4.14 (382 reviews)

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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.

Description

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

Content

Data Preprocessing

Data Preprocessing

Regresssion

Simple Linear Regression

Multiple Linear Regression

Polynomial Linear Regression

Support Vector Regression(SVR)

Decision Tree Regression

Random Forest Regression

Classification

Logistic Regression

K-Nearest Neighbour

Support Vector Machine (SVM)

Kernel SVM

Decision Tree Classification

Random Forest Classification

Clustering

K-Means Clustering

Hierarchical Clustering

Reviews

S

Shreya30 May 2021

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

M

Mukund28 May 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.

D

Diego26 May 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

C

Carlos24 May 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.

T

Thien19 May 2021

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

H

Harshita17 September 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.

K

Kriti21 July 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.

D

Divyang18 July 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.

Y

Yamini15 July 2020

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

S

Srinjoy12 July 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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