Top 7 Free Google Courses to Become a Machine Learning Engineer

 In 2024, big tech giants company offers a selection of free Google courses designed to equip individuals with the skills needed to excel in the field of Machine Learning. Let’s explore the top 7 free Google courses that pave the way for aspiring Machine Learning Engineers:

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Here Are the Top 7 Free Google Courses to Become a Machine Learning Engineer

1. Introduction to Machine Learning

Consider beginning with this approachable Introduction to Machine Learning course if you’re new to machine learning.

This course will teach you:

The several kinds of machine learning
Fundamental ideas in supervised machine learning
How machine learning differs from conventional methods of problem-solving

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2. Machine Learning Crash Course

A practical introduction to machine learning using the TensorFlow framework is provided by the Machine Learning Crash Course. You will discover the inner workings of machine learning algorithms and how to use TensorFlow to implement them.

The following sections make up this course:

Concepts of machine learning
Engineering for machine learning
Real-world applications of machine learning

Course Link Click Here

3. Machine Learning Problem Framing

What steps would you take to tackle a real-world problem with a machine-learning framework? First of all, how can you determine whether machine learning is actually required to tackle the specific problem?

This is where the Machine Learning Problem Framing course comes in handy. This course will teach you how to:

Determine whether applying machine learning to the problem you’re trying to solve is a sensible idea.
Problems with machine learning frameworks
Select the appropriate machine learning model.
Define the model’s success measures.

Course Link Click Here

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4. Data Preparation and Feature Engineering

It goes much beyond simply adding raw data and using it to train machine learning algorithms. To find the most pertinent and significant features, process them as necessary, and convert them as needed, you must invest time in understanding your data and concentrating on feature engineering.

You will learn the following in the Data Preparation and Feature Engineering course:

Impact of size, quality, and data
Gathering and transforming data for the machine learning process
gathering unprocessed data and turning it into a useful dataset
Managing Unbalanced Data
Managing both category and numerical data

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5. Testing and Debugging

Testing and debugging machine learning systems differs from testing standard software systems and requires additional work.

You will learn the following in the Testing and Debugging Machine Learning Models course:

Resolving issues with machine learning models
Putting testing into practice to aid with debugging
enhancing models for machine learning
Keeping an eye on model metrics

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6. Clustering

Among the most popular unsupervised learning algorithms is clustering. You will study the following in the Clustering course’s practical introduction to clustering:

Machine learning using clustering
Getting ready for data

What constitutes a resemblance
K-means grouping
Analyzing clustering algorithms’ output

Course Link Click Here

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7. Recommendation Systems

Recommendation algorithms play a significant role in our daily lives, from Netflix series suggestions to Amazon and other online retailers’ recommendations.

You will learn how to create your own applications and what goes into recommendation systems in the Recommendation Systems course. Here’s a summary of what you will discover:

Recommendation system components: TensorFlow implementations of recommendation algorithms included in

Course Link Click Here