Machine Learning Resources for Getting Started
Resources for getting started with Machine Learning, one of the most valuable fields in tech.
Machine learning is a branch of Artificial Intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate how humans learn, gradually improving its accuracy.
This field is very demanding nowadays. We are now in the digital world so all things are now automated. Machine learning is the field of AI and it is capable to do so many things in the real world like Computer vision, Speech recognition, Recommendation engines, Fraud detection, and many more.
I have researched and collected all these resources for Machine Learning. YouTube channels, Books, Blogs, Podcasts, and much more you can find in this blog.
Libraries
Python is very popular for doing Machine Learning tasks. We can do Machine Learning with other programming languages like R, JavaScript, and C++, but Python is easy to understand and implement. Also, Python has lots of inbuilt libraries for doing Machine Learning related tasks. Here’s a list of some libraries that we can use for Machine Learning.
- Pandas
- NumPy
- SciPy
- Matplotlib
- Scikit-learn
- seaborn
- TensorFlow
- Theano
- Keras
- PyTorch
- OpenCV
YouTube Channels
Most of us are learning from videos and also we can easily understand things by visualizing it. Here are some YouTube channels that will help you learn Machine Learning from basic to advance level.
Books
Sometimes books give you a better understanding. Because I have seen some YouTubers teach things that are given in books. I listed some of the best books for Machine Learning which can give you better intuition for understanding concept of Machine Learning.
- The Hundred-Page Machine Learning Book by Andriy Burkov
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
- Machine Learning For Absolute Beginners: A Plain English Introduction (2nd Edition) by Oliver Theobald
- Machine Learning for Dummies by John Paul Mueller and Luca Massaron
- Pattern Recognition and Machine Learning by Christopher M. Bishop
- Fundamentals of Machine Learning for Predictive Data Analytics by John D. Kelleher
- Machine Learning with TensorFlow by Nishant Shukla
- Machine Learning for Hackers: Case Studies and Algorithms to Get you Started by Drew Conway and John Myles White
- Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper
- Bayesian Reasoning and Machine Learning by David Barber
- Machine Learning in Action by Peter Harrington
For more books, check this out:
Courses
Some university and platforms offer Machine Learning courses paid or free. I have selected some of these popular courses and mentioned here.
Project Ideas
Doing projects is the best way to learn new things. Here I listed some beginner level to advance level projects. It’ll surely help you in learning Machine Learning concepts.
- Stock Price Prediction
- Iris Flower Classification
- Titanic Survival Analysis
- House Price Prediction
- MNIST Digit Classification
- Heart Disease Analysis
- Spam Detection
- Credit Card Fraud Detection
- Customer Segmentation
- Recommendation System
- Sentiment Analysis
- Image Segmentation
→ These resources are enough for you if you are a beginner in Machine Learning and get into this field. You just need to be consistent.
→ Just a piece of advice that don’t do all things together. Learn one thing at a time. Yes, it’ll take time but you’ll get a better understanding of each concept. In my opinion, take one course and persist in it. Even reading books is also great to learn, so also read any book alongside.
Here are my other blogs on Machine Learning. Do check it out if you haven’t.
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