AI in large text over a dark background. it's a blog banner for best way to learn AI post.

The Best Way to Learn AI in 2024: A Guide for Beginners

Have you decided to take the plunge and start a career in Artificial Intelligence (AI)?

Excellent choice.

AI isn’t just the cherry on top of the sundae that is tech— it’s the whole ice cream parlor.

Don’t just take my word for it, let’s let the numbers do the talking.

The market for Generative AI is projected to reach $22.12 billion by 2025. And by 2030, the potential contribution of AI technology could reach $15.7 trillion for the global economy.

Becoming part of the AI force can lead to great success and returns. As an AI engineer, you can earn an annual median salary of $136,620, which is significantly higher than the mean annual salary across all occupations in the US ($58,260).

Learning AI isn’t just a smart move. It’s a way to unlock innovation, and wealth, and stay at the forefront of technology. It opens up opportunities for higher income and a chance to actively shape the future.

This article will explore the best way to learn AI, regardless of your background or expertise. We will cover everything from the foundational knowledge required, to the range of free resources available for learning AI.

Brief Intro to AI

Tech Target defines AI as, “the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.”

In other words, AI involves creating machines that can perform and mimic human behavior.

AI powered systems can’t appreciate a good pun or savor a cup of coffee, but they can definitely beat you at chess, diagnose diseases, drive cars, and even write poetry.

Whether it’s Siri on your iPhone, Alexa at your home, or Tesla on the road, AI is everywhere. It’s silently transforming the way we live, work, and play, (both positively and negatively).

Why Should You Learn AI?

Not to sound dramatic, but AI is the future.

Like, the “bring-me-my-coffee, create an app, do-my-taxes, tell-me-a-joke” kind of future.

From healthcare and finance to entertainment and transportation, AI has its electronic fingerprints all over.

According to the World Economic Forum’s Future of Jobs report, AI and machine learning specialists top the list of fast-growing jobs over the next five years.

As industries continue to adopt AI technologies to streamline their operations and make better decisions, the demand for AI specialists will likely only increase.

In addition, according to new data from LinkedIn, there has been a significant increase in demand for AI skills between July 2021 and July 2023.

Some countries have experienced even higher growth rates compared to the global average of 2.2x. The U.K. witnessed a 2.3x rise, while Germany and France saw jumps of 2.6x and 2.8x, respectively.

This trend spans across various industries and geographies.

Learning AI doesn’t just look good on your resume. It also gives you access to the future. Not to mention, it’s a field of exponential growth and opportunities. 

Now, let’s get into how you can start learning AI today.

Best Resources for Learning AI

So, you’re probably wondering by now–how do I hitch a ride on this AI bandwagon? Well, buckle up because we’re about to get into the choicest resources and strategies to help you learn AI.

We’ll explore popular online platforms, dive into comprehensive books, and even talk about the communities that can help you on your AI journey.

Online platforms

The best online platforms to learn AI include:

1. Coursera

First on our list is Coursera, an ed-tech platform offering a wide range of AI courses from reputable universities and companies. 

For instance, the Introduction to Artificial Intelligence (AI) course by IBM, is an excellent starting point. 

In this course, you will learn:

  • What is AI, its applications, use cases, and how it is transforming our lives
  • Machine Learning, Deep Learning, and Neural Networks: Explained
  • Several issues and ethical concerns surrounding AI
  • Advice from experts about learning and starting a career in AI

If you want to dip your toes further, you can get the IBM AI Engineering Professional Certificate. This course will delve into:

  • Machine learning, deep learning, and neural networks
  • Various ML algorithms: classification, regression, clustering, and dimensional reduction
  • Implementation of supervised and unsupervised machine learning models using SciPy and ScikitLearn
  • Deployment of machine learning algorithms and pipelines on Apache Spark

Finally, the course will guide you in building deep learning models and neural networks using Keras, PyTorch, and TensorFlow.

If language models are more of your passion, AWS offers a course on Generative AI with Large Language Models. Here you’ll learn all about:

  • Foundational knowledge, practical skills, and a functional understanding of generative AI
  • Latest research on Gen AI and how companies create value with cutting-edge technology
  • Instruction from expert AWS AI practitioners actively deploying AI in business use-cases today.

