Machine Learning From Basic to Advanced

Learn to create Machine Learning Algorithms in Python Data Science enthusiasts. Code templates included.

3.80 (851 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Master Machine Learning on Python

Make accurate predictions

Make robust Machine Learning models

Use Machine Learning for personal purpose

Have a great intuition of many Machine Learning models

Know which Machine Learning model to choose for each type of problem

Use SciKit-Learn for Machine Learning Tasks

Make predictions using linear regression, polynomial regression, and multiple regression

Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, etc.

Why take this course?

Are you ready to start your path to becoming a Machine Learning Engineer!

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

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Machine Learning as well as Data Scientist!

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by Code Warriors the ML Enthusiasts so that we can share our knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way:

Part 1 - Data Preprocessing
Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression.
Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
Part 4 - Clustering: K-Means, Hierarchical Clustering.

And as a bonus, this course includes Python code templates which you can download and use on your own projects.

Screenshots

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Reviews

Emil

October 29, 2023

There is only an explanation on how the ML concepts work but no explanation on when to use them. It seems very weird to use some regression algorithms on the datasets that are presented.

Shyam

May 4, 2023

little more explaination and advanced topics should be expected. took basic questions which are readily available on internet.

Sandipan

July 31, 2022

as a student of statistics, I have prior knowledge of clustering, regression, etc. that are explained in this course even better to somewhat I knew. But I was expecting an even better approach to know machine learning, like in mathematical structure, and the ways of coding for AI and ML when it told that the course is from basic to advanced

Maheshbabu

June 2, 2022

Yes it was a good match for me. To learn more about this machine learning from basic to Advanced program course which the platform udemy is provoding.

Tim

December 20, 2021

I take a ton of Udemy classes. This class is really good. It's clear and to the point on how to use ML. There can be improvements like using Jupyter notebooks, deployments, how to better use predictions, and accuracy scoring. But overall, this course really hit home for me with how easy it was to learn. Thank you!

Shreya

May 30, 2021

The instructor did not explain why we are preferring one option over the other for the steps performed

Mukund

May 28, 2021

The course will give you the incites to understand the data driven mathematical functions to write softwares that can behave or change its behavior.

Diego

May 26, 2021

It starts right away. Doesn't explain what we will do, which is the goal and why we should follow this approach. It just takes a script and runs one line at a time

Carlos

May 24, 2021

The course lacks structure, quality is very low, audio is awful maybe the added resources can be useful but better explanations and proper introduction can be found for free on YouTube.

Thien

May 19, 2021

need more explanation of the mathematical model to help us understand why there is a data relationship

Harshita

September 17, 2020

It really was a good match for me. Best course for a quick revision so far. Instructor explained every line and word of the program, which can be really helpful for beginners. All in all, great experience.

Kriti

July 21, 2020

The way one of the trainer was speaking was a little bit hard to understand and adjust with. But good thing is I learnt new things and terminologies that have not even heard of before.

Divyang

July 18, 2020

The instructor was kind of reading the code. It would have been better if he would have explained why the code was written in this order.

Yamini

July 15, 2020

Explanation about code is good. concept explanation but why standardization used ?why not normalization needs to elaborate more

Srinjoy

July 12, 2020

Not a very descriptive course. Okay for those who just want to know the code to create the models. No proper explanation/intuition provided for the theory behind the models.

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