Machine Learning Handbook - Free AI-Powered Course

Machine Learning Handbook

This course is a hands-on intro to machine learning concepts anbd showcases the use of Python and its key ML libraries in practical coding examples.

Beginner

12 Lessons

2h 30min

Certificate of Completion

This course is a hands-on intro to machine learning concepts anbd showcases the use of Python and its key ML libraries in practical coding examples.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

5 Playgrounds

This course includes

5 Playgrounds

Course Overview

This course offers a thorough initiation into the field of machine learning (ML), a branch of artificial intelligence focussing on creating and analyzing statistical algorithms capable of generalizing and executing tasks autonomously, without requiring explicit programming instructions. The course encompasses fundamental concepts showcasing the use of Python and its key libraries in practical coding examples. It delves into crucial areas, including an exploration of common libraries and tools used in ML t...Show More

What You'll Learn

An understanding of the fundamentals of machine learning (ML) in data-driven decision-making processes

Familiarity with essential libraries and tools used for data preprocessing

Knowledge of practical applications of ML in image processing, computer vision, text analysis, and natural language processing (NLP)

Proficiency in distinguishing between different types of ML approaches

An understanding of the distinctions between ML methodologies and its advanced concepts

What You'll Learn

An understanding of the fundamentals of machine learning (ML) in data-driven decision-making processes

Course Content

Introduction to Machine Learning

Course Overview Overview of Machine Learning Machine Learning’s Significance in Data-Driven Decisions

Common Libraries and Tools for Machine Learning Tasks

Libraries for Machine Learning Tools for Data Preprocessing and Model Development

Types of Machine Learning

Supervised Learning Unsupervised Learning Reinforcement Learning Traditional Machine Learning vs. Deep Learning

Applications of Machine Learning

Image Processing and Computer Vision Natural Language Processing (NLP)

The Way Forward

Conclusion

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Souvik Kundu

Front-end Developer

Eric Downs

Musician/Entrepeneur

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

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