Python for Machine Learning: The Complete Beginner's Course

Course Curriculum

Chapter 1: Introduction to Machine Learning

Lecture 1: What is Machine Learning?

Lecture 2: Applications of Machine Learning

Lecture 3: Machine learning Methods

Lecture 4: What is Supervised learning?

Lecture 5: What is Unsupervised learning?

Lecture 6: Supervised learning vs Unsupervised learning

Lecture 7: Course Materials

Chapter 2: Optional: Setting Up Python & ML Algorithms Implementation

Lecture 1: Introduction

Lecture 2: Python libraries for Machine Learning

Lecture 3: Setting up Python

Lecture 4: What is Jupyter?

Lecture 5: Anaconda Installation Windows Mac and Ubuntu

Lecture 6: Implementing Python in Jupyter

Lecture 7: Managing Directories in Jupyter Notebook

Chapter 3: Simple Linear Regression

Lecture 1: Introduction to regression

Lecture 2: How Does Linear Regression Work?

Lecture 3: Line representation

Lecture 4: Implementation in python: Importing libraries & datasets

Lecture 5: Implementation in python: Distribution of the data

Lecture 6: Implementation in python: Creating a linear regression object

Chapter 4: Multiple Linear Regression

Lecture 1: Understanding Multiple linear regression

Lecture 2: Implementation in python: Exploring the dataset

Lecture 3: Implementation in python: Encoding Categorical Data

Lecture 4: Implementation in python: Splitting data into Train and Test Sets

Lecture 5: Implementation in python: Training the model on the Training set

Lecture 6: Implementation in python: Predicting the Test Set results

Lecture 7: Evaluating the performance of the regression model

Lecture 8: Root Mean Squared Error in Python

Chapter 5: Classification Algorithms: K-Nearest Neighbors

Lecture 1: Introduction to classification

Lecture 2: K-Nearest Neighbors algorithm

Lecture 3: Example of KNN

Lecture 4: K-Nearest Neighbours (KNN) using python

Lecture 5: Implementation in python: Importing required libraries

Lecture 6: Implementation in python: Importing the dataset

Lecture 7: Implementation in python: Splitting data into Train and Test Sets

Lecture 8: Implementation in python: Feature Scaling

Lecture 9: Implementation in python: Importing the KNN classifier

Lecture 10: Implementation in python: Results prediction & Confusion matrix

Chapter 6: Classification Algorithms: Decision Tree

Lecture 1: Introduction to decision trees

Lecture 2: What is Entropy?

Lecture 3: Exploring the dataset

Lecture 4: Decision tree structure

Lecture 5: Implementation in python: Importing libraries & datasets

Lecture 6: Implementation in python: Encoding Categorical Data

Lecture 7: Implementation in python: Splitting data into Train and Test Sets

Lecture 8: Implementation in python: Results prediction & Accuracy

Chapter 7: Classification Algorithms: Logistic regression

Lecture 1: Introduction

Lecture 2: Implementation steps

Lecture 3: Implementation in python: Importing libraries & datasets

Lecture 4: Implementation in python: Splitting data into Train and Test Sets

Lecture 5: Implementation in python: Pre-processing

Lecture 6: Implementation in python: Training the model

Lecture 7: Implementation in python: Results prediction & Confusion matrix

Lecture 8: Logistic Regression vs Linear Regression

Chapter 8: Clustering

Lecture 1: Introduction to clustering

Lecture 2: Use cases

Lecture 3: K-Means Clustering Algorithm

Lecture 4: Elbow method

Lecture 5: Steps of the Elbow method

Lecture 6: Implementation in python

Lecture 7: Hierarchical clustering

Lecture 8: Density-based clustering

Lecture 9: Implementation of k-means clustering in python

Lecture 10: Importing the dataset

Lecture 11: Visualizing the dataset

Lecture 12: Defining the classifier

Lecture 13: 3D Visualization of the clusters

Lecture 14: 3D Visualization of the predicted values

Lecture 15: Number of predicted clusters

Chapter 9: Recommender System

Lecture 1: Introduction

Lecture 2: Collaborative Filtering in Recommender Systems

Lecture 3: Content-based Recommender System

Lecture 4: Implementation in python: Importing libraries & datasets

Lecture 5: Merging datasets into one dataframe

Lecture 6: Sorting by title and rating

Lecture 7: Histogram showing number of ratings

Lecture 8: Frequency distribution

Lecture 9: Jointplot of the ratings and number of ratings

Lecture 10: Data pre-processing

Lecture 11: Sorting the most-rated movies

Lecture 12: Grabbing the ratings for two movies

Lecture 13: Correlation between the most-rated movies

Lecture 14: Sorting the data by correlation

Lecture 15: Filtering out movies

Lecture 16: Sorting values

Lecture 17: Repeating the process for another movie

Chapter 10: Conclusion

Lecture 1: Conclusion

Chapter 11: BONUS Section – Don't Miss Out

Lecture 1: BONUS Section – Don't Miss Out