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Python for Machine Learning: The Complete Beginner's Course
Section 01: Introduction to Machine Learning
Applications of Machine Learning (1:48)
What is Machine Learning? (1:53)
Machine learning Methods (0:29)
What is Supervised learning? (1:18)
What is Unsupervised learning? (0:59)
Supervised learning vs Unsupervised learning (3:36)
Section 02: Setting Up Python & ML Algorithms Implementation
Python Libraries for Machine Learning (1:44)
Introduction S2 (0:53)
Setting up Python (2:25)
What is Jupyter? (1:33)
Anaconda Installation Windows Mac and Ubuntu (4:16)
Implementing Python in Jupyter (0:45)
Managing Directories in Jupyter Notebook (2:48)
Section 03: Simple Linear Regression
How Does Linear Regression Work? (1:36)
Introduction to regression (1:41)
Line representation (0:58)
Implementation in Python: Importing libraries & datasets (1:48)
Implementation in Python: Distribution of the data (2:19)
Implementation in Python: Creating a linear regression object (2:56)
Section 04: Multiple Linear Regression
Implementation in Python: Exploring the dataset (3:53)
Understanding Multiple linear regression (1:34)
Implementation in Python: Encoding Categorical Data (4:47)
Implementation in Python: Splitting data into Train and Test Sets (1:48)
Implementation in Python: Training the model on the Training set (1:23)
Implementation in Python: Predicting the Test Set results (2:59)
Evaluating the performance of the regression model (1:20)
Root Mean Squared Error in Python (2:30)
Section 05: Classification Algorithms: K-Nearest Neighbors
Introduction to classification (1:05)
K-Nearest Neighbors algorithm (0:55)
Example of KNN (0:30)
K-Nearest Neighbours (KNN) using python (1:15)
Implementation in Python: Importing required libraries (0:51)
Implementation in Python: Importing the dataset (1:35)
Implementation in Python: Splitting data into Train and Test Sets (3:16)
Implementation in Python: Feature Scaling (0:26)
Implementation in Python: Importing the KNN classifier (2:05)
Implementation in Python: Results prediction & Confusion matrix (1:32)
Section 06: Classification Algorithms: Decision Tree
What is Entropy? (1:17)
Introduction to decision trees (1:23)
Exploring the dataset (0:36)
Decision tree structure (1:16)
Implementation in Python: Importing libraries & datasets (0:48)
Implementation in Python: Encoding Categorical Data (2:50)
Implementation in Python: Splitting data into Train and Test Sets (1:06)
Implementation in Python: Results Prediction & Accuracy (2:37)
Section 07: Classification Algorithms: Logistic regression
Implementation steps (0:52)
Introduction S7 (1:25)
Implementation in Python: Importing libraries & datasets (2:01)
Implementation in Python: Splitting data into Train and Test Sets (1:29)
Implementation in Python: Pre-processing (2:00)
Implementation in Python: Training the model (1:05)
Implementation in Python: Results prediction & Confusion matrix (2:23)
Logistic Regression vs Linear Regression (2:26)
Section 08: Clustering
Introduction to clustering (0:53)
Use cases (0:59)
K-Means Clustering Algorithm (1:26)
Elbow method (1:35)
Steps of the Elbow method (1:11)
Implementation in python (4:15)
Hierarchical clustering (1:17)
Density-based clustering (1:35)
Implementation of k-means clustering in Python (1:03)
Importing the dataset (3:06)
Visualizing the dataset (2:20)
Defining the classifier (1:37)
3D Visualization of the clusters (1:19)
Number of predicted clusters (2:51)
Section 09: Recommender System
Introduction S9 (1:28)
Collaborative Filtering in Recommender Systems (0:42)
Content-based Recommender System (0:51)
Implementation in Python: Importing libraries & datasets (2:57)
Merging datasets into one dataframe (0:53)
Sorting by title and rating (3:40)
Histogram showing number of ratings (0:50)
Frequency distribution (1:04)
Jointplot of the ratings and number of ratings (1:17)
Data pre-processing (2:04)
Sorting the most-rated movies (1:00)
Grabbing the ratings for two movies (1:25)
Correlation between the most-rated movies (2:15)
Sorting the data by correlation (0:54)
Filtering out movies (0:41)
Sorting values (1:02)
Repeating the process for another movie (2:23)
Section 10: Conclusion
Conclusion (0:22)
Correlation between the most-rated movies
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