The Complete Supervised Machine Learning Models in Python

The Complete Supervised Machine Learning Models in Python, available at $64.99, has an average rating of 3.8, with 86 lectures, based on 224 reviews, and has 2383 subscribers.

You will learn about Learn Complete Supervised Machine Learning Models in Python Learn the Math behind every Machine Learning Model Learn the Intuition of each Model Learn to make simple and GUI Based Templates Learn to choose the best Machine Learning Model for a specific problem This course is ideal for individuals who are Anyone who wants to learn Supervised Machine Learning Models or Anyone who wants to learn the Math behind Machine Learning Models or Anyone curious about Data Science It is particularly useful for Anyone who wants to learn Supervised Machine Learning Models or Anyone who wants to learn the Math behind Machine Learning Models or Anyone curious about Data Science.

Enroll now: The Complete Supervised Machine Learning Models in Python

Summary

Title: The Complete Supervised Machine Learning Models in Python

Price: $64.99

Average Rating: 3.8

Number of Lectures: 86

Number of Published Lectures: 86

Number of Curriculum Items: 86

Number of Published Curriculum Objects: 86

Original Price: $94.99

Quality Status: approved

Status: Live

What You Will Learn

Learn Complete Supervised Machine Learning Models in Python
Learn the Math behind every Machine Learning Model
Learn the Intuition of each Model
Learn to make simple and GUI Based Templates
Learn to choose the best Machine Learning Model for a specific problem

Who Should Attend

Anyone who wants to learn Supervised Machine Learning Models
Anyone who wants to learn the Math behind Machine Learning Models
Anyone curious about Data Science

Target Audiences

Anyone who wants to learn Supervised Machine Learning Models
Anyone who wants to learn the Math behind Machine Learning Models
Anyone curious about Data Science

In this course, you are going to learn all types of Supervised Machine Learning Models implemented in Python. The Math behind every model is very important. Without it, you can never become a Good Data Scientist. That is the reason, I have covered the Math behind every model in the intuition part of each Model.

Implementation in Python is done in such a way so that not only you learn how to implement a specific Model in Python but you learn how to build real times templates and find the accuracy rate of Models so that you can easily test different models on a specific problem, find the accuracy rates and then choose the one which give you the highest accuracy rate.

I am looking forward to see you in the course..

