It can also play a major role in drug discovery and testing, allowing the entire sector to get one step closer to curing patients quickly and efficiently.

Basics for Implementing Reinforcement Learning

The success of reinforcement learning in a specific area depends on many factors.

First, you need to analyze a specific situation and see which RL algorithm suits it. Your job doesn’t end there; now you need to define the environment and the agent and figure out the right reward system. Without them, RL doesn’t exist. Next, allow the agent to put its detective cap on and explore new features, but ensure it uses the existing knowledge adequately (strike the right balance between exploration and exploitation). Since RL changes rapidly, you want to keep your model updated. Examine it every now and then to see what you can tweak to keep your model in top shape.

Explore the World of Possibilities With Reinforcement Learning

Reinforcement learning goes hand-in-hand with the development and modernization of many industries. We’ve been witnesses to the incredible things RL can achieve when used correctly, and the future looks even better. Hop in on the RL train and immerse yourself in this fascinating world.

Algorithms are the backbone behind technology that have helped establish some of the world’s most famous companies. Software giants like Google, beverage giants Coca Cola and many other organizations utilize proprietary algorithms to improve their services and enhance customer experience. Algorithms are an inseparable part of the technology behind organization as they help improve security, product or service recommendations, and increase sales.

Knowing the benefits of algorithms is useful, but you might also be interested to know what makes them so advantageous. As such, you’re probably asking: “What is an algorithm?” Here’s the most common algorithm definition: an algorithm is a set of procedures and rules a computer follows to solve a problem.

In addition to the meaning of the word “algorithm,” this article will also cover the key types and characteristics of algorithms, as well as their applications.

Types of Algorithms and Design Techniques

One of the main reasons people rely on algorithms is that they offer a principled and structured means to represent a problem on a computer.

Recursive Algorithms

Recursive algorithms are critical for solving many problems. The core idea behind recursive algorithms is to use functions that call themselves on smaller chunks of the problem.

Divide and Conquer Algorithms

Divide and conquer algorithms are similar to recursive algorithms. They divide a large problem into smaller units. Algorithms solve each smaller component before combining them to tackle the original, large problem.

Greedy Algorithms

A greedy algorithm looks for solutions based on benefits. More specifically, it resolves problems in sections by determining how many benefits it can extract by analyzing a certain section. The more benefits it has, the more likely it is to solve a problem, hence the term greedy.

Dynamic Programming Algorithms

Dynamic programming algorithms follow a similar approach to recursive and divide and conquer algorithms. First, they break down a complex problem into smaller pieces. Next, it solves each smaller piece once and saves the solution for later use instead of computing it.

Backtracking Algorithms

After dividing a problem, an algorithm may have trouble moving forward to find a solution. If that’s the case, a backtracking algorithm can return to parts of the problem it has already solved until it determines a way forward that can overcome the setback.

Brute Force Algorithms

Brute force algorithms try every possible solution until they determine the best one. Brute force algorithms are simpler, but the solution they find might not be as good or elegant as those found by the other types of algorithms.

Algorithm Analysis and Optimization

Digital transformation remains one of the biggest challenges for businesses in 2023. Algorithms can facilitate the transition through careful analysis and optimization.

Time Complexity

The time complexity of an algorithm refers to how long you need to execute a certain algorithm. A number of factors determine time complexity, but the algorithm’s input length is the most important consideration.

Space Complexity

Before you can run an algorithm, you need to make sure your device has enough memory. The amount of memory required for executing an algorithm is known as space complexity.

Trade-Offs

Solving a problem with an algorithm in C or any other programming language is about making compromises. In other words, the system often makes trade-offs between the time and space available.

For example, an algorithm can use less space, but this extends the time it takes to solve a problem. Alternatively, it can take up a lot of space to address an issue faster.

Optimization Techniques

Algorithms generally work great out of the box, but they sometimes fail to deliver the desired results. In these cases, you can implement a slew of optimization techniques to make them more effective.

Memorization

You generally use memorization if you wish to elevate the efficacy of a recursive algorithm. The technique rewrites algorithms and stores them in arrays. The main reason memorization is so powerful is that it eliminates the need to calculate results multiple times.

Parallelization

As the name suggests, parallelization is the ability of algorithms to perform operations simultaneously. This accelerates task completion and is normally utilized when you have a lot of memory on your device.

Heuristics

Heuristic algorithms (a.k.a. heuristics) are algorithms used to speed up problem-solving. They generally target non-deterministic polynomial-time (NP) problems.

Approximation Algorithms

Another way to solve a problem if you’re short on time is to incorporate an approximation algorithm. Rather than provide a 100% optimal solution and risk taking longer, you use this algorithm to get approximate solutions. From there, you can calculate how far away they are from the optimal solution.

Pruning

Algorithms sometimes analyze unnecessary data, slowing down your task completion. A great way to expedite the process is to utilize pruning. This compression method removes unwanted information by shrinking algorithm decision trees.

Algorithm Applications and Challenges

Thanks to this introduction to algorithm, you’ll no longer wonder: “What is an algorithm, and what are the different types?” Now it’s time to go through the most significant applications and challenges of algorithms.

