Best Resources to Learn Machine Learning
We know that the pandemic has us all at home; Working from home is becoming the new normal for many of us, and good face-to-face training is hard to come by these days, but that doesn’t mean you should stop learning! We would say that this is the perfect time to start learning something new, and why not start with machine learning.
Machine learning combines the concepts of statistics, mathematics, artificial intelligence, algorithms, and data science. The market is ripe for skilled machine learning experts, so to help you make a career in machine learning, we have listed some of the best resources to learn machine learning in 2022.
To learn about machine learning, read our blog – What is machine learning?
Top Blogs to Learn Machine Learning
OpenAI – It is an AI research and deployment company. Co-chaired by Elon Musk and Sam Altman, OpenAI is sponsored by companies like Amazon Microsoft, and Infosys, with an aim to make AI accessible for the masses. You can find research articles and the latest intelligence-based content.
Machine Learning is Fun – it is a very helpful blog to learn machine learning for beginners. You will learn through interactive tutorials and practical examples.
The BAIR Blog – The Berkeley Artificial Intelligence Research manages the BAIR Blog. You can explore this blog for research findings and important information about their AI-related work. The blog caters to a wider audience, starting from beginners to experts.
Neptune.AI – Neptune.AI includes tutorials on data exploration, generative models, ML Experiment Tracking, ML Model Management, MLOps, ML Tools, etc.
Must Read – Top 10 Machine Learning Algorithms for Beginners
Facebook AI’s Blog – Facebook AI’s blog has gained a lot of popularity in recent years for its quality content on topics like computer vision, conversational AI, NLP, machine learning theory, as well as human and machine intelligence, among others.
Top Books to Learn Machine Learning
- Pattern Recognition and Machine Learning (1st Edition) by Christopher M. Bishop
- Fundamentals of Machine Learning for Predictive Data Analytics by John D. Kelleher
- Machine Learning: The Art and Science of Algorithms that Make Sense of Data (1st Edition) by Peter Flach
- Programming Collective Intelligence: Building Smart Web 2.0 Applications (1st Edition) by Toby Segaran
- Basics of Linear Algebra for Machine Learning: Discover the Mathematical Language of Data in Python by Jason Brownlee
- Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares by Lieven Vandenberghe and Stephen P. Boyd
- Mathematics for Machine Learning by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth
- Linear Algebra and Optimization for Machine Learning: A Textbook by Charu C. Aggarwal
- Linear Algebra and Optimization with Applications to Machine Learning – Volume I: Linear Algebra for Computer Vision, Robotics, and Machine Learning by Jean Gallier and Jocelyn Quaintance
Must Read – Best Machine Learning Books for Beginners
Top YouTube Channels to Learn Machine Learning
Being trendy, handy, and interesting, YouTube Channels can be named among the best resources to learn machine learning,
Machine Learning 101
Machine Learning 101 presents machine learning from an interesting perspective, which is, through How-to videos on AI and machine learning concepts for beginners. You can also go through podcasts with experts.
A brainchild of Kevin Markham, Data School publishes detailed YouTube tutorials to understand artificial intelligence and machine learning, and focuses on topics demanded by the machine learning market.
Ng was named one of Time’s 100 Most Influential People in 2012. He is the co-founder of Coursera and deeplearning.ai and is an adjunct professor at Stanford University. His staple course on machine learning is very good for anyone wanting to kick start their career in machine learning.
Machine Learning University
Machine Learning University (MLU) is an Amazon initiative for helping their employees master machine learning technology.
Springboard is one of the most popular platforms for scientific researchers. It publishes interviews with machine learning experts and data scientists from large companies such as Google, Facebook, Amazon, Airbnb, etc. From these videos, you can get an idea of what it is like to be a machine learning expert and gain valuable professional advice.
Online Machine Learning Courses
One of the best ways to learn machine learning is to enroll in an online education program or take an online course. You can opt for self-study courses or virtual classroom courses, or those that combine the two. One of the main benefits of online learning is the flexibility it provides. Online learning gives you the option to learn at any time of the day and when it is most convenient for you, unlike traditional classroom learning. There are several online course providers on the market and different modes of online study; I have listed some of these online machine learning courses from the top course providers here –
Machine Learning by Stanford University on Coursera
Machine learning by Stanford University covers topics like supervised learning, unsupervised learning, best practices in machine learning, etc.
Machine Learning with Python by IBM on Coursera
The course talks about Machine Learning and its application in the real world and gives an overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.
Machine Learning for Data Science and Analytics by Columbia University on edX
This free course from edX will help you understand the principles of machine learning and derive practical solutions using predictive analytics.
Machine Learning Specialization by the University of Washington on Coursera
The Machine Learning Specialization covers practical Machine Learning case studies including Prediction, Classification, Clustering, and Information Retrieval. You will also learn how to analyze large and complex datasets, create better systems, and build intelligent applications.
You will learn about machine learning-based approaches for predictive modeling, including tree-based techniques, support vector machines, and neural networks using Python.
Learn Machine Learning: Step by Step
Here’s an interesting step-by-step learning path:
Get a solid foundation in linear algebra –Linear algebra is the prerequisite to understanding the deeper concepts of machine learning. A lot of machine learning can be formulated in terms of a series of matrix operations, and it sometimes makes more sense.
Read about some basic algorithm optimization – Algorithm optimization is the task of finding the input parameters to a function that determine the minimum or maximum output of the function. Algorithm optimization is one of the most crucial steps in creating machine learning models and it’s a good idea to learn about it beforehand.
Learn a little about probability. Since a large part of machine learning involves dealing with unstructured and often incomplete data and learning from it, a basic knowledge of probability and statistics can help you make estimates for further analysis. The further you go the more useful it will be when you want to run simulations or something like that.
Learn Statistical Distributions – Bernoulli Distribution, Binomial Distribution, Uniform Distribution, Normal Distribution, Exponential Data Analysis, Linear Regression, are the big ones that you will use a lot. You do not have to memorize the formulas, but you should know when to use them.
Machine learning has proved to be an extremely powerful tool for businesses and has the ability to dramatically transform any industry. The field of machine learning is constantly making new advances every day and has the potential to completely revolutionize our future. Hope this article on the best resources to learn machine learning helped you to move towards your career goal.
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