Python for Machine Learning: The Complete Beginner's Course

Table of contents

1. Introduction to Machine Learning
   1. What is Machine Learning?
   2. Applications of Machine Learning
   3. Machine learning Methods
   4. What is Supervised learning?
   5. What is Unsupervised learning?
   6. Supervised learning vs Unsupervised learning
2. Implementing ML Algorithms in Python
   1. Introduction
   2. Python libraries for Machine Learning
   3. Setting up Python
   4. What is Jupyter?
   5. Anaconda Installation Windows Mac and Ubuntu
   6. Implementing Python in Jupyter
   7. Managing Directories in Jupyter Notebook
3. Simple Linear Regression
   1. Introduction to regression
   2. How Does Linear Regression Work?
   3. Line representation
   4. Implementation in python: Importing libraries datasets
   5. Implementation in python: Distribution of the data
   6. Implementation in python: Creating a linear regression object
4. Multiple Linear Regression
   1. Understanding Multiple linear regression
   2. Implementation in python: Exploring the dataset
   3. Implementation in python: Encoding Categorical Data
   4. Implementation in python: Splitting data into Train and Test Sets
   5. Implementation in python: Training the model on the Training set
   6. Implementation in python: Predicting the Test Set results
   7. Evaluating the performance of the regression model
   8. Root Mean Squared Error in Python
5. Classification Algorithms: K-Nearest Neighbors
   1. Introduction to classification
   2. K-Nearest Neighbors algorithm
   3. Example of KNN
   4. K-Nearest Neighbours (KNN) using python
   5. Implementation in python: Importing required libraries
   6. Implementation in python: Importing the dataset
   7. Implementation in python: Splitting data into Train and Test Sets
   8. Implementation in python: Feature Scaling
   9. Implementation in python: Importing the KNN classifier
   10. Implementation in python: Results prediction Confusion matrix
6. Classification Algorithms: Decision Tree
   1. Introduction to decision trees
   2. What is Entropy?
   3. Exploring the dataset
   4. Decision tree structure
   5. Implementation in python: Importing libraries datasets
   6. Implementation in python: Encoding Categorical Data
   7. Implementation in python: Splitting data into Train and Test Sets
   8. Implementation in python: Results prediction Accuracy
7. Classification Algorithms: Logistic regression
   1. Introduction
   2. Implementation steps
   3. Implementation in python: Importing libraries datasets
   4. Implementation in python: Splitting data into Train and Test Sets
   5. Implementation in python: Pre-processing
   6. Implementation in python: Training the model
   7. Implementation in python: Results prediction Confusion matrix
   8. Logistic Regression vs Linear Regression
8. Clustering
   1. Introduction to clustering
   2. Use cases
   3. K-Means Clustering Algorithm
   4. Elbow method
   5. Steps of the Elbow method
   6. Implementation in python
   7. Hierarchical clustering
   8. Density-based clustering
   9. Implementation of k-means clustering in python
   10. Importing the dataset
   11. Visualizing the dataset
   12. Defining the classifier
   13. 3D Visualization of the clusters
   14. 3D Visualization of the predicted values
   15. Number of predicted clusters
9. Recommender System
   1. Introduction
   2. Collaborative Filtering in Recommender Systems
   3. Content-based Recommender System
   4. Implementation in python: Importing libraries datasets
   5. Merging datasets into one dataframe
   6. Sorting by title and rating
   7. Histogram showing number of ratings
   8. Frequency distribution
   9. Jointplot of the ratings and number of ratings
   10. Data pre-processing
   11. Sorting the most-rated movies
   12. Grab the ratings for two movies
   13. Correlation between the most-rated movies
   14. Sorting the data by correlation
   15. Filtering out movies
   16. Sorting values
   17. Repeating the process for another movie
10. Conclusion
    1. Conclusion