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Machine learning is becoming increasingly popular in the data space. But there is often the idea that to become a machine learning engineer you need to have an advanced degree. However this is not entirely true. Because skills and experience trump titles, always.
If you’re reading this, you’re probably new to the data field and want to get started as a machine learning engineer. Perhaps you already work in data as a data analyst or BI analyst and would like to move into a machine learning role.
Whatever your career goals, we’ve curated a list of machine learning courses, which are completely free, to help you become proficient in machine learning. We have included courses that will help you understand both the theory and construction of machine learning models.
Let’s start!
If you are looking for a machine learning course that is accessible, Machine learning for everyone is for you.
Taught by Kylie Ying, this course takes a code-first approach to building simple and engaging machine learning models in Google Colab. Creating your own notebooks and building models while learning 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 modeling skills. Additionally, the Kaggle team also has a series of microcourses to get you up to speed on the fundamentals of machine learning.
You can consult the following (micro)courses. Each course will typically take a few hours to complete and work on the exercises:
- Introduction to machine learning
- Intermediate machine learning
- Feature Engineering
He Introduction to machine learning The course covers the following topics:
- How machine learning models work
- Data exploration
- Validation model
- Misfit and overfit
- Random forests
In it Intermediate machine learning Of course, you will learn:
- Handling missing values
- Working with categorical variables
- Machine Learning Pipelines
- Cross validation
- XGBoost
- data leak
He Feature Engineering The course covers:
- Mutual information
- Creating 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 prerequisites covered when moving 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 scikit-learn core team developers.
It covers a wide range of topics to help you learn how to build machine learning models with scikit-learn. Each module contains video tutorials and accompanying Jupyter notebooks. You should be familiar with Python programming and Python data science libraries to get the most out of the course.
Course contents include:
- Predictive Modeling Pipeline
- Model performance evaluation
- Hyperparameter tuning
- Selecting the best model
- Linear models
- Decision tree models
- model set
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 the course content in the ML concepts section:
- Machine learning concepts
- Machine learning engineering
- Machine learning systems in the real world
To take this course, you must be familiar with high school math, Python programming, and the command line.
The ML concepts section includes the following:
- Machine Learning Fundamentals
- Introduction to TensorFlow
- Feature Engineering
- Logistic regression
- Regularization
- Neural networks
The ML Engineering section covers:
- Static versus dynamic training
- Static versus 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 have seen courses that give you an insight into theoretical concepts while focusing on model building.
While this is a good start, you will need to understand in greater detail how machine learning algorithms work. This is important for conducting technical interviews, growing in your career, and engaging 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 lectures and lecture notes online. This course covers the following broad topics:
- Supervised learning
- Unsupervised learning
- Deep learning
- Generalization and regularization.
- Reinforcement learning and control.
Course link: CS229: Machine Learning
I hope you found 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 have time constraints, I recommend checking out Machine Learning in Python with scikit-learn for a deeper dive into scikit-learn and CS229 for essential theoretical foundations. Happy learning!
Bala Priya C. is a developer and technical writer from India. He 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. He likes to read, write, code and drink coffee! Currently, he is working to learn and share his knowledge with the developer community by creating tutorials, how-to guides, opinion pieces, and more.
