Learn Python programming and Scikit-Learn applied to machine learning regression in this comprehensive guide for beginners
About This Video
Learn Python programming and Scikit-Learn applied to machine learning regression in this comprehensive guide for beginners
About This Video
In Detail
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.
Audience
This course is for anyone interested in pursuing a career in machine learning, as well as Python programmers who want to add machine learning skills to their resume. This course will also benefit technologists who want to learn more about how machine learning works in the real world. This course requires familiarity with the fundamentals of Python, as well as readiness, flexibility, a will to learn, and, most importantly, basic mathematical skills.
Chapter 1 : Introduction to Machine Learning
What Is Machine Learning?
Applications of Machine Learning
Machine Learning Methods
What Is Supervised Learning?
What Is Unsupervised Learning?
Supervised Learning Versus Unsupervised Learning
Chapter 2 : Optional: Setting Up Python and ML Algorithms Implementation
Introduction
Python Libraries for Machine Learning
Setting Up Python
What Is Jupyter?
Anaconda Installation Windows Mac and Ubuntu
Implementing Python in Jupyter
Managing Directories in Jupyter Notebook
Chapter 3 : Simple Linear Regression
Introduction to Regression
How Does Linear Regression Work?
Line Representation
Implementation in Python: Importing Libraries and Datasets
Implementation in Python: Distribution of the Data
Implementation in Python: Creating a Linear Regression Object
Chapter 4 : 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
Chapter 5 : 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
Chapter 6 : 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
Chapter 7 : 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
Chapter 8 : 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
Chapter 9 : 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
Chapter 10 : Conclusion
Thank You