Getting an introduction to basic Machine Learning concepts doesn’t have to be cumbersome or expensive. In fact, there is an abundance of free online blog posts, videos, and coding tutorials to walk you through the basics – from introductory content to common applications to algorithms to applied skills.
This post includes a round-up of some of the best free options for an introductory look at ML. It’s sequenced to logically follow through with each one, with each concept building upon the last. You can also pick and choose based on prior experience and interest.
In the end, you’ll find two applied-skill ML tutorials as well as a link to additional blog posts if you want to continue learning more about Machine Learning.
Here are the top beginner tutorials for ML:
1. Introduction to Machine Learning
Geeks for Geeks has some great online tutorials and their Introduction to Machine Learning post serves as a solid first foundational piece to start with. They cover what Machine Learning is and a brief overview of classification and categorization methods.
Read the entire post here.
2. What’s the Difference Between Machine Learning and Deep Learning?
In this video, data scientist Misra Turp explores how Machine Learning and Deep Learning are different from each other. She explores structural differences, differences in how types of models are trained, and how each produces results.
Watch the video:
https://www.youtube.com/watch?v=TJnMp9wuG7Q
3. Everyday Applications of Machine Learning
In this Medium article, the author answers the question: how do we use Machine Learning in everyday applications? Learn how ML is integrated into social media, search engine recommendations, fraud detection, NLP applications like Speech-to-Text APIs and Sentiment Analysis, video surveillance, and more.
Read the entire article here.
4. Types and Classifications
While also an introductory article, this post on Towards Data Science is worthwhile because it goes into great detail about the types of Machine Learning, including Supervised, Unsupervised, Reinforcement, and various subsets of each. It also explains ML classifications such as logistic regression, support vector machines, decision tree classification, and more.
Read the entire post here.
5. Understanding ML Algorithms
This article is a compilation of several posts that will help you better understand the nuances of ML algorithms. Click through each post to learn how ML algorithms work, types of ML algorithms, and common problems you may encounter and how to solve them.
Read the entire post here.
6. Bias and Variance
Now that you have a better understanding of ML algorithms, we’re going to look at two common problems you may encounter with ML models: bias and variance. This YouTube video uses easy-to-follow examples that make the concept more understandable and faster to master.
Watch here.
7. Evaluation Metrics
While there are many different evaluation metrics, choosing the correct one is critical when trying to train your ML model. This YouTube video breaks down the different types and how to successfully choose the right one.
Watch it here:
https://www.youtube.com/watch?v=LbX4X71-TFI
8. Build an ML Web App from Scratch
You have a foundational understanding of ML. Now it’s time to put your knowledge to the test. This YouTube tutorial will walk you through how to analyze data, build an ML model, and then build a web app from scratch using Streamlit and Python.
Follow along here.
9. ML and Python Tutorial
In the second YouTube tutorial, learn how to build an ML model that can successfully predict what type of music a person likes to listen to. The video also has great general knowledge content about Machine Learning.
Watch here:
https://www.youtube.com/watch?v=7eh4d6sabA0