Sorting Algorithms

Sorting algorithms arrange elements in a series to help solve complex issues faster. There are different types of sorting, including linear, insertion, and bubble sorting. They’re generally used for exploring databases and virtual search spaces.

Searching Algorithms

An algorithm in C or other programming languages can be used as a searching algorithm. They allow you to identify a small item in a large group of related elements.

Graph Algorithms

Graph algorithms are just as practical, if not more practical, than other types. Graphs consist of nodes and edges, where each edge connects two nodes.

There are numerous real-life applications of graph algorithms. For instance, you might have wondered how engineers solve problems regarding wireless networks or city traffic. The answer lies in using graph algorithms.

The same goes for social media sites, such as Facebook. Algorithms on such platforms contain nodes, which represent key information, like names and genders and edges that represent the relationships or dependencies between them.

Cryptography Algorithms

When creating an account on some websites, the platform can generate a random password for you. It’s usually stronger than custom-made codes, thanks to cryptography algorithms. They can scramble digital text and turn it into an unreadable string. Many organizations use this method to protect their data and prevent unauthorized access.

Machine Learning Algorithms

Over 70% of enterprises prioritize machine learning applications. To implement their ideas, they rely on machine learning algorithms. They’re particularly useful for financial institutions because they can predict future trends.

Famous Algorithm Challenges

Many organizations struggle to adopt algorithms, be it an algorithm in data structure or computer science. The reason being, algorithms present several challenges:

• Opacity – You can’t take a closer look at the inside of an algorithm. Only the end result is visible, which is why it’s difficult to understand an algorithm.
• Heterogeneity – Most algorithms are heterogeneous, behaving differently from one another. This makes them even more complex.
• Dependency – Each algorithm comes with the abovementioned time and space restrictions.

Algorithm Ethics, Fairness, and Social Impact

When discussing critical characteristics of algorithms, it’s important to highlight the main concerns surrounding this technology.

Bias in Algorithms

Algorithms aren’t intrinsically biased unless the developer injects their personal biases into the design. If so, getting impartial results from an algorithm is highly unlikely.

Transparency and Explainability

Knowing only the consequences of algorithms prevents us from explaining them in detail. A transparent algorithm enables a user to view and understand its different operations. In contrast, explainability of an algorithm relates to its ability to provide reasons for the decisions it makes.

Privacy and Security

Some algorithms require end users to share private information. If cyber criminals hack the system, they can easily steal the data.

Algorithm Accessibility and Inclusivity

Limited explainability hinders access to algorithms. Likewise, it’s hard to include different viewpoints and characteristics in an algorithm, especially if it is biased.

Algorithm Trust and Confidence

No algorithm is omnipotent. Claiming otherwise makes it untrustworthy – the best way to prevent this is for the algorithm to state its limitations.

Algorithm Social Impact

Algorithms impact almost every area of life including politics, economic and healthcare decisions, marketing, transportation, social media and Internet, and society and culture in general.

Algorithm Sustainability and Environmental Impact

Contrary to popular belief, algorithms aren’t very sustainable. The extraction of materials to make computers that power algorithms is a major polluter.

Future of Algorithms

Algorithms are already advanced, but what does the future hold for this technology? Here are a few potential applications and types of future algorithms:

• Quantum Algorithms – Quantum algorithms are expected to run on quantum computers to achieve unprecedented speeds and efficiency.
• Artificial Intelligence and Machine Learning – AI and machine learning algorithms can help a computer develop human-like cognitive qualities via learning from its environment and experiences.
• Algorithmic Fairness and Ethics – Considering the aforementioned challenges of algorithms, developers are expected to improve the technology. It may become more ethical with fewer privacy violations and accessibility issues.

Smart, Ethical Implementation Is the Difference-Maker

Understanding algorithms is crucial if you want to implement them correctly and ethically. They’re powerful, but can also have unpleasant consequences if you’re not careful during the development stage. Responsible use is paramount because it can improve many areas, including healthcare, economics, social media, and communication.

If you wish to learn more about algorithms, accredited courses might be your best option. AI and machine learning-based modules cover some of the most widely-used algorithms to help expand your knowledge about this topic.

Software engineering tackles designing, testing, and maintaining software (programs). This branch involves many technologies and tools that assist in the process of creating programs for many different niches.

Here, we’ll provide an answer to the “What is software engineering?” question. We’ll also explain the key concepts related to it, the skills required to become a software engineer, and introduce you to career opportunities.

Basics of Software Engineering

History and Evolution of Software Engineering

Before digging into the nitty-gritty behind software engineering, let’s have a (very short) history lesson.

We can say that software engineering is relatively young compared to many other industries: it was “born” in 1963. Margaret Hamilton, an American computer scientist, was working on the software for the Apollo spacecraft. It was she who coined the term “software engineer” to describe her work at the time.

Two NATO software engineering conferences took place a few years later, confirming the industry’s significance and allowing it to find its place under the computer-science sun.

During the 1980s, software engineering was widely recognized in many countries and by various experts. Since then, the field has advanced immensely thanks to technological developments. It’s used in many spheres and offers a wide array of benefits.

