Source: Deep Learning on Medium
Artificial Intelligence it’s a journey, not a destination.
This means only one thing; you need to be prepared for constant learning.
Is it a tough path? With all the abundance of abstract terms and an almost infinite number of details, the AI and ML learning curve can indeed be steep for many. But, getting started with anything new is hard, isn’t it? Moreover, I believe everyone can learn it if only there is a strong desire.
Besides, there is an effective approach that will facilitate your learning. Like for example, you don’t need to rush, just start with small moves. Imagine a picture of everything you have learned. Every day you should add new elements to this picture, make it bigger and more detailed.
Today you can make your picture even bigger by dint of lots of tools out there that allow anyone to get started learning Machine Learning. No excuses! And you have not to be an AI wizard or mathematician. You just need to learn how to teach machines that work in ones and zeros to reach their conclusions about the world. You’re teaching them how to think!
Wanna learn how to do so? Here are the best books, courses and more that will help you do it more effectively without being confused.
Bes AI & ML Online Courses
If you want to know more about Artificial Intelligence and Machine learning, online course is a great opportunity to study theoretical aspects and solve practical problems. If you have a sufficient amount of time for this, use this chance. Here are a few courses that I will undoubtedly recommend:
This intensive course provides an in-depth introduction to AI and Machine Learning, it helps understand statistical modeling and discusses best practices for applying Machine Learning. Here you can learn everything about training and assessing models performing common tasks such as classification, regression, and clustering. All this is just in fifteen videos and 81 exercises with an estimated timeline of six hours.
By the end of this course, you’ll have a basic understanding of all the main principles. Consequently, it will equip you to transition into a role as a machine learning engineer.
Totally legendary and the most basic machine learning course from Andrew Ng, one of Coursera’s co-founders. Highly recommend this one. Why so? It provides an in-depth introduction and helps you understand statistical modeling and discusses best practices for applying. This is a really good course, after which many things in machine learning become clear.
In total, the course lasts 11 weeks. Each week involves 1–2 hours of video lectures, a test of knowledge of the theory and a practical task on the application of specific machine learning methods. In total, it took me 4–6 hours to complete all the material and complete all the tasks of one week.
It is important to complete practical tasks, you need to be able to program at least at the most basic level. Personally, I recommend that you complete all the tasks yourself. Nevertheless, if you do not strive to get a follow-up of course, you can not do them. As a last resort, GitHub is full of repositories with various ready-made solutions to practical problems.
In my opinion, the course has exactly one disadvantage — the code will need to be written in MATLAB. If this does not bother you, then don’t hesitate to take it.
Another one creation from Andrew Ng. I especially liked the third course, where Andrew talks about how to conduct research in the field of deep learning. But his advice can come in handy in classic ML. What background knowledge is necessary? Basic programming skills (understanding of for loops, if/else statements, data structures such as lists and dictionaries) and that’s all.
If you’re looking for a short yet concise online course that gives a great summarization to your already existing ML knowledge, this is the best choice for you. This course on Machine Learning with Python will equip you to understand the concepts of using data to predict future events.
Here you will learn to build predictive models and use Python to perform Supervised learning with scikit-learn, the most powerful ML library used by every Machine Learning Engineer and Data Scientist.
Last but not the least, this course will help you master ML on Python and R, make accurate predictions, build a great intuition of many machine learning models, handle specific tools like reinforcement learning, NLP and Deep Learning. In other words, here is everything you need to master!
* And one more suggestion concerning statistics. Where would we be without statistics?
In order to set up experiments and correctly calculate correlations, you need to know the statistics. There is an excellent course that I recommend. And if you are completely lazy, then use the book Head First Statistics. Small, with visual pictures — you can read it in just a couple of hours.
AI and Machine Learning Books
Well, then…if you want to dig a little deeper and figure out what’s what, there is no other way than reading good books! This approach can not boast of relevance, but this can be a source of information for a limited period of time and give you a fundamental understanding of technology and how it can be implemented for your tasks.
Nice book for everyone! The author reveals the methods of constructing models and machine learning algorithms. Here are carefully selected examples, accompanied by illustrations, which are gradually becoming more complicated. At the end of each part are links to additional literature with comments by the author.
This one is simpler and easier to read and also it has lots of practical examples. In general, this book will not make you a specialist in machine learning, but will introduce you to the basics in “human language” and show examples of use. Very suitable for the first acquaintance with the topic, especially when you have a background in programming.
One more great book I would recommend for everyone! It makes it clear why we need to study math and probability theory.
Must-read! This book is one of the most advanced in deep learning and machine learning. It also covers the mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
The book is a bestseller in the Artificial Intelligence section. A huge benefit of this book is the underestimated requirements for the reader’s knowledge. The book is a step-by-step journey through the mathematics of neural networks to create your own neural network using Python.
After reading, you can do the main thing: write code in Python, create your own neural networks, teaching them how to recognize various images, and even create solutions based on the Raspberry Pi. But this is not all, because there is also mathematics in the book, but it will not make you scream from horror and misunderstanding 😉
It’s hard for me to call this book a must-read, cause most experts usually get acquainted with this content in practice. However, this book can save you time on the invention of some bicycles and introduce you to the classical methods of speech recognition, language processing, and information retrieval. Whether this is necessary for the era of dominance of neural networks is up to you.
Through a minimal theory, application of concrete examples, and two pre-built Python production infrastructures — scikit-learn and TensorFlow — the author will help you to achieve an intuitive understanding of tools and concepts for building intelligent systems. Thanks to this book, you will learn a wide range of techniques, from simple linear regression and progression to deep neural networks. Totally recommend this book!
The book is intended for graduate students and is intended for those who have basic knowledge in the field of machine learning. I liked the emphasis on missing values of some of the chapters. Would recommend the middle part of the book as a good, but slightly unorthodox introduction to machine learning.
And the last book on this list that I can’t ignore. It is a fascinating series of essays that ponder the effect that the development of artificial intelligence might have in all the circles of our life. I am still reading it and it is an intellectual feast.
Additional Information and Useful Links
Wanna learn more? Have no time for reading books, or taking a course? Read articles or find needed stuff on GitHub. Here are some must-visited places for this:
Wrapping it up..
You don’t have to be great to start, but you have to start to be great ― Zig Ziglar.
That’s how I wanna end this post.
And the last thing, learn AI an ML, cause this is a super exciting time to be involved in this field. And you probably won’t regret it if you start this journey to new knowledge and spend your time on this. If believing the predictions of futurists, these technologies are our future!
Thanks for reading!