English | MP4 | AVC 1280Ă—720 | AAC 44KHz 2ch | 77 lectures (10h 21m) | 3.46 GB

Build 8+ Practical Projects and Master Machine Learning Regression Techniques Using Python, Scikit Learn and Keras

Artificial Intelligence (AI) revolution is here! The technology is progressing at a massive scale and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries.

Machine Learning is a subfield of Artificial Intelligence that enables machines to improve at a given task with experience. Machine Learning is an extremely hot topic; the demand for experienced machine learning engineers and data scientists has been steadily growing in the past 5 years. According to a report released by Research and Markets, the global AI and machine learning technology sectors are expected to grow from $1.4B to $8.8B by 2022 and it is predicted that AI tech sector will create around 2.3 million jobs by 2020.

The purpose of this course is to provide students with knowledge of key aspects of machine learning regression techniques in a practical, easy and fun way. Regression is an important machine learning technique that works by predicting a continuous (dependant) variable based on multiple other independent variables. Regression strategies are widely used for stock market predictions, real estate trend analysis, and targeted marketing campaigns.

The course provides students with practical hands-on experience in training machine learning regression models using real-world dataset. This course covers several technique in a practical manner, including:

- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Logistic Regression
- Decision trees regression
- Ridge Regression
- Lasso Regression
- Artificial Neural Networks for Regression analysis
- Regression Key performance indicators

The course is targeted towards students wanting to gain a fundamental understanding of machine learning regression models. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master machine learning regression models and can directly apply these skills to solve real world challenging problems.

What you’ll learn

- Master Python programming and Scikit learn as applied to machine learning regression
- Understand the underlying theory behind simple and multiple linear regression techniques
- Apply simple linear regression techniques to predict product sales volume and vehicle fuel economy
- Apply multiple linear regression to predict stock prices and Universities acceptance rate
- Cover the basics and underlying theory of polynomial regression
- Apply polynomial regression to predict employeesâ€™ salary and commodity prices
- Understand the theory behind logistic regression
- Apply logistic regression to predict the probability that customer will purchase a product on Amazon using customer features
- Understand the underlying theory and mathematics behind Artificial Neural Networks
- Learn how to train network weights and biases and select the proper transfer functions
- Train Artificial Neural Networks (ANNs) using back propagation and gradient descent methods
- Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance
- Apply ANNs to predict house prices given parameters such as area, number of rooms..etc
- Assess the performance of trained Machine learning models using KPI (Key Performance indicators) such as Mean Absolute error,
- Mean squared Error, and Root Mean Squared Error intuition, R-Squared intuition, Adjusted R-Squared and F-Test
- Understand the underlying theory and intuition behind Lasso and Ridge regression techniques
- Sample real-world, practical projects

## Table of Contents

**INTRODUCTION TO THE COURSE [QUICK WIN IN FIRST 10-12 MINS]**

1 Course Welcome Message

2 Updates on Udemy Reviews

3 Course Overview

4 BONUS Learning Path

5 ML vs. DL vs. AI

6 Get the materials

**ANACONDA AND JUPYTER INSTALLATION**

7 Download and Set up Anaconda

8 What is Jupiter Notebook

**SIMPLE LINEAR REGRESSION**

9 Intro to Simple Linear Regression

10 Simple Linear Regression Intuition

11 Least Squares

12 Project #1 – Overview

13 Project #1 – Data Visualization

14 Project #1 – Divide Data into Training and Testing

15 Project #1 – Train Model

16 Project #1 – Test Model

17 Project #2 – Overview

18 Project #2 – Solution

19 Project #2 – Visualization

20 Project #2 – Prepare Training and Testing Data

21 Project #2 – Test Model

22 Project #2 – Model Testing

**REGRESSION KEY PERFORMANCE INDICATORS**

23 Regression Metrics Intro

24 Regression Metric Part 1

25 Regression Metric Part 2

26 Bias Variance Tradeoff

**POLYNOMIAL REGRESSION**

27 Polynomial Regression Intro

28 Polynomial Regression – Intuition

29 Poly Regression – Salary Load Data

30 Poly Regression – Visualize Data

31 Poly Regression – Linear Trainingtesting

32 Poly Regression – Poly Part 1

33 Poly Regression – Poly Part 2

34 Poly Regression Project 2 Overview

35 Poly Regression – Economies Linear -1

36 Poly Regression – Economies Linear -2

37 Poly Regression – Economies Poly

**MULTIPLE LINEAR REGRESSION**

38 Multiple Linear Regression Intro

39 Multiple Linear Regression Overview

40 Project #1 – Load Data and Libraries

41 Project #1 – Data Visualization

42 Project #1 – Model Training and Evaluation

43 Project #1 – Model Results Evaluation

44 Project #2 – Overview

45 Project #2 – Load Data

46 Project #2 – Data Visualization

47 Project #2 – Train the Model

48 Project #2 – Model Evaluation

49 Project #2 – Retraining Model

**LOGISTIC REGRESSION**

50 Logistic Regression Intro

51 Logistic Regression Intuition

52 Confusion Matrix

53 Project #2 – Data Import

54 Project #2 – Visualization

55 Project #2 – Data Cleaning

56 Project #2 – Training Testing

57 Model Testing Visualization

**APPLY ARTIFICIAL NEURAL NETWORKS TO PERFORM REGRESSION TASKS**

58 Artificial Neural Networks Intro

59 Theory Part 1

60 Theory Part 2

61 Theory Part 3

62 Theory Part 4

63 Theory Part 5

64 Theory Part 6

65 Project – Load Dataset

66 Project – Visualize Dataset

67 Scale the Data

68 Train the Model

69 Evaluate the Model

70 Multiple Linear regression

71 Model Improvement with more features

**LASSO AND RIDGE REGRESSION**

72 Ridge and Lasso Intro

73 Ridge Lasso Part 1

74 Ridge Lasso Part 2

75 Ridge Lasso Part 3

76 Ridge and Lasso in Practice

**Bonus Lectures**

77 YOUR SPECIAL BONUS

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