Practical Machine Learning in Telugu
4.5 (3 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
11 students enrolled

Practical Machine Learning in Telugu

Learn Machine Learning and feel like Super Hero
4.5 (3 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
11 students enrolled
Created by Aravind Pilla
Last updated 1/2019
Telugu
Current price: $80.99 Original price: $124.99 Discount: 35% off
16 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 6.5 hours on-demand video
  • 16 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • What is Machine Learning?
  • What are different types of Algorithms.
  • How to do Predictions as Super Hero.
Course content
Expand all 35 lectures 06:14:58
+ Introduction
2 lectures 09:53

Introduction to Practical Machine Learning Course in Telugu

Preview 06:30

How this Course is different from other courses in market 

Preview 03:23
+ Linear Regression
8 lectures 01:15:53

Simple Linear Regression Intuition

Preview 18:48
Test what you learned so far
2 questions

Simple Linear Regression Python Part 1 

Simple Linear Regression Python code Part 1
10:01

Simple Linear Regression Python Part 2 

Simple Linear Regression Python code Part 2
02:02

Simple Linear Regression Python Part 3.

Find the Source Code of Salary data.

Download it and change extension from         ".py"      to    ".ipynb"   and open it in Jupyter Notebook

Simple Linear Regression Python code Part 3
05:22
Multi Linear Regression Intuition
14:35
Test what you learned so far
1 question
Multi Linear Regression Python Code Part 1
15:45
Multi Linear Regression Python Code Part 2
02:26

Find the Source Code of Startup data.

Download it and change extension from         ".py"      to    ".ipynb"   and open it in Jupyter Notebook

Multi Linear Regression Python Code Part 3
06:54
Test what you learned so far
1 question
+ Logistic Regression
5 lectures 57:31
Logistic Regression Intuition
21:59
Test what you learned so far
1 question
Confusion Matrix Intuition
07:24
Test what you learned so far
1 question
Logistic Regression Python Code Part 1
12:36
Logistic Regression Python Code Part 2
08:37
Test what you learned so far
1 question

Find the Source Code of data.

Download it and change extension from         ".py"      to    ".ipynb"   and open it in Jupyter Notebook

Logistic Regression Python Code Feature Scaling Part 3
06:55
+ Decision Tree
2 lectures 15:23
Decision Tree Intuition
05:19

Find the Source Code of  data.

Download it and change extension from         ".py"      to    ".ipynb"   and open it in Jupyter Notebook

Decision Tree Python code both Classification and Regression
10:04
Test what you learned so far
1 question
+ Random Forest
2 lectures 17:47
Random Forest Intuition
07:12

Find the Source Code of data.

Download it and change extension from         ".py"      to    ".ipynb"   and open it in Jupyter Notebook

Random Forest Python Code both Classification and Regression
10:35
Test what you learned so far
1 question
+ Gradient Boosting Machines
2 lectures 18:50
Gradient Boosting Machines Intuition
08:18

Find the Source Code of data.

Download it and change extension from         ".py"      to    ".ipynb"   and open it in Jupyter Notebook

Gradient Boosting Machines Python Code both Classification and Regression
10:32
Test what you learned so far
1 question
+ Project 1 : Loan_prediction
7 lectures 01:36:53
Loan Prediction Project Part 1
21:44
Test what you learned so far
3 questions
Loan Prediction Project Part 2
27:35
Loan Prediction Project Part 3
08:28
Test what you learned so far
1 question
Loan Prediction Project Part 4 (Logistic Regression)
14:18
Loan Prediction Project Part 5 (Decision Tree)
08:21
Loan Prediction Project Part 6 (Random Forest)
07:03

Find the Source Code of data.

Download it and change extension from         ".py"      to    ".ipynb"   and open it in Jupyter Notebook

Loan Prediction Project Part 7 (Gradient Boosting Machine)
09:24
+ Project 2 : Counterfeit Sales Prediction
7 lectures 01:22:48
Counterfeit Medicines Sale Prediction Part 1
18:08
Test what you learned so far
2 questions
Counterfeit Medicines Sale Prediction Part 2
16:56
Counterfeit Medicines Sale Prediction Part 3
09:02
Counterfeit Medicines Sale Prediction Part 4 (Linear Regression)
15:48
Counterfeit Medicines Sale Prediction Part 5 (Decision Tree Regressor)
06:52
Counterfeit Medicines Sale Prediction Part 6 (Random Forest Regressor)
05:17

Find the Source Code of data.

Download it and change extension from         ".py"      to    ".ipynb"   and open it in Jupyter Notebook

Counterfeit Medicines Sale Prediction Part 7 (Gradient Boosting Regressor)
10:45
Final Assessment
3 questions
Requirements
  • Eagerness to learn new concepts is more than enough.
  • Should know python basics at least. if you don't know even no problem we are providing it too.
Description

Hello Friends, this is a beginner to intermediate level course in which you learn intuitions of different machine learning concepts along with 2 Powerful Projects. This course has 6.5 hours of Video Content that will surely make you feel confident to attempt some competitions on Analytical Vidya, Kaggle etc. I tried hard to make this course more simple by both code wise and concept wise.

Who this course is for:
  • Beginner
  • Beginners to Intermediate