2. edX

edX is another valuable resource for learning AI. This platform is home to Harvard University’s free course, CS50’s Introduction to Artificial Intelligence with Python

This course eases you into AI by teaching you:

  • Graph search algorithms
  • Reinforcement learning
  • Machine learning
  • Artificial intelligence principles
  • How to design intelligent systems
  • How to use AI in Python programs

3. Udacity

Udacity is one of the best platforms to learn AI thanks to the many AI-focused courses it offers. For instance, if you’re aiming to become an AI programmer, you can start with AI Programming with Python Nanodegree. 

This course covers Python, NumPy, Pandas, Matplotlib, and a dose of linear algebra—the essential toolkit for any aspiring AI practitioner.

Books and research papers to Help You Learn AI

Here are the top books to use for learning AI:

1. Artificial Intelligence: A Modern Approach

First on our list, we have Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig.

The book is fondly referred to as the AI “Bible”, as it offers a comprehensive dive into AI’s foundational concepts and is widely adopted in universities worldwide.

You’ll find clear explanations and real-world examples that bridge theory and practice, perfect for those just stepping into the field.

2. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Next, we recommend Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurelien Geron. This gem offers a practical approach to learning AI.

It unveils complex concepts with hands-on examples and intuitive explanations. Be ready to roll up your sleeves and dive into coding.

3. Deep Learning

Lastly, Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville is a must-read. This book takes a deep dive (pun totally intended) into deep learning.

Its blend of depth and clarity makes it an excellent guide for both novices and seasoned practitioners.

Communities and forums

If you want to learn AI faster, join the following communities:

1. Stack Overflow

First up, we have the legendary Stack Overflow. This is your go-to online community for all your technical questions.

Stuck on a piece of code? Baffled by an error message? Stack Overflow is the place to be.

It’s the coder’s best friend and a treasure chest of knowledge and expertise on AI and more.

2. GitHub

Next, there’s GitHub. While it’s primarily a platform for version control and source code management, GitHub is also a vibrant community where developers share and collaborate on projects, including AI.

You can find thousands of open-source projects and libraries related to AI here, which can serve as valuable learning materials and inspiration.

3. Kaggle

Kaggle is an excellent community for AI learners. It offers a wide range of resources such as courses, competitions, and datasets to help you learn AI and related disciplines like Machine Learning.

4. Reddit

Last but not least, there’s Reddit.

Specifically, the subreddits such as r/artificial, r/MachineLearning, r/OpenAI, and r/learnmachine are the places to be.

These online communities are teeming with discussion, advice, news, and learning resources on AI.

The best way to benefit from such communities is to not hesitate to ask questions, share your learning progress, and engage with others in the community. After all, the best way to learn AI is together.

How to Learn Artificial Intelligence Step-by-Step

Step 1: Acquire the Essential Mathematics and Statistics Knowledge

Start with the basics of Mathematics. Knowledge in areas such as Linear Algebra, Calculus, and Probability is fundamental to understanding the concepts of AI. 

Websites like Khan Academy offer free and comprehensive lessons on these topics.

However, the best option is Mathematics for Machine Learning and Data Science Specialization on Coursera.

Statistics is another foundational pillar to scale the heights of AI.

It’s the backbone of understanding algorithms, making predictions, and interpreting data – all critical aspects in the AI arena.

Websites like Khan Academy and Coursera offer in-depth courses on statistics. 

If you’re keen on a light-hearted approach, Statistics Done Wrong is a witty and informative guide that explores common statistical pitfalls. 

Another helpful course is the Statistics for AI, Machine Learning, and Data Science course by Stanford Uni.

Step 2: Learn to Code

Python is highly recommended due to its simplicity and the vast range of AI and Machine Learning libraries it supports. Websites like Codecademy and Coursera offer interactive Python courses.

Step 3: Get Your Hands on AI Frameworks and Libraries

After grasping Python, get familiar with AI libraries and frameworks such as TensorFlow and PyTorch. These tools will be your bread and butter when developing AI models. The official websites of TensorFlow and PyTorch offer tutorials and resources to get started.

Step 4: Study Machine Learning

Before diving into AI, it’s crucial to understand the broader field of Machine Learning. Online platforms like Coursera and Udacity offer comprehensive courses on Machine Learning.

