As a citizen of a developing country, we are now getting obsessed with the word “Artificial Intelligence.” A lot of people thinking to become a Machine Learning Engineer or Data Scientists. So, I have tried to make a content carrying some of the best resources to learn Artificial Intelligence from basics to advance through a sequence of blogs, videos, and courses. I have also given you many tips and additional resources from my experience. I have tried to deliver almost everything that needs for Machine Learning Engineer or Data Scientist. Today’s whole discussion is about the best resources to learn Artificial Intelligence.
Why Artificial Intelligence or Machine Learning?
In the era of digital revolutions, data is floating through advanced Machine Learning. We, peoples, are producing data by the amount of volume and scale, which is humungous. But, people need a computer to convert data into meaningful information and analyze its length and breadth. Here Machine Learning takes place. We use computers for analyzing petabytes of information, so Machine Learning techniques with the right kind of data provide endless applications for sectors such as Science, Banking & Finance, Healthcare, Manufacturing and more.
Artificial Intelligence, Machine Learning, Deep Learning, Data Science are the buzzwords in the learning of Artificial Intelligence (AI). If you’re passionate about anything, then you’ll find free resources from your own. It would help if you had that motivation and enthusiasm.
Books, Labs, and Toolkit
The best resource for learning anything is booked. After the study, you need a place where you can practice based on your learning. And finally, a tool that decreases your time in learning of AI. So, let’s see some of the books, practice labs and toolkit.
Paradigms of Artificial Intelligence Programming (Book)
Let’s start with one of the oldest but widely regarded resources book named as “Case Studies in Common Lisp” that published in 1992. It isn’t written for a particular language for today’s Artificial Intelligence. No matter which language you’re using for programming, the author Peter Norvig covers all the aspects of AI programming in this definitive resource. You’ll get this book with the all code from the GitHub link for free. Also, you can purchase it from Online.
An Introduction to Statistical Learning (Book)
This book which is written by professors at USC, Stanford and the University of Washington, is available in PDF format for free. It focused on the statistical computing languages with all lab codes that are often used for AI programs and machine learning. The book has described “The ‘how-to’ manual for statistical learning.” After the completion of this book, move on to next “The Elements of Statistical Learning,” followed by the author that is also available for free online. You can purchase both books as well.
Machine Learning from Theory to Algorithms (Book)
You need to understand theoretical as well as practical when you desired to “getting” machine learning. This book from Cambridge University Press in 2014 is great because of its reputation, and also you can access several courses online. It is available in PDF format for free and provides a supplement of your learning, no matter what way you choose.
Neural Networks and Deep Learning (Book)
Michael Nielsen, the fellow of Y Combinator Research, has written this online book that covers everything from differences between shallow and deep networks including plenty of hands-on exercises and the backpropagation technique, how it works with issues via training neural networks, etc. And most important, it is available and accessible for free to everyone. The author suggests that readers who enjoy the content of the book can make a small donation of 5 dollars via the given Website in the book.
Learn Python 3 the Hard Way (Book)
If you are going to use Python and Python 3 – the most popular languages for machine learning, then you need to study Zed Shaw’s “Learn Python 3 the Hard Way: A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code.” Instead of focusing on AI, this book will help you to gain knowledge of Python. It can be purchased, and you will get bonus videos and exercises when you buy it from the author.
IBM Watson Starter Kits (Labs)
IBM, with its omnipresent cognitive computing tool/AI platform, offers a wealth of resources for developers, enterprises who want to start with Watson. IBM also offers a free IBM Cloud account to it. The company will provide you with various Starter Kits once you set up everything. The Kits help you to move forward, step-by-step through different scenarios. If you like the work, then you can dive in deeper. Besides a great introduction to Watson, these Smarter Kits are the power of AI. IBM is a great place to start as a beginner.
Microsoft’s Cognitive Toolkit (Toolkit)
The commercial-graded, open-source toolkit from Microsoft offers deep-learning capabilities for unstructured data in free of cost. It helps users to quickly realize and combine popular model types such as convolutional neural networks (CNNs), feed-forward DNNs, and recurrent neural networks (RNNs/LSTMs). Implementation of stochastic gradient descent learning such as SGD, and error backpropagation with automatic differentiation and parallelization by CNTK across multiple servers and GPUs.
Online Datasets, Articles, Videos, and Training
Now, in the era of digitization, everything is available online. So, you will get the resources to learn Artificial Intelligence through online datasets, videos, articles and online training classes.