Different Types of Software

What software does software engineering really tackle? You won’t be wrong if you say all software. But learning about the actual types can’t hurt:

• System software – This software powers a computer system. It gives life to computer hardware and represents the “breeding ground” for applications. The most basic example of system software is an operating system like Windows or Linux.
• Application software – This is what you use to listen to music, create a document, edit a photo, watch a movie, or perform any other action on your computer.
• Embedded software – This is specialized software found in an embedded device that controls its specific functions.

Software Development Life Cycle (SDLC)

What does the life of software look like? Let’s analyze the key stages.

Planning and Analysis

During this stage, experts analyze the market, clients’ needs, customers’ input, and other factors. Then, they compile this information to plan the software’s development and measure its feasibility. This is also the time when experts identify potential risks and brainstorm solutions.

Design

Now it’s time to create a design plan, i.e., design specification. This plan will go to stakeholders, who will review it and offer feedback. Although it may seem trivial, this stage is crucial to ensure everyone’s on the same page. If that’s not the case, the whole project could collapse in the blink of an eye.

Implementation

After everyone gives the green light, software engineers start developing the software. This stage is called “implementation” and it’s the longest part of the life cycle. Engineers can make the process more efficient by dividing it into smaller, more “digestible” chunks.

Testing

Before the software reaches its customers, you need to ensure it’s working properly, hence the testing stage. Here, testers check the software for errors, bugs, and issues. This can also be a great learning stage for inexperienced testers, who can observe the process and pick up on the most common issues.

Deployment

The deployment stage involves launching the software on the market. Before doing that, engineers will once again check with stakeholders to see if everything’s good to go. They may make some last-minute changes depending on the provided feedback.

Maintenance

Just because software is on the market doesn’t mean it can be neglected. Every software requires some degree of care. If not maintained regularly, the software can malfunction and cause various issues. Besides maintenance, engineers ensure the software is updated. Since the market is evolving rapidly, it’s necessary to introduce new features to the software to ensure it fulfills the customers’ needs.

Key Concepts in Software Engineering

Those new to the software engineering world often feel overwhelmed by the number of concepts thrown at them. But this can also happen to seasoned engineers who are switching jobs and/or industries. Whatever your situation, here are the basic concepts you should acquire.

Requirements Engineering

Requirements engineering is the basis for developing software. It deals with listening and understanding the customers’ needs, putting them on paper, and defining them. These needs are turned into clearly organized requirements for efficient software development.

Software Design Principles

Modularity

Software engineers break down the software into sections (modules) to make the process easier, quicker, more detailed, and independent.

Abstraction

Most software users don’t want to see the boring details about the software they’re using. Being the computer wizards they are, software engineers wave their magic wand to hide the more “abstract” information about the software and highlight other aspects customers consider more relevant.

Encapsulation

Encapsulation refers to grouping certain data together into a single unit. It also represents the process when software engineers put specific parts of the software in a secure bubble so that they’re protected from external changes.

Coupling and Cohesion

These two concepts define a software’s functionality, maintainability, and reliability. They denote how much software modules depend on each other and how elements within one module work together.

Software Development Methodologies

Waterfall

The basic principle of the waterfall methodology is to have the entire software development process run smoothly using a sequential approach. Each stage of the life cycle we discussed above needs to be fully completed before the next one begins.

Agile Methodologies

With agile methodologies, the focus is on speed, collaboration, efficiency, and high customer satisfaction. Team members work together and aim for continual improvement by applying different agile strategies.

DevOps

DevOps (development + operations) asks the question, “What can be done to improve an organization’s capability to develop software faster?” It’s basically a set of tools and practices that automate different aspects of the software development process and make the work easier.

Quality Assurance and Testing

Software engineers don’t just put the software in use as soon as they wrap up the design stage. Before the software gets the green light, its quality needs to be tested. This process involves testing every aspect of the software to ensure it’s good to go.

Software Maintenance and Evolution

Humans are capable of adapting their behavior depending on the situation. Let’s suppose it’s really cold outside, even though it’s summer. Chances are, you won’t go out in a T-shirt and a pair of shorts. And if you catch a cold due to cold weather, you’ll take precautions (drink tea, visit a doctor, or take medicine).

While humans can interpret new situations and “update” their behavior, the software doesn’t work that way. They can’t fix themselves or change how they function. That’s why they need leaders, a.k.a. software engineers, who can keep them in tip-top shape and ensure they’re on top of the new trends.

Essential Skills for Software Engineers

What do you need to be a software engineer?

Programming Languages

If you can’t “speak” a programming language, you can’t develop software. Here are a few of the most popular languages:

• Java – It runs on various platforms and uses C and C++.
• Python – A general-purpose programming language that is a classic among software engineers.
• C++ – An object-oriented language that almost all computers contain, so you can understand its importance.
• JavaScript – A programming language that can handle complex tasks and is one of the web’s three key technologies.

Problem-Solving and Critical Skills

A software engineer needs to be able to look at the bigger picture, identify a problem, and see what it can be done to resolve it.

Communication and Collaboration

Developing software isn’t a one-man job. You need to communicate and collaborate with other team members if you want the best results.

Time Management and Organization

Software engineers often race against the clock to complete tasks. They need to have excellent organizational and time management skills to prevent being late.

Continuous Learning and Adaptability

Technology evolves rapidly, and you need to do that as well if you want to stay current.

