Python for Machine Learning - The Complete Beginner's Course [Video]
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-
Free ChapterIntroduction to Machine Learning
-
Optional: Setting Up Python and ML Algorithms Implementation
-
Simple Linear Regression
-
Multiple Linear Regression
- Understanding Multiple Linear Regression
- Implementation in Python: Exploring the Dataset
- Implementation in Python: Encoding Categorical Data
- Implementation in Python: Splitting Data into Train and Test Sets
- Implementation in Python: Training the Model on the Training Set
- Implementation in Python: Predicting the Test Set Results
- Evaluating the Performance of the Regression Model
- Root Mean Squared Error in Python
-
Classification Algorithms: K-Nearest Neighbors
- Introduction to Classification
- K-Nearest Neighbors Algorithm
- Example of KNN
- K-Nearest Neighbors (KNN) Using Python
- Implementation in Python: Importing Required Libraries
- Implementation in Python: Importing the Dataset
- Implementation in Python: Splitting Data into Train and Test Sets
- Implementation in Python: Feature Scaling
- Implementation in Python: Importing the KNN Classifier
- Implementation in Python: Results Prediction and Confusion Matrix
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Classification Algorithms: Decision Tree
- Introduction to Decision Trees
- What Is Entropy?
- Exploring the Dataset
- Decision Tree Structure
- Implementation in Python: Importing Libraries and Datasets
- Implementation in Python: Encoding Categorical Data
- Implementation in Python: Splitting Data into Train and Test Sets
- Implementation in Python: Results Prediction and Accuracy
-
Classification Algorithms: Logistic Regression
- Introduction
- Implementation Steps
- Implementation in Python: Importing Libraries and Datasets
- Implementation in Python: Splitting Data into Train and Test Sets
- Implementation in Python: Pre-Processing
- Implementation in Python: Training the Model
- Implementation in Python: Results Prediction and Confusion Matrix
- Logistic Regression Versus Linear Regression
-
Clustering
- Introduction to Clustering
- Use Cases
- K-Means Clustering Algorithm
- Elbow Method
- Steps of the Elbow Method
- Implementation in Python
- Hierarchical Clustering
- Density-Based Clustering
- Implementation of K-Means Clustering in Python
- Importing the Dataset
- Visualizing the Dataset
- Defining the Classifier
- 3D Visualization of the Clusters
- 3D Visualization of the Predicted Values
- Number of Predicted Clusters
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Recommender System
- Introduction
- Collaborative Filtering in Recommender Systems
- Content-Based Recommender System
- Implementation in Python: Importing Libraries and Datasets
- Merging Datasets into One Dataframe
- Sorting by Title and Rating
- Histogram Showing Number of Ratings
- Frequency Distribution
- Jointplot of the Ratings and Number of Ratings
- Data Pre-Processing
- Sorting the Most-Rated Movies
- Grabbing the Ratings for Two Movies
- Correlation Between the Most-Rated Movies
- Sorting the Data by Correlation
- Filtering Out Movies
- Sorting Values
- Repeating the Process for Another Movie
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Conclusion
About this
video
Machine learning is a branch of computer science in which you can use mathematical input to develop complicated models that fulfil various roles. Python is a popular choice for building machine learning models because of the large number of libraries available. This course will walk you through an astonishing combination of Python and machine learning, teaching you the fundamentals of machine learning so you can construct your own projects.
We will begin by studying Python programming and applying Scikit-Learn to machine learning regression in this course. After that, we will look at the theory underpinning simple and multiple linear regression algorithms. Following that, we will look at how to solve linear and logistic regression issues. Later, we will use sklearn to learn both the theory and the actual application of logistic regression. We will also go into the math underpinning decision trees. Finally, you will learn about the various clustering algorithms.
By the end of this course, you will be able to use these algorithms in the real world.
The code bundle for this course is available at: https://github.com/PacktPublishing/Python-for-Machine-Learning---The-Complete-Beginner-s-Course
- Publication date:
- September 2022
- Publisher
- Packt
- Duration
- 2 hours 27 minutes
- ISBN
- 9781804619308