Step 5: Deep Dive into Deep Learning

Deep Learning is a subset of Machine Learning and the engine behind most AI systems today. ]

Resources like the Deep Learning Book by Ian Goodfellow, Yoshua Bengio and Aaron Courville, or Andrew Ng’s Deep Learning Specialization on Coursera are great places to start.

Step 6: Practical Implementation and Projects

Put your knowledge into practice by working on projects and participating in competitions on platforms like Kaggle

This hands-on experience will hone your skills and provide you with practical insight into real-world AI applications.

Additionally, take advantage of APIs to access commercially available models.

APIs allow you to learn from and build upon the work of others, fast-tracking your practical experience.

Some of the commercially available APIs you should consider include the OpenAI API, Cohere API, and Anthropic API.

Engaging with these resources will offer hands-on experience, allowing you to see real-world AI applications in action and to further hone your skills.

Learning AI is not a sprint, but a marathon. Be patient with your progress, stay consistent, and most importantly, enjoy the process!

Creating Your Portfolio

Now that we’ve set the roadmap for your AI learning journey, let’s shift gears a little. One key aspect that’s as crucial as your learning process is showcasing your skills and knowledge to the world. Here’s how to go about it:

Choose the Sub-Niche of AI You Want to Focus on

AI is a broad field with numerous sub-niches, each with its own set of unique skills and applications. Here, we will highlight a few to help you decide where you might want to focus your learning journey.

Natural Language Processing (NLP)

NLP is the intersection of AI and linguistics that allows computers to communicate with humans in a natural, human-like manner. From language translation apps like Google Translate to voice assistants like Siri and Alexa, NLP is everywhere.

Computer Vision

This niche of AI enables computers to ‘see’ and ‘understand’ digital images and videos. Security cameras that identify suspicious activities, facial recognition in your smartphone, or even self-driving cars all utilize computer vision technology.

Reinforcement Learning

Reinforcement Learning involves an agent that learns how to behave in an environment, by performing certain actions and observing the results. AlphaGo, the program that defeated a world champion Go player, is a famous example of reinforcement learning.

Choosing a sub-niche doesn’t mean you’re limiting yourself. 

It’ll help you focus and gain a deep understanding of a particular area. Once you’ve mastered one area, you can always branch out into others.

Pro tip: Use Free Project Management software to manage your projects. 

Practice, Practice, Practice

The key to mastering any new skill, particularly something as complex as AI, is consistent practice. Theoretical knowledge will only take you so far.

To truly grasp the nuances and intricacies of AI, you need to roll up your sleeves and immerse yourself in practical projects.

Start small, with simple projects like building a recommendation system or a text classifier, then gradually work your way up to more complex problems. Participate in online coding challenges and hackathons.

These platforms not only provide a varied range of problems to solve but also expose you to opportunities for networking and learning from experts in the field.

It’s through this process of trial and error that you’ll learn, grow, and become a savvy AI practitioner.

Applying for an internship will also help you gain more experience and expand your portfolio.

Project Ideas for Beginner AI Learners

Building your first portfolio can feel like a daunting task. But fret not, we’ve got you covered with some ideas to kickstart your AI journey. Here are a few ideas:

  1. Sentiment Analysis Tool

Sentiment analysis is a popular area of AI. 

You could build a tool that scans social media posts or customer reviews and determines whether the sentiment behind them is positive, negative, or neutral.

  1. Chatbot

Another great project could be developing a simple chatbot. It could be a customer service bot for a fictitious company or a personal assistant bot that manages your calendar or provides weather updates.

  1. Image Classification Application

Leveraging deep learning for image classification is another noteworthy project. 

You could build an app that identifies different breeds of dogs, and types of flowers, or even diagnose diseases from medical images.

  1. Stock Market Predictor

Use machine learning algorithms to predict stock market trends. 

Although it won’t be perfect (if only!), it’s an engaging way to showcase your predictive modeling skills.

Career Opportunities in AI

The AI industry is booming and offers a wide variety of roles that cater to different interests and skills. Here is a brief overview of some of the job titles you might encounter:

  1. AI Engineer

These tech-savvy individuals are responsible for designing, testing, and implementing AI models. They often have strong software development skills and a deep understanding of AI principles.

  1. Machine Learning Engineer

Machine learning engineers build and deploy machine learning models to solve business problems. They typically have expertise in several programming languages and machine-learning libraries.

