Machine Learning in Python for Absolute Beginners

سرفصل های دوره

Learn the fundamentals of machine learning from ground up

1. Course Introduction

1. Course Introduction

2. Introduction to Machine Learning with Python

1. Section Introduction

2. What is Machine Learning

3. Types of Machine Learning

4. Applications of Machine Learning

5. Setting up the dev environment

6. Summary

7. Quiz.html

3. Data Preparation

1. Section Introduction

2. Loading data sets

3. Preprocessing text data

4. Data Cleaning

5. Handling the Missing Data

6. Handling the Noisy Data

7. Data Transformation

8. Data Reduction

9. Data Integration

10. Summary

11. Quiz.html

4. Evaluation and tuning of Classification Models

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1. Section Introduction

2. Introduction to Validation Techniques

3. Resubstitution and Hold-out

4. K-fold Cross-Validation

5. Leave-One-Out-Cross-Validation

6. Random Sub- Sampling and Bootstrapping

7. Bias

8. Variance

9. Underfitting and Overfitting

10. Hyperparameter tuning

11. Implementing Hyperparameter Tuning

12. Visualizing model results

13. Summary

14. Quiz.html

5. Supervised Learning 1 - Regression

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1. Section Introduction

2. Introduction to Linear regression

3. Model evaluation and interpretation of results

4. Important Metrics

5. Confusion Matrix

6. Multiple Linear regression

7. Non linear regression

8. Regression on Iris Data Set

9. Summary

10. Quiz.html

6. Supervised Learning 2- Classification

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1. Section 5 Introduction

2. Introduction to Classification algorithms - Part 1

3. Introduction to Classification algorithms - Part 2

4. Different types of Classification Algorithms

5. Coding up a simple classification model using Decision Trees

6. Coding up a simple classification model using Naive Bayes

7. Summary

8. Quiz.html

7. Supervised Learning 3- Classification

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1. Section Introduction

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

3. Introduction to Random Forest models

4. Coding up a simple classification model using random forest

5. Coding up a simple classification model using logistic regression

6. Summary

7. Quiz.html

8. Supervised Learning 4 - Classification

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1. Section Introduction

2. Introduction to K-nearest neighbors

3. Introduction to SVM

4. Coding up models using k-nearest neighbors

5. Coding up models using SVM

6. Summary

7. Quiz.html

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8. Case studies from real world companies -Part 1

9. Case studies from real world companies -Part 2

10. Credit Card Fraud Case

11. Traffic prediction using machine learning

12. Customer Behavior Analysis

13. Fake news detection

14. Summary

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تولید کننده: Udemy-Training