Top resources to get started in ML - Part 1
Essential Resources for Building Strong Theoretical Foundations with Kartik Singhal
Hi everyone! 👋🏻
It’s Josep here again!
And today, I’m bringing some exciting news…
As the field of Machine Learning continues to evolve, aspiring ML professionals face the challenge of acquiring an ever-growing set of skills to excel in this domain.
With so many resources out there—many lacking in quality—
from ML Engineer Insights and I have decided to collaborate on a detailed guide to empower beginners in mastering Machine Learning.I know most of you might be wondering…
What’s required to be a successful ML engineer?
The answer is quite straightforward, you need to develop a strong foundation in several key areas, including:
Programming (in Python!!)
Mathematics and statistics
Understanding ML algorithms and models
Data handling and processing
Practical applications
All these skills require a balance between fundamental knowledge and practical experience.
To help you build a robust understanding of Machine Learning in both approaches, we're publishing two posts: One centered on resources for theoretical foundations to establish solid knowledge, and the other on practical experience to get your hands dirty.
Start your journey with this carefully selected list of resources designed to make your data side shine.
PART 1 - Mastering the basics
Online Courses and Tutorials
Online courses are a great way to break into a new field. However, only the right ones offer foundational knowledge and practical insights.
Here are some top-rated courses to get you started:
Fundamentals Courses
Fundamental courses will help you get a better theoretical understanding of the field. Our recommendations are:
Machine Learning: Probability and Statistics by Coursera
This course focuses on the statistical foundations of machine learning, bridging the gap between mathematical theory and practical application.Introduction to Machine Learning Specialization by Coursera
This free course is ideal for beginners, providing a comprehensive introduction to machine learning basics, from simple models to introduction to complex ones.Deep Learning Specialization by Coursera
Created by Andrew Ng, this course offers an in-depth exploration of neural networks and deep learning frameworks, guided by a leading expert in the field.CS229: Machine Learning by Stanford
Stanford's CS229 is a renowned course covering a wide range of topics, emphasizing both supervised and unsupervised learning with practical implementations.Josep’s Favorite: Practical Deep Learning for Coders by fast.ai
Fast.ai offers a hands-on approach to deep learning, making it accessible for coders who want to implement advanced models from the start.
Books
Some of the best Machine Learning resources, particularly books, are often hidden behind paywalls, and yet they can be invaluable for building a strong foundation. Here are some of our book recommendations:
Fundamentals
Introduction to Statistical Learning with Python by James et al.
Probably one of the best (and free!) books for traditional machine learning algorithms out there. Our recommendation is to do it jointly with the Andrew Ng course, referring to more detailed information about the concepts introduced in the course.
Machine Learning for Absolute Beginners: A Plain English Introduction by Oliver Theobald
This book serves as an accessible entry point for beginners, breaking down complex machine learning concepts into simple, understandable terms without overwhelming technical jargon.Kartik’s favorite: The Hundred-Page Machine Learning Book by Andriy Burkov
Despite its brevity, this book provides a comprehensive overview of key machine learning concepts and techniques, making it an ideal resource for those seeking a concise yet thorough introduction.
Practical Applications
Our personal favorite: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
This comprehensive guide provides an in-depth exploration of machine learning concepts and techniques using Python, perfect for readers looking to build a solid foundation in ML with practical examples. This book by itself can be a complete resource for a starting point.AI and Machine Learning for Coders by Laurence Moroney
Laurence Moroney’s book emphasizes practical code examples and tools like Google Cloud AI Platform, offering an approachable way to learn AI and ML applications without complex theory. The book itself assumes certain basic knowledge about traditional ML algorithms already so look at the above resources before starting out this one..Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen
Chip Huyen's book guides readers through designing machine learning systems, focusing on real-world challenges and practical considerations for deploying production-ready applications.
Specializations
Natural Language Processing:
Natural Language Processing with Transformers, Revised Edition
This book offers a comprehensive guide to NLP using transformers, covering basic concepts to advanced techniques for building state-of-the-art models.
Computer Vision:
Modern Computer Vision with PyTorch - Second Edition: A Practical Roadmap from Deep Learning Fundamentals to Advanced Applications and Generative AI
This book provides a practical roadmap for mastering computer vision with PyTorch, from deep learning fundamentals to advanced applications and generative AI, suitable for those exploring cutting-edge techniques.
Podcasts and YouTube Channels
Podcasts and YouTube channels offer a more personal touch, allowing you to learn directly from tech experts and industry leaders while building your projects. Here are some of the best resources to help you get started in the field.
Fundamentals
This YouTube video provides a beginner-friendly introduction to Machine Learning concepts, making it an excellent starting point for those new to the field.Deep Learning
This YouTube playlist provides an in-depth exploration of Deep Learning concepts and techniques, perfect for those looking to deepen their understanding of neural networks.Kaggle YouTube Channel
Kaggle's YouTube channel offers in-depth discussions and solutions for various data science challenges, making it an invaluable resource for aspiring data scientists.
Blogs and Articles
Towards Data Science
It is a popular Medium publication that offers a wide variety of articles on various aspects of data science, including machine learning, data visualization, and artificial intelligence. The TDS Editors have some curated lists of articles to crush into ML, you can check it out here.
Some of Josep’s favorite articles in TDS:
How I’d Learn Machine Learning (If I Could Start Over) by
.Statistics for people in a hurry by Cassie Kozyrkov
The Math Behind Neural Networks by Cristian Leo
KDnuggets
KDnuggets is a leading site for analytics, big data, data science, and machine learning. It aggregates content from various sources, offering a mix of news, opinions, tutorials, and best practices.
Some of Josep’s favorite articles in KDnuggets:
Step-by-Step Tutorial to Building Your First Machine Learning Model by
Beginner’s Guide to Machine Learning with Python by Nate Rosidi
Machine Learning Mastery
Created by Jason Brownlee, it is a comprehensive resource for machine learning practitioners. The blog offers practical tutorials and guides on machine learning and deep learning, with a strong focus on Python. They have a curated roadmap to get started and succeed in the ML field.
Eugene Yan’s Blog
Kartik’s favorite: Eugene Yan is a senior scientist at Amazon. He writes extensively on topics related to data science, machine learning, and career development, offering practical insights and advice through his blog. He has a great starting out page with a list of the most important articles.
Conclusion
Understanding the theoretical fundamentals of Machine Learning is essential for aspiring professionals. The resources we've covered—from online courses to books, YouTube channels and blogs—offer a solid starting point.
You don't need to complete all the resources of course.
These are high-quality resources, and following just a few will be enough to build a strong foundation. These tools will help you grasp key principles, concepts, and algorithms, preparing you for the challenges ahead.
This Saturday we'll dive into practical, hands-on learning resources with Part 2. We'll cover projects and techniques to help you apply your knowledge and build a standout portfolio.
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 | A guest post by
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Amazing list! Thank you for the mention as well.
One of the best things I have done while learning Machine Learning is following you on Substack and LinkedIn. Thank you so much for these valuable insights, @Kartik Sir and @Josep Sir.