# Machine Learning From Basic to Advanced – (Free Course)

0
457

## What you’ll learn

1. Master Python machine learning
2. Make accurate predictions
3. Build robust machine learning models
4. Use machine learning for personal purposes
5. You have great intuition for many machine learning models
6. Find out which machine learning model to choose for each type of problem
7. Use SciKit-Learn for machine learning tasks
8. Make predictions with linear regression, polynomial regression, and multiple regression
9. Classify data using K-Means Clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and others.

## This course includes:

• 3 hours on-demand video
• Access on mobile and TV
• Certificate of completion

## Description

This comprehensive course is your guide to learning how to harness the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!

Data Scientist is ranked the #1 job on Glassdoor and the average salary for a Data Scientist in the United States is over \$120,000 according to Indeed! Data Science is a rewarding career that will allow you to solve some of the most interesting problems in the world!

This course is aimed at beginners with programming experience and experienced developers looking to dive into machine learning, as well as data scientists!

Are you interested in the field of machine learning? Then this course is for you!

This course was created by Code Warriors, ML enthusiasts, so we can share our knowledge and help you learn complex coding theories, algorithms and libraries in an easy way.

We guide you step by step through the world of machine learning. With each lesson, you will develop new skills and increase your understanding of this challenging but rewarding subfield of data science.

This course is fun and exciting, but at the same time we are deep into machine learning. It is structured as follows:

Part 1 – pre-processing of data

Section 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression.

Section 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

Section 4 – Grouping: K-Means, hierarchical clustering.

And as a bonus, the course comes with Python code templates that you can download and use in your own projects.

## How to Get this course FREE?

Note: The udemy Courses Will be free for a Maximum of 1000 Learners can use the promo code AND Get this course 100% Free. After that, you will get this course at a discounted price. (Still, It’s a good deal for you to get this course at a discounted price).

External links may contain affiliate links, meaning we get a commission if you decide to make a purchase. Read our disclosure.