Top 7 Free Google Machine Learning Courses

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Machine learning is one of the most in-demand skills in the tech industry today. This field combines computer science and statistics to develop algorithms that can learn from data and make predictions or decisions without explicit instructions. As such, many individuals are eager to learn more about this exciting and lucrative field, but they are often daunted by the cost of traditional education. Fortunately, there are many free online courses that can help you get started with machine learning and improve your chances of landing a great job.

Some of the best free online courses for learning machine learning include:

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1. Preparing for Google Cloud Certification: Machine Learning Engineer 

87% of Google Cloud-certified users are more confident in their cloud skills. This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized Google Cloud Professional Machine Learning Engineer certification.

Here’s what you need to do

1) Complete the Google Cloud Machine Learning Engineer Professional Training Certificate

2) Check out other recommended resources for the Google Cloud Professional Machine Learning Engineer exam

3) See the Machine Learning Professional Engineer Exam Guide

4) Fill out sample questions for professional machine learning engineers

5) Register for the Google Cloud Certification exam (remote or at the test center)

Applied learning projects

This professional certification includes hands-on labs using the Qwiklabs platform. This practical component allows you to apply the skills you have learned. Project includes Google Cloud Platform products used in Qwiklabs. You will gain practical experience with the concepts described in the modules.

2. Machine Learning on Google Cloud Specialization

This course teaches you how to create BigQuery ML models using fundamental SQL and Vertex AI AutoML models without writing a single line of code. with limited Docker experience, design Vertex AI bespoke training jobs that can deploy in containers; choose the best data preprocessing options for your use case, use Feature Store for data management and governance, and feature engineering to improve models. Write distributed ML models that scale in TensorFlow, make use of best practices to perform machine learning on Google Cloud, and use Vertex Vizier hyperparameter tweaking to incorporate the ideal combination of parameters that results in accurate, generalized models.

3. Advanced Machine Learning on Google Cloud Specialization

This 5-course specialization focuses on advanced machine learning topics using the Google Cloud Platform, where you’ll gain hands-on experience optimizing, deploying, and scaling ML production models of various types in hands-on labs. This specialization continues GCP Machine learning and teaches you how to build scalable, accurate, and production-ready models for structured data, images, time series, and natural language text. It concludes with a course on building recommendation systems. Topics introduced in previous courses are referenced in later courses, so it is recommended that you complete the courses in this exact order.

4. How Google does Machine Learning

Google thinks a little differently about machine learning: It’s about providing a unified platform for managed data sets, store of functions, how to build, train, and deploy machine learning models without writing a single line of code, to provide the ability to modify tag data, Build a desk notebook using frameworks like TensorFlow, SciKit Learn, Pytorch, R and others. Our Vertex AI platform also offers the ability to train user models, build component pipelines, and perform online and batch predictions. We also cover the five stages of converting a candidate’s machine learning use case and consider why it’s important not to skip them. Finally, we identify biases that machine learning can amplify and how to spot them.

5.Google Cloud Big Data and Machine Learning Fundamentals

This course introduces Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the process, challenges, and benefits of building pipelines for big data and machine learning models with Vertex AI in Google Cloud.
WHAT YOU WILL LEARN

Identify the data lifecycle in AI on Google Cloud and key big data and machine learning products.

Flow design with Dataflow and Pub/Sub and design flow with Dataflow and Pub/Sub.

Identify options for building machine learning solutions on Google Cloud.

Describe a machine learning workflow and key steps using Vertex AI and build a machine learning pipeline using AutoML.

6.IBM Machine Learning Professional Certificate

Machine learning is one of the skills most in demand for jobs related to advanced AI applications, an area where hiring has grown 74% annually over the past four years (LinkedIn). This IBM professional certification is for anyone interested in developing the skills and experience to pursue a career in machine learning and to take advantage of the main types of machine learning: unsupervised learning, supervised learning, deep learning, and reinforcement learning. It also complements your learning with special topics.

The program consists of 6 courses that will provide you with a solid theoretical understanding and extensive practice of basic algorithms, applications and best practices related to machine learning. You will track and code your own projects using some of the most relevant open source frameworks and libraries and apply what you have learned in various courses by completing completed projects.

7.Mathematics for Machine Learning Specialization

For many advanced courses in machine learning and data science, you’ll find that you need to brush up on the basics of math — things you may have learned in school or university, but are taught in a different context or are not very intuitive, therefore , you find it difficult to associate it with the use of computer science. This specialization aims to fill this gap by introducing you to basic mathematics, building intuitive understanding, and connecting it to machine learning and data science.

In our first course on linear algebra, we looked at what linear algebra is and how it relates to data. Next, let’s see what vectors and matrices are and how to work with them.

8.Google Cloud Computing Foundations

The Google Cloud Computing Foundations course is designed for people with little or no cloud computing experience. They provide an overview of key concepts for cloud, big data, and machine learning fundamentals, and where and how Google Cloud fits in.

By the end of the course series, the learner will be able to articulate these concepts and demonstrate some practical skills.

Courses must be taken in the following order:

  1. Google Cloud Computing Basics: Cloud Computing Basics
  2. Google Cloud Computing Fundamentals: Infrastructure on Google Cloud
  3. Google Cloud Computing Fundamentals: Networking and Security in Google Cloud
  4. Google Cloud Computing Foundations: Data, ML and AI in Google Cloud

9.Google Cloud Computing Foundations

The Google Cloud Computing Foundations course is designed for people with little or no cloud computing experience. They provide an overview of key concepts for cloud, big data, and machine learning fundamentals, and where and how Google Cloud fits in.

By the end of the course series, the learner will be able to articulate these concepts and demonstrate some practical skills.

Courses must be taken in the following order:

  1. Google Cloud Computing Basics: Cloud Computing Basics
  2. Google Cloud Computing Fundamentals: Infrastructure on Google Cloud
  3. Google Cloud Computing Fundamentals: Networking and Security in Google Cloud
  4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud

10. Google Cloud Computing Foundations: Networking & Security in Google Cloud

The Google Cloud Computing Foundations course is designed for people with little or no cloud computing experience. They provide an overview of key concepts for cloud, big data, and machine learning fundamentals, and where and how Google Cloud fits in.

By the end of the course series, the learner will be able to articulate these concepts and demonstrate some practical skills.

Courses must be taken in the following order:

  1. Google Cloud Computing Basics: Cloud Computing Basics
  2. Google Cloud Computing Fundamentals: Infrastructure on Google Cloud
  3. Google Cloud Computing Fundamentals: Networking and Security in Google Cloud
  4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud

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