Udemy – Machine Learning Essentials (2023) – Master core ML concepts [Update 02/2023]

Udemy – Machine Learning Essentials (2023) – Master core ML concepts [Update 02/2023]
English | Tutorial | Size: 15.85 GB

Kickstart Machine Learning, understand maths behind essential algorithms, implement them in python & build 8+ projects!

Read to jumpstart the world of Machine Learning & Artificial intelligence?

This hands-on course is designed for absolute beginners as well as for proficient programmers who want kickstart Machine Learning for solving real life problems. You will learn how to work with data, and train models capable of making “intelligent decisions”

Data Science has one of the most rewarding jobs of the 21st century and fortune-500 tech companies are spending heavily on data scientists! Data Science as a career is very rewarding and offers one of the highest salaries in the world. Unlike other courses, which cover only library-implementations this course is designed to give you a solid foundation in Machine Learning by covering maths and implementation from scratch in Python for most statistical techniques.

This comprehensive course is taught by Prateek Narang & Mohit Uniyal, who not just popular instructors but also have worked in Software Engineering and Data Science domains with companies like Google. They have taught thousands of students in several online and in-person courses over last 3+ years.

We are providing you this course to you at a fraction of its original cost! This is action oriented course, we not just delve into theory but focus on the practical aspects by building 8+ projects.

With over 170+ high quality video lectures, easy to understand explanations and complete code repository this is one of the most detailed and robust course for learning data science.

Some of the topics that you will learn in this course.

Logistic Regression

Linear Regression

Principal Component Analysis

Naive Bayes

Decision Trees

Bagging and Boosting

K-NN

K-Means

Neural Networks

Some of the concepts that you will learn in this course.

Convex Optimisation

Overfitting vs Underfitting

Bias Variance Tradeoff

Performance Metrics

Data Pre-processing

Feature Engineering

Working with numeric data, images & textual data

Parametric vs Non-Parametric Techniques

Sign up for the course and take your first step towards becoming a machine learning engineer! See you in the course!

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