Career Opportunities in Software Engineering

Job Roles and Titles

• Software Developer – If you love to get all technical and offer the world practical solutions for their problems, this is the perfect job role.
• Software Tester – Do you like checking other people’s work? Software testing may be the way to go.
• Software Architect – The position involves planning, analyzing, and organizing, so if you find that interesting, check it out.
• Project Manager – If you see yourself supervising every part of the process and ensuring it’s completed with flying colors, this is the ideal position.

Industries and Sectors

• Technology – Many software engineers find their dream jobs in the technology industry. Whether developing software for their employer’s needs or working with a major client, software engineers leave a permanent mark on this industry.
• Finance – From developing credit card software to building major financial education software, working as a software engineer in this industry can be rewarding (and very lucrative).
• Healthcare – Software engineers may not be doctors, but they can save lives. They can create patient portals, cloud systems, or consumer health apps and improve the entire healthcare industry with their work.
• Entertainment – The entertainment industry would collapse without software engineers who develop content streaming apps, video games, animations, and much more.

Education and Certifications

• Bachelor’s degree in computer science or related field – Many on-campus and online universities and institutes offer bachelor’s degree programs that could set you up for success in the industry.
• Professional certifications – These certifications can be a great starting point or a way to strengthen the skills you already have.
• Online courses and boot camps – Various popular platforms (think Coursera and Udemy) offer excellent software engineering courses.

Hop on the Software Engineering Train

There’s something special and rewarding about knowing you’ve left your mark in this world. As a software engineer, you can improve the lives of millions of people and create simple solutions to seemingly complicated problems.

If you want to make your work even more meaningful and reap the many benefits this industry offers, you need to improve your skills constantly and follow the latest trends.

According to Statista, the U.S. cloud computing industry generated about $206 billion in revenue in 2022. Expand that globally, and the industry has a value of $483.98 billion. Growth is on the horizon, too, with Grand View Research stating that the various types of cloud computing will achieve a compound annual growth rate (CAGR) of 14.1% between 2023 and 2030.

The simple message is that cloud computing applications are big business.

But that won’t mean much to you if you don’t understand the basics of cloud computing infrastructure and how it all works. This article digs into the cloud computing basics so you can better understand what it means to deliver services via the cloud.

The Cloud Computing Definition

Let’s answer the key question immediately – what is cloud computing?

Microsoft defines cloud computing as the delivery of any form of computing services, such as storage or software, over the internet. Taking software as an example, cloud computing allows you to use a company’s software online rather than having to buy it as a standalone package that you install locally on your computer.

For the super dry definition, cloud computing is a model of computing that provides shared computer processing resources and data to computers and other devices on demand over the internet.

Cloud Computing Meaning

Though the cloud computing basics are pretty easy to grasp – you get services over the internet – what it means in a practical context is less clear.

In the past, businesses and individuals needed to buy and install software locally on their computers or servers. This is the typical ownership model. You hand over your money for a physical product, which you can use as you see fit.

You don’t purchase a physical product when using software via the cloud. You also don’t install that product, whatever it may be, physically on your computer. Instead, you receive the services managed directly by the provider, be they storage, software, analytics, or networking, over the internet. You (and your team) usually install a client that connects to the vendor’s servers, which contain all the necessary computational, processing, and storage power.

What Is Cloud Computing With Examples?

Perhaps a better way to understand the concept is with some cloud computing examples. These should give you an idea of what cloud computing looks like in practice:

• Google Drive – By integrating the Google Docs suite and its collaborative tools, Google Drive lets you create, save, edit, and share files remotely via the internet.
• Dropbox – The biggest name in cloud storage offers a pay-as-you-use service that enables you to increase your available storage space (or decrease it) depending on your needs.
• Amazon Web Services (AWS) – Built specifically for coders and programmers, AWS offers access to off-site remote servers.
• Microsoft Azure – Microsoft markets Azure as the only “consistent hybrid cloud.” This means Azure allows a company to digitize and modernize their existing infrastructure and make it available over the cloud.
• IBM Cloud – This service incorporates over 170 services, ranging from simple databases to the cloud servers needed to run AI programs.
• Salesforce – As the biggest name in the customer relationship management space, Salesforce is one of the biggest cloud computing companies. At the most basic level, it lets you maintain databases filled with details about your customers.

Common Cloud Computing Applications

Knowing what cloud computing is won’t help you much if you don’t understand its use cases. Here are a few ways you could use the cloud to enhance your work or personal life:

• Host websites without needing to keep on-site servers.
• Store files and data remotely, as you would with Dropbox or Salesforce. Most of these providers also provide backup services for disaster recovery.
• Recover lost data with off-site storage facilities that update themselves in real-time.
• Manage a product’s entire development cycle across one workflow, leading to easier bug tracking and fixing alongside quality assurance testing.
• Collaborate easily using platforms like Google Drive and Dropbox, which allow workers to combine forces on projects as long as they maintain an internet connection.
• Stream media, especially high-definition video, with cloud setups that provide the resources that an individual may not have built into a single device.