You need data to get hands-on with machine learning. And, Kaggle is one of the most popular Websites where you’ll get free datasets offered by data science world. You are bound with datasets through finding something, starting from U.K. traffic accidents to stock data, to top Spotify tracks, to the air quality in India. It correlates with the other Website’s datasets that help to cover a lot using Kaggle.
Google Dataset Search (Datasets)
Google knows how much benefit machine learning will provide and the need and love of students towards the data. Students have to be able to find their need easily and quickly in the right format. So the company has launched (surprisingly just recent) a search option to search the Internet for only datasets. The results come from a well-formatted site (like Kaggle) includes the information in data format. Google also notes the number of articles for providing descriptive details to the user.
Open Data on AWS (Datasets)
Amazon Web Services (AWS) offers a vast collection of datasets such as air quality data, satellite imagery, rice genomes and brain scans. Human Microbiome Project gathers a feather in AWS with more than 80 organizations and 300 scientists who came together to collect this data. The samples of Microbial were taken from 300 adult subjects that generate results of 45TB data over the referred 1,128 genomes, including 2,400 metagenome sequence datasets from healthy subjects. This dataset can be accessed on the portal site of the project.
James McCaffrey’s Data Science Lab (Article Series)
If you’re ready for machine learning, then James McCaffrey’s article series is essential to read and should not miss it. Microsoft Researcher James McCaffrey focused on “R” through the topics “Linear Regression with R,” “Neural Networks Using the R net Package,” and “Python” through the topics “Neural Network Back-Propagation Using Python,” “Neural Network L1 Regularization Using Python,” and much more in this article series. As it is an ongoing article series, so every month new content is added.
TensorFlow.org’s Neural Network Playground (Interactive Website)
“Don’t Worry. You Can’t Break It. We Promise.” With this tagline, the site can set up a neural network in a device that is precisely what expected from its name. You can play it with your browser. There’s a wide variety of factors that need to adjust. It unleashes numerous hidden layers and turns on or turn off the various controls. It’s available on GitHub whether you want to use in a new way.
Hugo Larochelle’s Neural Network Videos (Video Series)
When you’re ready to dive into neural networks, then jump over YouTube and dive into Université de Sherbrooke’s Hugo Larochelle’s excellent video collections. Even the comments are constructive under the video. The series does not only consist of the videos; you will also get PDFs of the slides recommended by the author and excellent outline of the entire course.
Google’s Machine Learning Crash Course (Online Training)
Google’s Machine Learning Crash Course that focuses on using TensorFlow offers from one of the oldest resources to one of the newest as per company’s open-source machine learning framework. When it releases, it became an instant hit in the earlier 2018. With a free 15-hour course, it provides 25 lessons, video lectures, and 40 exercises from Google researchers. When you’re done, go to Google’s AI page for more free offers.
Machine Learning Coursera of Andrew Ng’s Course (Online Training)
AI professionals refer to the course of Stanford Assistant Professor Andrew Ng as “the gold standard” in machine learning. His lecture consists of 11 weeks of training, and after the training period, your skills will be tested. It is free for learning, but if you want a certificate, then you need to pay 79 dollars. Otherwise, you can walk away with the knowledge from the course. If you are serious and want to move forward with machine learning in your career, then make your background skills healthy through other resources and then sign up for Andrew Ng’s Machine Learning course.
MIT Linear Algebra and Artificial Intelligence Class (Online Training)
As math is essential for all areas of programming and vice versa, you need to brush up the linear algebra before you dive into aspects of machine learning. For a quick refresh, MIT Open CourseWare offers the online course from lectures to exams – the entire curriculum for free.
Also, the MIT Open CourseWare course include a second course that covers AI through the videos. Watch the videos whose topics are the part of AI journey of your interest.
Professional Program from Microsoft for Artificial Intelligence (Online Training)
Besides Google, Microsoft is also providing a free AI training course this year and available to everyone via the Internet. The extensive learning track of Google consists of 10 individual courses with titles such as “Introduction to Python for Data Science,” “Build Machine Learning Models” and “Develop Applied AI Solutions.” You can take either one course or a mix of courses or all of them. Each course lasts for a particular number of weeks. You need to pay separately to earn the overall program certificate.
In my opinion, you need to study first, then took the online training and did the practice again and again. So it would help if you went through the first portion of my article, and then find suitable online training courses for your skill development. Moreover, if you think we are missing out on some essential points, drop a comment below. And share this article among your connection to deliver the best resources to learn Artificial Intelligence.