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As a machine learning engineer, you can create effective machine learning solutions for real-world challenges. Sounds exciting, right? So how to become a machine learning engineer and what should you learn?
This collection of free courses from Google will help you go from a machine learning beginner to a skilled machine learning engineer who can understand and frame real-world problems as problems that can be addressed by machine learning. These courses will also help you learn advanced machine learning techniques, as well as design, test, and debug machine learning pipelines.
Let's start.
If you're new to machine learning, consider starting with this beginner's tutorial. Introduction to machine learning course.
In this course, you will learn:
- Types of machine learning
- Key concepts of supervised machine learning
- How machine learning is different from traditional problem-solving approaches
Link: Introduction to machine learning
He Machine Learning Crash Course is a practical introduction to machine learning using the TensorFlow framework. You will 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 decide if machine learning is necessary to solve the particular problem?
That's where the course Framing machine learning problems it becomes relevant. In this course, you will learn how to:
- Decide if machine learning is a good solution to the problem you are trying to solve.
- Framework Machine Learning Issues
- Choose the right machine learning model
- Define success metrics for the model.
Link: Introduction to Machine Learning Problem Framing
Machine learning is much more than throwing raw data and training machine learning algorithms with it. You should spend time understanding your data and focus on feature engineering to identify the most relevant and important features, process them, and transform them as necessary.
He Data preparation and feature engineering. The course will teach you the following:
- Impact of data quality and size
- Data collection and transformation within the ML workflow
- Collect raw data and build a usable data set from it.
- Handling imbalanced data
- Management of numerical and categorical data.
Link: Data preparation and feature engineering.
Debugging and testing machine learning systems is more complicated and different from testing traditional software systems.
the course on Testing and debugging Machine learning models will teach you:
- Debugging machine learning models
- Implement tests to help with debugging
- Optimizing machine learning models
- Monitoring model metrics
Link: Testing and debugging
Clustering is one of the most used unsupervised learning algorithms. In the practical introduction to grouping in the Group Of course, you will learn the following:
- Clustering for machine learning
- Preparing data
- Defining similarity
- K-means clustering
- Evaluation of results of clustering algorithms.
Link: Group
From recommendations on Amazon and other online shopping sites to series recommendations on Netflix, recommendation systems are very relevant in our daily lives.
He Recommendation systems The course will teach you what such recommendation systems entail and how you can create your own applications. Here's an overview of what you'll learn:
- Components of a recommender system
- Scale
- TensorFlow implementations of recommendation algorithms
Link: Recommendation systems
I hope you found this summary of free courses useful. Most of these courses are designed to give you enough opportunities to practice and build your own projects.
Try creating your own projects to apply what you have learned in the course. This will help them reinforce their knowledge and also develop their project portfolio. Happy learning and coding!
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. Bala also creates engaging resource descriptions and coding tutorials.