The Basics of Cloud Computing

With the general introduction to cloud computing and its applications out of the way, let’s get down to the technical side. The basics of cloud computing are split into five categories:

• Infrastructure
• Services
• Benefits
• Types
• Challenges

Cloud Infrastructure

The interesting thing about cloud infrastructure is that it simulates a physical build. You’re still using the same hardware and applications. Servers are in play, as is networking. But you don’t have the physical hardware at your location because it’s all off-site and stored, maintained, and updated by the cloud provider. You get access to the hardware, and the services it provides, via your internet connection.

So, you have no physical hardware to worry about besides the device you’ll use to access the cloud service.

Off-site servers handle storage, database management, and more. You’ll also have middleware in play, facilitating communication between your device and the cloud provider’s servers. That middleware checks your internet connection and access rights. Think of it like a bridge that connects seemingly disparate pieces of software so they can function seamlessly on a system.

Services

Cloud services are split into three categories:

Infrastructure as a Service (IaaS)

In a traditional IT setup, you have computers, servers, data centers, and networking hardware all combined to keep the front-end systems (i.e., your computers) running. Buying and maintaining that hardware is a huge cost burden for a business.

IaaS offers access to IT infrastructure, with scalability being a critical component, without forcing an IT department to invest in costly hardware. Instead, you can access it all via an internet connection, allowing you to virtualize traditionally physical setups.

Platform as a Service (PaaS)

Imagine having access to an entire IT infrastructure without worrying about all the little tasks that come with it, such as maintenance and software patching. After all, those small tasks build up, which is why the average small business spends an average of 6.9% of its revenue on dealing with IT systems each year.

PaaS reduces those costs significantly by giving you access to cloud services that manage maintenance and patching via the internet. On the simplest level, this may involve automating software updates so you don’t have to manually check when software is out of date.

Software as a Service (SaaS)

If you have a rudimentary understanding of cloud computing, the SaaS model is the one you are likely to understand the most. A cloud provider builds software and makes it available over the internet, with the user paying for access to that software in the form of a subscription. As long as you keep paying your monthly dues, you get access to the software and any updates or patches the service provider implements.

It’s with SaaS that we see the most obvious evolution of the traditional IT model. In the past, you’d pay a one-time fee to buy a piece of software off the shelf, which you then install and maintain yourself. SaaS gives you constant access to the software, its updates, and any new versions as long as you keep paying your subscription. Compare the standalone versions of Microsoft Office with Microsoft Office 365, especially in their range of options, tools, and overall costs.

Benefits of Cloud Computing

The traditional model of buying a thing and owning it worked for years. So, you may wonder why cloud computing services have overtaken traditional models, particularly on the software side of things. The reason is that cloud computing offers several advantages over the old ways of doing things:

• Cost savings – Cloud models allow companies to spread their spending over the course of a year. It’s the difference between spending $100 on a piece of software versus spending $10 per month to access it. Sure, the one-off fee ends up being less, but paying $10 per month doesn’t sting your bank balance as much.
• Scalability – Linking directly to cost savings, you don’t need to buy every element of a software to access the features you need when using cloud services. You pay for what you use and increase the money you spend as your business scales and you need deeper access.
• Mobility – Cloud computing allows you to access documents and services anywhere. Where before, you were tied to your computer desk if you wanted to check or edit a document, you can now access that document on almost any device.
• Flexibility – Tied closely to mobility, the flexibility that comes from cloud computing is great for users. Employees can head out into the field, access the services they need to serve customers, and send information back to in-house workers or a customer relationship management (CRM) system.
• Reliability – Owning physical hardware means having to deal with the many problems that can affect that hardware. Malfunctions, viruses, and human error can all compromise a network. Cloud service providers offer reliability based on in-depth expertise and more resources dedicated to their hardware setups.
• Security – The done-for-you aspect of cloud computing, particularly concerning maintenance and updates, means one less thing for a business to worry about. It also absorbs some of the costs of hardware and IT maintenance personnel.

Types of Cloud Computing

The types of cloud computing are as follows:

• Public Cloud – The cloud provider manages all hardware and software related to the service it provides to users.
• Private Cloud – An organization develops its suite of services, all managed via the cloud but only accessible to group members.
• Hybrid Cloud – Combines a public cloud with on-premises infrastructure, allowing applications to move between each.
• Community Cloud – While the community cloud has many similarities to a public cloud, it’s restricted to only servicing a limited number of users. For example, a banking service may only get offered to the banking community.

Challenges of Cloud Computing

Many a detractor of cloud computing notes that it isn’t as issue-proof as it may seem. The challenges of cloud computing may outweigh its benefits for some:

• Security issues related to cloud computing include data privacy, with cloud providers obtaining access to any sensitive information you store on their servers.
• As more services switch over to the cloud, managing the costs related to every subscription you have can feel like trying to navigate a spider’s web of software.
• Just because you’re using a cloud-based service, that doesn’t mean said service handles compliance for you.
• If you don’t perfectly follow a vendor’s terms of service, they can restrict your access to their cloud services remotely. You don’t own anything.
• You can’t do anything if a service provider’s servers go down. You have to wait for them to fix the issue, leaving you stuck without access to the software for which you’re paying.
• You can’t call a third party to resolve an issue your systems encounter with the cloud service because the provider is the only one responsible for their product.
• Changing cloud providers and migrating data can be challenging, so even if one provider doesn’t work well, companies may hesitate to look for other options due to sunk costs.

