Want to pursue a career as an Machine Learning engineer? Google’s free training programs can help you get there.
As a machine learning engineer, you may create practical ML solutions for real-world problems. Sounds interesting, right? So, how does one grow into a machine learning engineer, and what should they learn?
This collection of free Google courses will help you progress from a machine learning novice to a proficient ML engineer who can identify and frame practical issues as challenges that can be solved using machine learning. These courses will also teach you cutting-edge techniques, such as creating, testing, and troubleshooting pipelines.
Fundamentals to Machine Learning.
If you’re unfamiliar with machine learning, take our helpful for beginners Introduction to Machine Learning course.
This course will teach you the following:
Types of Machine Learning
Key aspects of supervised machine learning:
How does machine learning differ from previous problem-solving approaches?
Link: Introduction to Machine Learning.
Machine Learning Crash Course.
The Machine Learning Crash Course provides a hands-on introduction to machine learning with the TensorFlow framework. You’ll learn about machine learning algorithms and how to use them in TensorFlow.
This course includes the following sections:
Machine Learning Concepts
Machine learning engineering
Machine learning in the real world.https://developers.google.com/machine-learning/crash-course
Link: Machine Learning Crash Course.
Machine Learning Challenge Formulation
How would you tackle a real-world problem utilizing a ML framework? First and foremost, how do you determine whether machine learning is essential to tackle the specific problem?
This is where the course on Machine Learning Problem Formulation comes in handy. This course will teach you how to:
Determine whether machine learning is an effective answer to the problem you’re attempting to address.
Frame machine learning issues.
Choose the proper machine learning model.
Define the success measures for the model.
Link: Introduction to ML Problems Framing.
Data preparation and feature engineering.
Machine learning entails much more than simply feeding raw data into ML algorithms. You must spend time understanding your data and focusing on feature engineering to discover the most relevant and critical features, then process and modify them as needed.
The Data Preparation and Feature Engineering course will teach you the following.
Impact of data quality and data size.
Data collection and manipulation in the ML pipeline.
Collecting raw data and creating useable datasets from it.
Handling unbalanced data
Handling numerical and categorical data.
Link for Data Preparation and Feature Engineering
Testing & Debugging
Troubleshooting and validating machine learning systems requires more effort and differs from testing standard software systems.
The course Assessing and Trying to debug Machine Learning Models will teach you the following:
Troubleshooting Machine Learning Models
Implementing testing to assist with debugging
Optimizing ML models.
Monitoring model metrics
Link: Testing and Debugging.
Clustering
Clustering is one of the most used unsupervised learning methods. The Clustering course’s hands-on approach to clustering covers the following topics:
Clustering for Machine Learning
Preparing Data
Define resemblance.
K-means clustering.
evaluating the outcomes of clustering algorithms
Link: Clustering.
Recommendation Systems
From Amazon and other online shopping sites to Netflix series suggestions, recommendation algorithms play an important role in our daily lives.
The Recommendation Systems course will show you what goes into recommendation systems and how to create your applications. Here is a summary of what you will learn:
Elements of a recommendation system
Embeddings and TensorFlow implementations for recommendation algorithms
Link: Recommendation Systems.
Wrapping Up
I hope you found this roundup of free courses useful. The majority of these courses are designed to provide you with plenty of opportunities to practice and create your projects.
So, attempt to create your projects to utilize what you’ve learned in the course. This can allow you to strengthen your understanding while also expanding your project portfolio. Happy learning and coding!