  1. Research Scientist

Research scientists are at the forefront of AI, working on innovative and groundbreaking projects. These individuals often hold advanced degrees and have a deep understanding of multiple AI disciplines.

  1. AI Trainer

Breaking into AI doesn’t always mean you need to be the one building the models. 

AI Trainers play a critical role in teaching AI systems how to perform their tasks. This involves feeding the AI with relevant data, tweaking parameters, and assessing its performance. 

It’s similar to being a tutor for an extremely fast, ever-evolving student who’s trying to master, well, everything! 

You’ll need a fundamental understanding of AI, a keen eye for detail, and oodles of patience. But the payoff? You’re helping shape an entity that might just change the world. No biggie.

  1. Robotics Engineer

Robotics Engineers design, build, and program robots. They use their skills to create machines that can perform tasks automatically or be controlled by humans.

  1. NLP Engineer

NLP engineers use their smarts to build and improve algorithms, models, and systems that help computers understand and work with human language. 

They take on cool challenges like sentiment analysis, language translation, and speech recognition. 

Their work helps make virtual assistants, chatbots, recommendation systems, and other cool stuff even better.

How Long Does it Take to Learn AI?

When it comes to learning AI, there’s no magic wand to wave for instant expertise. Even if you’re a quick learner, you’re looking at a journey that could last from months to years.

  1. Grasping the Basics (3-6 months): Start with understanding the foundational concepts of programming, statistics, and linear algebra. Get your hands dirty with Python or R—both popular languages in AI programming.
  2. Diving into Machine Learning (6-9 months): Once you’re comfortable with the basics, it’s time to plunge into the deep end of machine learning. Master the art of using relevant libraries and frameworks, and start solving real-world problems.
  3. Exploring Deep Learning (1-2 years): Deep Learning is Machine Learning on steroids. It’s more complex, sure, but it’s also where a lot of the cool, cutting-edge stuff happens. Think self-driving cars and voice assistants. This stage is about experimenting, learning from mistakes, and continuously refining your models.
  4. Specializing (Ongoing): AI is a huge field. Once you’ve got a good grasp on the basics, you might want to specialize. Whether it’s natural language processing, robotics, or AI ethics, find your passion and specialize.

It takes time, patience, and persistence. The above timeframe is just an example. If you already know how to work with code, the journey to learning AI will be shorter. It also helps if you have a mentor and a solid community of peers.

What is the Future of AI?

As industries across the board continue to integrate AI into their operations, the demand for AI expertise is skyrocketing. Whether it’s healthcare, finance, entertainment, or tech, there’s a high chance an AI expert can find a niche. And let’s not forget about the potentially fat paycheck that comes with this expertise.

But let’s be honest here. As awesome as that sounds, the best part about a career in AI isn’t the money or the job security. It’s about being at the forefront of a technological revolution. It’s about creating systems that think and learn. It’s about shaping the future. And what could be cooler than that?

AI Experts Advice on Learning AI as a Beginner

“Don’t chase after the hottest trends or the biggest splashes, as these areas will have the most competition and also will likely be superseded quickly anyway. Think about what kinds of problems you’re most interested in solving, and what problems are likely to make the most impact if solved. The first involves being aware of what kinds of work you like doing (programming, theorizing, playing with real-world data, etc), and the second involves looking around and being informed about how the rest of the world lives. ”

Jane Wang, Senior Research Scientist, DeepMind

“1. Learn the foundations. “Hands-on” alone, without an understanding of the underlying math, will not let you become the best in this profession. Today, and especially in the next 5-7 years, the tools will become so mature, that only your imagination will count. In AI, you cannot imagine anything meaningful if you don’t know how the machine “thinks.” Take a sculptor, an architect, or a painter. The best of them know everything about the tools and materials they work with. The same is true for AI.2. Go where the data is. AI is nothing without data, just as your talents.”

Andriy Burkov, Director of Data Science, Gartner

What’s Next?

The road may be tough, but remember, every challenge you face, every algorithm you struggle with, and every neural network you train is a stepping stone toward a brighter, smarter future. Most importantly know how you can save time while sufficiently covering all AI topics.

Keep exploring, keep learning, and most importantly, don’t forget to enjoy the ride! Who knows, you might just be the one to create the next big AI breakthrough.

FAQ on Best Way to Learn AI

Leave a Reply

Your email address will not be published. Required fields are marked *