Cloud Computing Is the Present and Future

For all of the challenges inherent in the cloud computing model, it’s clear that it isn’t going anywhere. Techjury tells us that about 57% of companies moved, or were in the process of moving, their workloads to cloud services in 2022.

That number will only increase as cloud computing grows and develops.

So, let’s leave you with a short note on cloud computing. It’s the latest step in the constant evolution of how tech companies offer their services to users. Questions of ownership aside, it’s a model that students, entrepreneurs, and everyday people must understand.

The artificial intelligence market was estimated to be worth $136 billion in 2022, with projections of up to $1,800 billion by the end of the decade. More than a third of companies today implement AI in their business processes, and over 40% will consider doing so in the future.

These whopping numbers testify to the importance, prevalence, and reality of AI in the modern world. If you’re considering an education in AI, you’re looking at a highly rewarding and prosperous future career. But what are the applications of artificial intelligence, and how did it all begin? Let’s start from scratch.

What Is Artificial Intelligence?

Artificial intelligence definition describes AI as a part of computer science that focuses on building programs and software with human intelligence. There are four types of artificial intelligence: the theory of mind, reactive, self-aware, and limited memory.

Reactive AI masters one field, like playing chess, performing a single manufacturing task, and similar. Limited memory machines can gather and remember information and use findings to offer recommendations (hotels, restaurants, etc.).

Theory of mind is a more developed type of AI capable of understanding human emotions. These machines can also take part in social interactions. Finally, self-aware AI is a conscious machine, but its development is reserved for the future.

History of Artificial Intelligence

The concept of artificial intelligence has roots in the 1950s. This was when AI became an academic discipline, and scientists started publishing papers about it. It all started with Alan Turing and his paper about computer machinery and intelligence that introduced basic AI concepts.

Here are some important milestones in the artificial intelligence field:

• 1952 – Arthur Samuel created a program that taught itself to play checkers.
• 1955 – John McCarthy’s workshop on AI, where the term was used for the first time.
• 1961 – First robot worker on a General Motors factory’s assembly line.
• 1980 – First conference on AI.
• 1986 – Demonstration of the first driverless car.
• 1997 – A program beat Gary Kasparov in a legendary chess match, thus becoming the first AI tool to win in a competition over a human.
• 2000 – Development of a robot that simulates a person’s body movement and human emotions.

AI in the 21st Century

The 21st century has witnessed some of the fastest advancements and applications of artificial intelligence across industries. Robots are becoming more sophisticated, they land on other planets, work in shops, clean, and much more. Global corporations like Facebook, Twitter, Netflix, and others regularly use AI tools in marketing to boost user experience, etc.

We’re also seeing the rise of AI chatbots like ChatGPT that can create content indistinguishable from human content.

Fields Used in Artificial Intelligence

Artificial intelligence relies on the use of numerous technologies:

• Machine Learning – Making apps and processes that can perform tasks like humans.
• Natural Language Processing – Training computers to understand words like humans.
• Computer Vision – Developing tools and programs that can read visual data and take information from it.
• Robotics – Programming agents to perform tasks in the physical world.

Applications of Artificial Intelligence

Below is an overview of applications of artificial intelligence across industries.

Automation

Any business and sector that relies on automation can use AI tools for faster data processing. By implementing advanced artificial intelligence tools into daily processes, you can save time and resources.

Healthcare

Fraud is common in healthcare. AI in this field is mostly oriented toward lowering the risk of fraud and administrative fees. For example, using AI makes it possible to check insurance claims and find inconsistencies.

Similarly, AI can help advance and finetune medical research, telemedicine, medical training, patient engagement, and support. There’s virtually no aspect of healthcare and medicine that couldn’t benefit from AI.

Business

Businesses across industries benefit from AI to finetune various aspects like the hiring process, threat detection, analytics, task automation, and more. Business owners and managers can make better-informed business decisions with less risk of error.

Education

Modern-day education offers personalized programs tailored to the individual learner’s abilities and goals. By automating tasks with AI tools, teachers can spend more time helping students progress faster in their studies.

Security

Security has never been more important following the rise of web applications, online shopping, and data sharing. With so much sensitive information shared daily, AI can help increase data protection and mitigate hacking attacks and threats. Systems with AI features can diagnose, scan, and detect threats.

Benefits and Challenges of Artificial Intelligence

There are enormous benefits of AI applications that can revolutionize any industry. Here are just some of them:

Automation and Increased Efficiency

AI helps streamline repetitive tasks, automate processes, and boost work efficiency. This characteristic of AI is already visible in all industries, and the use of programming languages like R and Python makes it all possible.

Improved Decision Making

Stakeholders can use AI to analyze immense amounts of data (with millions or billions of pieces of information) and make better-informed business decisions. Compare this to limited data analysis of the past, where researchers only had access to local documents or libraries, and you can understand how AI empowers present-day business owners.

Cost Savings

By automating tasks and streamlining processes, businesses also spend less money. Savings in terms of energy, extra work hour costs, materials, and even HR are significant. When you use AI right, you can turn almost any project into reality with minimal cost.

