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Machine learning is becoming more and more popular in the data field. But we often think that to become a machine learning engineer, you need to have an advanced degree. However, this is not entirely true. Because skills and experience always trump qualifications.
If you’re reading this, you’re probably new to data and want to get started as a machine learning engineer. Perhaps you already work in data as a data analyst or BI analyst and want to transition into a machine learning role.
Whatever your career goals, we’ve put together a list of machine learning courses, completely free, to help you master machine learning. We’ve included courses that will help you understand both the theory and building of machine learning models.
Let’s get started!
If you are looking for an accessible machine learning course, Machine learning for everyone is for you.
Taught by Kylie Ying, this course takes a code-first approach to building simple and interesting machine learning models in Google Colab. Creating your own notebooks and creating models while learning just enough theory is a great way to get familiar with machine learning.
This course makes machine learning concepts accessible and covers the following topics:
- Introduction to Machine Learning
- K-Nearest Neighbors
- Naive Bayes
- Logistic regression
- Linear regression
- K-Means Clustering
- Principal component analysis (PCA)
Course link: Machine learning for everyone
Kaggle is a great platform to participate in real-world data challenges, build your data science portfolio, and hone your model building skills. Additionally, the Kaggle team also offers a series of micro-courses to familiarize you with the fundamentals of machine learning.
You can consult the following (micro) courses. Each course generally takes a few hours to complete and complete the exercises:
- Introduction to Machine Learning
- Intermediate machine learning
- Feature Engineering
THE Introduction to Machine Learning The course covers the following topics:
- How ML Models Work
- Data mining
- Model validation
- Underfitting and overfitting
- Random Forests
In the Intermediate machine learning course, you will learn:
- Handling missing values
- Working with categorical variables
- ML Pipelines
- Cross validation
- XGBoost
- Data leak
THE Feature Engineering the course covers:
- Mutual information
- Creation of features
- K-Means Clustering
- Principal component analysis
- Target encoding
It is recommended that you take the courses in the order above so that you have the prerequisites covered when you move from one course to the next.
Course link:
Machine learning in Python with Scikit-Learn on the FUN MOOC platform is a free, self-paced course created by the developers of the scikit-learn core team.
It covers a wide range of topics to help you learn how to create machine learning models with scikit-learn. Each module contains video tutorials and associated Jupyter notebooks. You should have some familiarity with Python programming and Python data science libraries to get the most out of the course.
Course content includes:
- Predictive modeling pipeline
- Model performance evaluation
- Hyperparameter tuning
- Selection of the best model
- Linear models
- Decision Tree Models
- Set of templates
Course link: Machine learning in Python with Scikit-Learn
Machine learning crash course from Google is another good resource for learning machine learning. From the basics of building a model to feature engineering and more, this course will teach you how to build machine learning models using the TensorFlow framework.
This course is divided into three main sections, with the majority of course content in the ML Concepts section:
- ML Concepts
- ML Engineering
- ML Systems in the Real World
To take this course, you should be familiar with high school math, Python programming, and the command line.
The ML Concepts section includes the following:
- ML Foundations
- Introduction to TensorFlow
- Feature Engineering
- Logistic regression
- Regularization
- Neural networks
The ML Engineering section covers:
- Static or dynamic training
- Static or dynamic inference
- Data dependencies
- Justice
And ML Systems in the Real World is a set of case studies to understand how machine learning is done in the real world.
Course link: Machine learning crash course
So far, we’ve seen courses that give you an overview of theoretical concepts while focusing on building models.
While this is a good start, you will need to understand in more detail how machine learning algorithms work. This is important for acing technical interviews, growing in your career, and getting started in ML research.
CS229: Machine learning at Stanford University is one of the most popular and recommended ML courses. This course will give you the same technical depth as a semester-long college course.
You can access courses and course notes online. This course covers the following main topics:
- Supervised teaching
- Unsupervised learning
- Deep learning
- Generalization and regularization
- Reinforcement learning and control
Course link: CS229: Machine learning
I hope you found some useful resources to help you on your machine learning journey! These courses will help you achieve a good balance between theoretical concepts and practical model building.
If you are already familiar with machine learning and are limited on time, I recommend checking out Machine Learning in Python with scikit-learn for an in-depth look at scikit-learn and CS229 for the essential theoretical foundations. Good learning!
Bala Priya C is an Indian developer and technical writer. She enjoys working at the intersection of mathematics, programming, data science, and content creation. His areas of interest and expertise include DevOps, data science and natural language processing. She loves reading, writing, coding and coffee! Currently, she is working on learning and sharing her knowledge with the developer community by creating tutorials, how-to guides, opinion pieces, and more.