Best

Course Curriculum

Chapter 1: Introduction

Lecture 1: What is Machine Learning

Lecture 2: Supervised vs Unsupervised Machine Learning Models

Lecture 3: Installing the Spyder IDE

Lecture 4: Data sets for the Course

Chapter 2: Simple Linear Regression Model

Lecture 1: Simple Linear Regression Intuition 1

Lecture 2: Simple Linear Regression Intuition 2

Lecture 3: Simple Linear Regression Intuition 3

Chapter 3: Simple Linear Regression implementation in Python

Lecture 1: Simple Linear Regression Python Part 1

Lecture 2: Simple Linear Regression Python Part 2

Lecture 3: Simple Linear Regression Python Part 3

Lecture 4: Simple Linear Regression Python Part 4

Lecture 5: Simple Linear Regression Python Part 5

Lecture 6: Simple Linear Regression Python Part 6

Lecture 7: Simple Linear Regression Python Part 7

Chapter 4: Multiple Linear Regression Model Intuitions

Lecture 1: Multiple Linear Regression Intuition 1

Lecture 2: Multiple Linear Regression Intuition 2

Lecture 3: Multiple Linear Regression Intuition 3

Lecture 4: Multiple Linear Regression Intuition 4

Lecture 5: Multiple Linear Regression Intuition 5

Lecture 6: Multiple Linear Regression Intuition 6

Chapter 5: Multiple Linear Regression Model implementation in Python

Lecture 1: Multiple Linear Regression Python 1

Lecture 2: Multiple Linear Regression Python 2

Lecture 3: Multiple Linear Regression Python 3

Lecture 4: Multiple Linear Regression Python 4

Lecture 5: Multiple Linear Regression Python 5

Lecture 6: Multiple Linear Regression Python 6

Lecture 7: Multiple Linear Regression Python 7

Chapter 6: Polynomial Regression Model Intuitions

Lecture 1: Polynomial Regression Intuition 1

Chapter 7: Polynomial Regression Model implementation in Python

Lecture 1: Polynomial Regression Python 1

Lecture 2: Polynomial Regression Python 2

Lecture 3: Polynomial Regression Python 3

Chapter 8: Ridge Regression Model Intuitions

Lecture 1: Ridge Regression Intuition 1

Lecture 2: Ridge Regression Intuition 2

Chapter 9: Ridge Regression implementation in Python

Lecture 1: Ridge Regression Python 1

Lecture 2: Ridge Regression Python 2

Lecture 3: Ridge Regression Python 3

Lecture 4: Ridge Regression Python 4

Chapter 10: Lasso Regression Model Intuition

Lecture 1: Lasso Regression Intuition 1

Chapter 11: Lasso Regression implementation in Python

Lecture 1: Lasso Regression Python 1

Lecture 2: Lasso Regression Python 2

Lecture 3: Lasso Regression Python 3

Lecture 4: Lasso Regression Python 4

Chapter 12: Decision Tree Regression Model Intuition

Lecture 1: Decision Tree Regression Intuition 1

Chapter 13: Decision Tree Regression implementation in Python

Lecture 1: Decision Tree Regression Python 1

Lecture 2: Decision Tree Regression Python 2

Lecture 3: Decision Tree Regression Python 3

Lecture 4: Decision Tree Regression Python 4

Lecture 5: Decision Tree Regression Python 5

Chapter 14: Random Forest Regression Model Intuition

Lecture 1: Random Forest Regression Intuition 1

Chapter 15: Random Forest Regression implementation in Python

Lecture 1: Random Forest Regression Python 1

Lecture 2: Random Forest Regression Python 2

Lecture 3: Random Forest Regression Python 3

Chapter 16: K Nearest Neighbors Model

Lecture 1: KNN Intuition

Chapter 17: K Nearest Neighbors implementation in Python

Lecture 1: KNN Python 1

Lecture 2: KNN Python 2

Chapter 18: Logistic Regression Model

Lecture 1: Logistic Regression Intuition

Chapter 19: Logistic Regression implementation in Python

Lecture 1: Logistic Regression Python 1

Lecture 2: Logistic Regression Python 2

Lecture 3: Logistic Regression Python 3

Lecture 4: Logistic Regression Python 4

Lecture 5: Logistic Regression Python 5

Lecture 6: Logistic Regression Python 6

Chapter 20: Decision Tree Classification Model

Lecture 1: Decision Tree Classification Intuition

Lecture 2: Decision Tree Classification Intuition 2

Chapter 21: Decision Tree Classification implementation in Python

Lecture 1: Decision Tree Classification Python 1

Lecture 2: Decision Tree Classification Python 2

Lecture 3: Decision Tree Classification Python 3

Lecture 4: Decision Tree Classification Python 4

Chapter 22: Random Forest Classification Model

Lecture 1: Random Forest Classification Intuition

Chapter 23: Random Forest Classification implementation in Python

Lecture 1: Random Forest Classification Python 1

Lecture 2: Random Forest Classification Python 2

Lecture 3: Random Forest Classification Python 3

Lecture 4: Random Forest Classification Python 4

Lecture 5: Random Forest Regression Python 5

Chapter 24: The Naive Bayes Classification Model

Lecture 1: Naive Bayes Intuition 1

Lecture 2: Naive Bayes Intuition 2

Instructors

Data Science Academy
Software Engineer, Data Scientist and Entrepreneur

Rating Distribution

1 stars: 5 votes
2 stars: 2 votes
3 stars: 17 votes
4 stars: 32 votes
5 stars: 168 votes

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