Challenges of AI

Despite the numerous benefits, AI also comes with a few challenges:

Data Privacy and Security

All AI developments take place online. The web still lacks proper laws on data protection and privacy, and it’s highly possible that user data is being used without consent in AI projects worldwide. Until strict laws are enacted, AI will continue to pose a threat to data privacy.

Algorithmic Bias

Algorithms today assist humans in decision-making. Stakeholders and regular users rely on data provided by AI tools to complete or approach tasks and even form new beliefs and behaviors. Poorly trained machines can encourage human biases, which can be especially harmful.

Job Less

AI is developing at the speed of light. Many tools are already replacing human labor in both the physical and digital worlds. A question remains to what degree machines will overtake the labor market in the future.

Artificial Intelligence Examples

Let’s look at real-world examples of artificial intelligence across applications and industries.

Virtual Assistants

Apple was the first company to introduce a virtual assistant based on AI. We know the tool today by the name of Siri. Numerous other companies like Amazon and Google have followed suit, so now we have Alexa, Google Assistant, and many other AI talking assistants.

Recommendation Systems

Users today find it ever more challenging to resist addictive content online. We’re often glued to our phones because our Instagram feed keeps suggesting must-watch Reels. The same goes for Netflix and its binge-worthy shows. These platforms use AI to enhance their recommendation system and offer ads, TV shows, or videos you love.

Shopping on Amazon works in a similar fashion. Even Spotify uses AI to offer audio recommendations to customers. It relies on your previous search history, liked content, and similar data to provide new suggestions.

Autonomous Vehicles

New-age vehicles powered by AI have sophisticated systems that make commuting easier than ever. Tesla’s latest AI software can collect information in real-time from the multiple cameras on the vehicles. The AI makes a 3D map with roads, obstacles, traffic lights, and other elements to make your ride safer.

Waymo has a similar system of lidar sensors around the vehicles that send pulsations around the car and offer an overview of the car’s surroundings.

Fraud Detection

Banks and credit card companies implement AI algorithms to prevent fraud. Advanced software helps these companies understand their customers and prevent non-authorized users from making payments or completing other unauthorized actions.

Image and Voice Recognition

If you have a newer smartphone, you’re already familiar with Face ID and voice assistant tools. These are built on basic AI principles and are being integrated into broader systems like vehicles, vending machines, home appliances, and more.

Deep Learning

Artificial intelligence encompasses both deep learning and machine learning. Machine learning encompasses deep learning and uses algorithms that learn from data, explore patterns, and predict outputs.

Deep learning relies on sophisticated neural networks similar to the networks in the human brain. Deep learning specialists use these neural networks to pinpoint patterns in large data sets.

Artificial Intelligence Continues to Grow and Develop

Although predicting the future is impossible, numerous AI specialists expect to see further development in this computer science discipline. More businesses will start implementing AI and we’ll see more autonomous vehicles and smarter robotics. That said, it’s increasingly important to take into account ethical considerations. As long as we use AI ethically, there’s no danger to our social interactions and privacy.

Technology transforms the world in so many ways. Ford’s introduction of the assembly line was essential to the vehicle manufacturing process. The introduction of the internet changed how we communicate, do business, and interact with the world. And in machine learning, we have an emerging technology that transforms how we use computers to complete complex tasks.

Think of machine learning models as “brains” that machines use to actively learn. No longer constrained by rules laid out in their programming, machines have the ability to develop an understanding of new concepts and deliver analysis in ways they never could before. And as a prospective machine learning student, you can become the person who creates the “brains” that modern machines use now and in the future.

But you need a good starting point before you can do any of that. This article covers three of the best machine learning tutorials for beginners who want to get their feet wet while building foundational knowledge that serves them in more specialized courses.

Factors to Consider When Choosing a Machine Learning Tutorial

A machine learning beginner can’t expect to jump straight into a course that delves into neural networking and deep learning and have any idea what they’re doing. They need to learn to crawl before they can walk, making the following factors crucial to consider when choosing a machine learning tutorial for beginners.

• Content quality. You wouldn’t use cheap plastic parts to build an airplane, just like you can’t rely on poor-quality course content to get you started with machine learning. Always look for reviews of a tutorial before engaging, in addition to checking the credentials of the provider to ensure they deliver relevant content that aligns with your career goals.
• Instructor expertise. Sticking with our airplane analogy, imagine being taught how to pilot a plane by somebody who’s never actually flown. It simply wouldn’t work. The same goes for a machine learning tutorial, as you need to see evidence that your instructor does more than parrot information that you can find elsewhere. Look for real-world experience and accreditation from recognized authorities.
• Course structure and pacing. As nice as it would be to have an infinite amount of free time to dedicate to learning, that isn’t a reality for anybody. You have work, life, family, and possibly other study commitments to keep on top of, and your machine learning tutorial has to fit around all of it.
• Practical and real-world examples. Theoretical knowledge can only take you so far. You need to know how to apply what you’ve learned, which is why a good tutorial should have practical elements that test your knowledge. Think of it like driving a car. You can read pages upon pages of material on how to drive properly but you won’t be able to get on the road until you’ve spent time learning behind the wheel.
• Community support. Machine learning is a complex subject and it’s natural to feel a little lost with the materials in many tutorials. A strong community gives you a resource base to lean into, in addition to exposing you to peers (and experienced tech-heads) who can help you along or point you in the right career direction.

