TechMobius

Beginner-friendly resources for Machine Learning

Want to learn Machine Learning? The read on -

A traditional program is a set of instructions that we provide to a machine in order to perform a specific task. On the other hand, Machine Learning is quite different and unique. Machine Learning is a subset of Artificial Intelligence where a machine has the ability to learn and improve itself from experience without being programmed explicitly by anyone. Machine Learning is in a stage of booming and there is a lot of interesting research work going on. Everyone wants to understand it and break into AI for utilizing its power. Unfortunately, it is often perceived as a miraculous black box that takes some input data and gives out magical predictions but it has got so much more to it than that. Students and developers from various traits and fields want to start using Machine Learning in their projects but the important question is,

“Where do I begin with Machine Learning?”

Breaking into this field is not a task of a few days but a lot of resources today have made it easier for beginners to get a head start. Today, you don’t need to be enrolled in a Ph.D. program or you don’t even need to be doing a Computer Science degree for being able to learn about this new technology. Regardless of the background, you have various options to get started with ML. In this article, we will go over some of the best beginner-friendly resources for Machine Learning.

 

Various Paths In Machine Learning

Before getting straight into it, you should know that there are basically various paths that you can take to get into ML and AI. There is a top-down approach where you just learn high-level things in brief and focus on implementations using frameworks that readily available online. The other approach is that you focus on every algorithm and all the maths and statistics behind it. The latter approach is most beneficial for people who want to get into the research domain of AI and ML. Identify the one that suits you better.

According to me, a top-down approach is better for most of the beginners who want to break into AI and ML and start working on their projects. In this approach, you can learn the mathematics behind it as and when you need it. Here you can directly experience the practical working and this keeps you motivated to keep learning. Whereas one might lose their interest if they just keep learning the theory of ML and not use it anywhere. One important point to keep in mind is that in this approach, people tend to skip the perception part and just copy-paste some code from the internet or courses. The code will work but it is necessary to understand the intuition and reasoning behind it. You must know the reason for every line of code that you write.

If you want to start with a top-down approach, then there are many great resources available for free on the internet. The most common question that we get from people is, “I’ve done Python. Now, how do I start with ML?” Our answer to that is a series of steps that I’ll go over in this article. If you have the knowledge of Python language, then that is great because it has a great pool of Machine Learning libraries that makes it one of the best languages for ML. But if you don’t know anything about this language, don’t be demotivated. It is one of the simplest programming languages to learn.

Learning Python

This is one of the best courses out there on Coursera for getting started with Python Programming Language. It covers all the topics like data structures, databases, and networked application program interfaces.

Andrei Neagoie is a great instructor, we have personally loved all his courses. This is his course for people wanting to get started with Python.

This is an approximately 4 hours 30 minutes long video where you’ll get everything you need to get started with Python language. This course is developed by Mike Dane.

Python for Machine Learning

After learning Python, it’s best to first get a good knowledge about libraries you’ll need to use while working on ML and AI projects like Pandas, Matplotlib, Numpy.

This covers some basics of python in the first week. And then it advances to data structures and playing with data using python. The last week consists of analyzing US economic data and building a dashboard.

This is a wonderful specialization for all the ML enthusiasts out there. If you complete this course, you’ll be very comfortable with python language for data visualization, mining, manipulation, and other stuff that is required in data science.

Topics covered in this course are Intro to Python, Programming using python, libraries for data science, and data visualization.

The best resources for getting familiar with the libraries is to go through their official documentation. They are the best place to explore and get an in-depth understanding of it.

Basics of ML and AI

While learning python and the libraries, it is better to keep going through very basic courses of AI where you can gain an intuition of what actually it is.

Elements of AI is a free online course curated for all the beginners out there. You’ll get a basic concept of AI and their main goal is to demystify AI.

This is another wonderful course curated by Andrew Ng. It covers all points that anyone might want to know before dipping their toes into AI. This is meant for people from all traits and backgrounds.

AI For Everyone

Offered by DeepLearning.AI. AI is not only for engineers. If you want your organization to become better at using AI

Josh Gordon, in this series, nicely explains how anyone can get started with basic code in ML using the libraries 

This is a 6 weeks course that covers ML topics like linear regression, logistic regression, clustering, and recommender systems.

Machine Learning in-depth

After going through the basics of AI and exactly understanding the concepts behind it, ML is no more a magical black box. Now, you should get deeper into it and learn how to use frameworks readily available online to build your own ML models.

Machine Learning

If you ever want to get deep knowledge about all the types of Machine Learning models algorithms out there, this is the go-to course for you. The course content is very amazing and nicely formed. You’ll see the regression, classification, neural networks, anomaly detection, recommender systems, and much more throughout this course. It is filled with great content.

Best Books

Personally, we’ve found books to be the best source of knowledge after going through the courses. This is where you can strengthen your theoretical understanding of the concepts that you use in your ML projects.

1 – The Hundred-Page Machine Learning Book by Andriy Burkov

A very short book but with perfect knowledge. Andriy has compressed all the vital points in AI/ ML and put it in this 100 pages book [138 to be precise].

2 – Hands-on Machine Learning with Scikit-Learn, Keras and Tensorflow 2.0 Book by Aurelien Geron — O’Reilly

According to us, this book is an alternative to the Machine Learning and Deep Learning specializations by deeplearning.ai. We prefer this book as it has perfect explanations and every concept has a good code to try out side by side. 

3 – Deep Learning book by Ian Goodfellow

If you want to get deeper into the mathematical side of deep learning, then this book has everything that you need. It was published in 2015, so it is relatively old but the content is great.

Life 3.0 isn’t for learning AI and ML but it is a beautiful book that discusses the impact of Artificial Intelligence on the future of the human race and cosmic influence. The views of the author are interesting and it is indeed a great read.

Conclusion

Finally, we’ve reached the end of this entire list of resources. These are a lot and there are more! But don’t be overwhelmed. Once, you have gone through most of these resources, you’ll find it interesting to explore more and find new helpful resources on your own. The most important thing to know is that you shouldn’t get stuck in completing courses and books. Keep making projects every now and then. Make it a habit to build projects after every new skill you learn. According to me, that’s how you’ll know what you’ve learned and that’s how you keep yourself motivated — by building interesting projects.

Also, keep reading technical blogs and articles. Medium, Wired, TechCrunch are some of the great places for such technical blogs. Be up to date with recent research works. You’ll notice that in the start you won’t understand much of it but as you progress, everything starts making sense. It’s good to see recent progress in ML/ AI because everything keeps changing very quickly.

It’s a great journey ahead of you… Keep Learning! 

Please feel free to get in touch with us !