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As a machine learning engineer, you get to build effective ML solutions to real-world problems. Sounds exciting? But how do you become a machine learning engineer and what should you learn?
This collection of free courses from Google will help you go from being new to machine learning to being an accomplished ML engineer who understands and frames real-world problems as problems that can be solved using machine learning. These courses will help you design, test, and debug machine learning pipelines, as well as master advanced machine learning techniques.
Let’s get started.
If you are new to machine learning, consider starting with our Introduction to Machine Learning for Beginners course.
In this course you will learn:
- Types of Machine Learning
- Key concepts of supervised machine learning
- How machine learning differs from traditional problem-solving approaches
Link: Introduction to Machine Learning
Machine Learning Crash Course is a practical introduction to machine learning using the TensorFlow framework. Learn how machine learning algorithms work and how to implement them in TensorFlow.
This course is divided into the following sections:
- Machine Learning Concepts
- Machine Learning Engineering
- Machine learning in the real world
Link: Machine Learning Crash Course
Given a real-world problem, how do you solve it using a machine learning framework? First, how do you determine whether you even need machine learning to solve a particular problem?
This is where our course on framing machine learning problems comes in. In this course, you will learn how to:
- Determine if machine learning is the right solution for the problem you are trying to solve
- Framing the machine learning problem
- Choosing the right machine learning model
- Define success metrics for your model
Link: Introduction to framing machine learning problems
Machine learning is not just about inputting raw data and training an ML algorithm – it requires a focus on feature engineering to take the time to understand the data, identify the most relevant and important features, and then process and transform them as needed.
In the Data Preparation and Feature Engineering course, you will learn:
- Impact of data quality and data size
- Data collection and transformation within the ML workflow
- Collecting raw data and building usable datasets from it
- Handling Imbalanced Data
- Working with Numeric and Categorical Data
Link: Data Preparation and Feature Engineering
Debugging and testing machine learning systems is more complex and different than testing traditional software systems.
In the Testing and Debugging Machine Learning Models course, you will learn how to:
- Debugging machine learning models
- Implementing tests to aid in debugging
- Optimizing machine learning models
- Monitoring model metrics
Link: Testing and debugging
Clustering is one of the most widely used unsupervised learning algorithms. In our Practical Introduction to Clustering course, you will learn:
- Clustering for Machine Learning
- Prepare your data
- Definition of Similarity
- K-means clustering
- Evaluating the results of clustering algorithms
Link: Clustering
From recommendations on Amazon and other online shopping sites to series recommendations on Netflix, recommendation systems are highly relevant in our daily lives.
In the Recommendation Systems course, you will learn what is needed for a recommendation system and how to build your own application.
- Components of a Recommendation System
- embedded
- TensorFlow implementation of the recommendation algorithm
Link: Recommendation System
We hope you found this roundup of free courses useful. Most of these courses are designed to give you ample opportunity to practice and build your own projects.
Try to build your own projects to apply what you have learned in the course. This will deepen your understanding and help you build your project portfolio. Have fun learning and coding.
Bala Priya C Bala is a Developer and Technical Writer from India. He loves working at the intersection of Mathematics, Programming, Data Science, and Content Creation. His areas of interest and expertise are DevOps, Data Science, and Natural Language Processing. He loves reading, writing, coding, and coffee. Currently, he is committed to acquiring and sharing knowledge among the developer community by writing tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource compendiums and coding tutorials.