Top Three Machine Learning Tutorials for Beginners

Now you know what to look for in a machine learning tutorial for beginners, you’re ready to start searching for a course. But if you want to take a shortcut and jump straight into learning, these three courses are superb starting points.

Tutorial 1 – Intro to Machine Learning (Kaggle)

Offered at no cost, Intro to Machine Learning is a three-hour self-paced course that allows you to learn as and when you feel like learning. All of which is helped by Kaggle’s clever save system. You can use it to save your progress and jump back into your learning whenever you’re ready. The course has seven lessons, the first of which offers an introduction to machine learning as a concept. Whereas the other six dig into more complex topics and come with an exercise for you to complete.

Those little exercises are the tutorial’s biggest plus point. They force you to apply what you’ve learned before you can move on to the next lesson. The course also has a dedicated community (led by tutorial creator Dan Becker) that can help you if you get stuck. You even get a certificate for completing the tutorial, though this certificate isn’t as prestigious as one that comes from an organization like Google or IBM.

On the downside, the course isn’t a complete beginner’s course. You’ll need a solid understanding of Python before you get started. Those new to coding should look for Python courses first or they’ll feel lost when the tutorial starts throwing out terminology and programming libraries that they need to use.

Ideal for students with experience in Python who want to apply the programming language to machine learning models.

Tutorial 2 – What Is Machine Learning? (Udemy)

You can’t build a house without any bricks and you can’t build a machine learning model before you understand the different types of learning that underpin that model. Those different types of learning are what the What is Machine Learning tutorial covers. You’ll get to grips with supervised, unsupervised, and reinforcement learning, which are the three core learning types a machine can use to feed its “brain.”

The course introduces you to real-world problems and helps you to see which type of machine learning is best suited to solving those problems. It’s delivered via online videos, totaling just under two hours of teaching, and includes demonstrations in Python to show you how each type of learning is applied to real-world models. All the resources used for the tutorial are available on a GitHub page (which also gives you access to a strong online community) and the tutorial is delivered by an instructor with over 27 years of experience in the field.

It’s not the perfect course, by any means, as it focuses primarily on learning types without digging much deeper. Those looking for a more in-depth understanding of the algorithms used in machine learning won’t find it here, though they will build foundational knowledge that helps them to better understand those algorithms once they encounter them. As an Udemy course, it’s free to take but requires a subscription to the service if you want a certificate and the ability to communicate directly with the course provider.

Ideal for students who want to learn about the different types of machine learning and how to use Python to apply them.

Tutorial 3 – Machine Learning Tutorial (Geeksforgeeks)

As the most in-depth machine learning tutorial for beginners, the Geeksforgeeks offering covers almost all of the theory you could ever hope to learn. It runs the gamut from a basic introduction to machine learning through to advanced concepts, such as natural language processing and neural networks. And it’s all presented via a single web page that acts like a hub that links you to many other pages, allowing you to tailor your learning experience based on what aligns best with your goals.

The sheer volume of content on offer is the tutorial’s biggest advantage, with dedicated learners able to take themselves from complete machine learning newbies to accomplished experts if they complete everything. There’s also a handy discussion board that puts you in touch with others taking the course. Plus, the “Practice” section of the tutorial includes real-world problems, including a “Problem of the Day” that you can use to test different skills.

However, some students may find the way the material is presented to be a little disorganized and it’s easy to lose track of where you are among the sea of materials. The lack of testing (barring the two or three projects in the “Practice” section) may also rankle with those who want to be able to track their progress easily.

Ideal for self-paced learners who want to be able to pick and choose what they learn and when they learn it.

Additional Resources for Learning Machine Learning

Beyond tutorials, there are tons of additional resources you can use to supplement your learning. These resources are essential for continuing your education because machine learning is an evolving concept that changes constantly.

• Books. Machine learning books are great for digging deeper into the theory you learn via a tutorial, though they come with the downside of offering no practical examples or ways to interact with authors.
• YouTube channels. YouTube videos are ideal for visual learners and they tend to offer a free way to build on what you learn in a tutorial. Examples of great channels to check out include Sentdex and DeepLearningAI, with both channels covering emerging trends in the field alongside lectures and tutorials.
• Blogs and websites. Blogs come with the advantage of the communities that sprout up around them, which you can rely on to build connections and further your knowledge. Of course, there’s the information shared in the blogs, too, though you must check the writer’s credentials before digging too deep into their content.

Master a Machine Learning Tutorial for Beginners Before Moving On

A machine learning tutorial for beginners can give you a solid base in the fundamentals of an extremely complex subject. With that base established, you can build up by taking other courses and tutorials that focus on more specialized aspects of machine learning. Without the base, you’ll find the learning experience much harder. Think of it like building a house – you can’t lay any bricks until you have a foundation in place.

The three tutorials highlighted here give you the base you need (and more besides), but it’s continued study that’s the key to success for machine learning students. Once you’ve completed a tutorial, look for books, blogs, YouTube channels, and other courses that help you keep your knowledge up-to-date and relevant in an ever-evolving subject.