Machine Learning in Python for Absolute Beginners

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1. Course Introduction

1. Course Introduction.mp4

02:18

2. Introduction to Machine Learning with Python

1. Section Introduction.mp4

00:59

2. What is Machine Learning.mp4

03:57

3. Types of Machine Learning.mp4

12:32

4. Applications of Machine Learning.mp4

08:45

5. Setting up the dev environment.mp4

01:41

6. Summary.mp4

03:00

7. Quiz.html

3. Data Preparation

1. Section Introduction.mp4

01:06

2. Loading data sets.mp4

02:55

3. Preprocessing text data.mp4

04:24

4. Data Cleaning.mp4

04:07

5. Handling the Missing Data.mp4

03:03

6. Handling the Noisy Data.mp4

03:08

7. Data Transformation.mp4

03:17

8. Data Reduction.mp4

03:36

9. Data Integration.mp4

02:07

10. Summary.mp4

01:48

11. Quiz.html

4. Evaluation and tuning of Classification Models

1.1 16215574.zip

1. Section Introduction.mp4

00:47

2. Introduction to Validation Techniques.mp4

02:22

3. Resubstitution and Hold-out.mp4

02:48

4. K-fold Cross-Validation.mp4

02:54

5. Leave-One-Out-Cross-Validation.mp4

03:00

6. Random Sub- Sampling and Bootstrapping.mp4

01:35

7. Bias.mp4

01:54

8. Variance.mp4

01:31

9. Underfitting and Overfitting.mp4

08:58

10. Hyperparameter tuning.mp4

03:23

11. Implementing Hyperparameter Tuning.mp4

10:33

12. Visualizing model results.mp4

02:09

13. Summary.mp4

01:07

14. Quiz.html

5. Supervised Learning 1 - Regression

1.1 16215575.zip

1. Section Introduction.mp4

00:32

2. Introduction to Linear regression.mp4

02:51

3. Model evaluation and interpretation of results.mp4

02:10

4. Important Metrics.mp4

05:53

5. Confusion Matrix.mp4

00:58

6. Multiple Linear regression.mp4

03:17

7. Non linear regression.mp4

01:24

8. Regression on Iris Data Set.mp4

04:19

9. Summary.mp4

01:32

10. Quiz.html

6. Supervised Learning 2- Classification

1.1 16215576.zip

1. Section 5 Introduction.mp4

00:41

2. Introduction to Classification algorithms - Part 1.mp4

04:38

3. Introduction to Classification algorithms - Part 2.mp4

04:22

4. Different types of Classification Algorithms.mp4

05:56

5. Coding up a simple classification model using Decision Trees.mp4

05:00

6. Coding up a simple classification model using Naive Bayes.mp4

01:59

7. Summary.mp4

00:52

8. Quiz.html

7. Supervised Learning 3- Classification

1.1 16215577.zip

1. Section Introduction.mp4

00:38

2. Introduction to Logistic Regression - Logistic vs. Linear Regression.mp4

03:09

3. Introduction to Random Forest models.mp4

02:50

4. Coding up a simple classification model using random forest.mp4

04:13

5. Coding up a simple classification model using logistic regression.mp4

01:31

6. Summary.mp4

00:34

7. Quiz.html

8. Supervised Learning 4 - Classification

1.1 16215581.zip

1. Section Introduction.mp4

00:41

2. Introduction to K-nearest neighbors.mp4

02:26

3. Introduction to SVM.mp4

04:38

4. Coding up models using k-nearest neighbors.mp4

04:20

5. Coding up models using SVM.mp4

01:36

6. Summary.mp4

00:55

7. Quiz.html

8.1 16215580.zip

8. Case studies from real world companies -Part 1.mp4

00:35

9. Case studies from real world companies -Part 2.mp4

06:51

10. Credit Card Fraud Case.mp4

01:07

11. Traffic prediction using machine learning.mp4

05:54

12. Customer Behavior Analysis.mp4

04:36

13. Fake news detection.mp4

05:09

14. Summary.mp4

00:54

Description

Learn the fundamentals of machine learning from ground up

What You'll Learn?

Learn the basics of python programming language for machine learning
Learn to build machine learning models from scratch
Learn to build classification and regression solutions from ground up
Work on multiple real world projects

Who is this for?

Any one who wants to start learning machine learning will find this course very useful

What You Need to Know?

Basic programming knowledge is must for taking the course

More details

Description
Do You Want To Know How Machine Learning Algorithms Are Being Implemented In Python?
In this course, you'll learn about machine learning and how to utilize python for building reliable and efficient machine learning models to find solutions for real-life problems. We will be covering aspects like preparing data sets to train the machine learning models and setting up a python environment on your desktops and laptops. Also, you'll learn how to utilize these libraries to evaluate and fine-tune your machine learning models.
This beginner program will help anyone who wants to quickly start working on machine learning solutions.Â This program will teach the concepts using real-world problems.
Let's Have A Look At The Major Topics We'll Be Covering In This Course!
Introduction to Machine Learning with Python
Data Preparation
Evaluation and tuning of Classification Models
Supervised LearningÂ - Regression and Classification

In this course, we'll take you through the topics of supervised learning and unsupervised learning. Also, you'll learn about the different algorithms like regression, naive Bayes, decision trees, logistic regression, random forest, KNN, and Support Vector Machines (SVM).
You'll be learning how to implement the following steps to successfully build machine learning models using Python
Installing the Python and libraries
Loading the dataset
Summarizing the dataset
Visualizing the dataset
Evaluating some algorithms
Making some predictions
Enroll today and learn the most in-demand skills of Python and machine learning
See You In The Class!
Who this course is for:
Any one who wants to start learning machine learning will find this course very useful

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