Machine Learning With scikit-learn
Use python to build machine learning models for regression and classification using scikit-learn. Learn how to create, train and evaluate models. Be familiar with the common machine learnign algorithms available from scikit-learn.
Prerequisites
- Intermediate knowledge of Python and common packages (pandas, numpy).
- Familiarity with programming environments, especially Jupyter notebooks.
Contents
Introduction to Machine Learning, Deep Learning, and Generative AI.
- Overview of machine learning concepts.
- Introduction to supervised, unsupervised, and reinforcement learning.
Data Pre-processing.
- Techniques for cleaning and preparing data for machine learning.
- Missing Values, Imputers, Formatting
- One Hot encoding
- Feature Scaling
- The scikit-learn object model.
Regression Analysis.
- Understanding regression analysis and its applications.
- Building regression models using Python
- Simple Linear
- Multiple Linear
- Polynomial
- Model Optimization
Classification.
- Introduction to classification models.
- Logistic Regression
- KNN Classification
- Multi Class Classification
Clustering.
- Overview of clustering techniques.
- KNN Clustering
- Divisive and Agglomerative Clustering
- Clustering to perform image compression
Rule Association Learning.
- Implementing rule association learning for pattern discovery.
- Apriori rule association
- Eclat rule association
Dimensionality Reduction.
- Know how to use Dimensionality Reduction as a tool for feature engineering
- Principal Component Analysis
- Latent Discriminative Analysis
- Kernel Principal Component Analysis
