Introduction

Scikit-learn is one of the most popular and powerful libraries in Python for machine learning and data analysis. This 5-day training course will introduce participants to the essential techniques of supervised and unsupervised learning, as well as model evaluation and hyperparameter tuning using Scikit-learn. Participants will also gain hands-on experience with implementing algorithms for classification, regression, clustering, and dimensionality reduction on real-world datasets. By the end of the course, participants will be equipped with the knowledge and practical experience to develop, optimize, and deploy machine learning models using Scikit-learn.

Objectives

By the end of this course, participants will:

1. Understand the core concepts of machine learning and its different types (supervised, unsupervised, reinforcement).
2. Get familiar with the Scikit-learn library and its data structures for machine learning.
3. Learn how to preprocess data for feature scaling, encoding, and handling missing values.
4. Implement various supervised learning algorithms for classification and regression tasks.
5. Learn unsupervised learning techniques such as clustering and dimensionality reduction.
6. Evaluate model performance using metrics, cross-validation, and model selection.
7. Tune machine learning models using hyperparameter optimization techniques.
8. Apply learned techniques to solve real-world problems and make data-driven decisions.

Who Should Attend?

• Data Scientists & Analysts looking to deepen their machine learning expertise.
• Machine Learning Engineers aiming to build real-world models with Scikit-learn.
• Business Analysts and professionals interested in applying machine learning for data-driven decision-making.
• Software Engineers who want to integrate machine learning models into applications.
• Anyone interested in learning practical machine learning with Python and Scikit-learn.

Course Outline (5 Days)

Day 1: Introduction to Machine Learning and Scikit-Learn

Morning Session

• Introduction to Machine Learning

  • Overview of machine learning types: supervised learning, unsupervised learning, and reinforcement learning.
  • The machine learning workflow: data collection, data preprocessing, modeling, evaluation, deployment.
  • Key concepts: features, labels, training vs. test data, and overfitting.
  • Hands-on: Overview of the Scikit-learn library and installation process.

• Scikit-learn Overview

  • Understanding the core data structures in Scikit-learn: Datasets, Numpy arrays, Pandas DataFrames.
  • The essential fit-transform-predict workflow in Scikit-learn.
  • Hands-on: Loading datasets (Iris, Boston Housing, etc.) and using the Scikit-learn API.

Afternoon Session

• Data Preprocessing with Scikit-learn

  • Feature scaling (Standardization, Normalization).
  • Handling missing values and encoding categorical data (One-Hot, Label Encoding).
  • Splitting data into train and test sets using train_test_split.
  • Hands-on: Preprocessing data for a classification problem (Iris dataset).

• Hands-on Exercise

  • Preprocessing a real-world dataset and preparing it for machine learning.

Day 2: Supervised Learning: Classification Algorithms

Morning Session

• Introduction to Supervised Learning

  • Understanding classification vs. regression tasks.
  • Overview of evaluation metrics for classification: accuracy, precision, recall, F1 score, confusion matrix.
  • Hands-on: Evaluating classification performance on a dataset.

• K-Nearest Neighbors (KNN)

  • Intuition behind KNN for classification.
  • How to choose the best K value using cross-validation.
  • Hands-on: Implementing KNN for classifying the Iris dataset.

• Logistic Regression

  • Understanding logistic regression for binary classification.
  • Interpreting the output of logistic regression and performance evaluation.
  • Hands-on: Building a logistic regression model on a binary classification problem.

Afternoon Session

• Decision Trees & Random Forests

  • Understanding decision trees and how they handle classification tasks.
  • Ensemble methods: Random Forests for improved classification performance.
  • Hands-on: Building and evaluating decision trees and random forests.

• Support Vector Machines (SVM)

  • Introduction to SVM for classification.
  • Choosing the appropriate kernel for SVMs.
  • Hands-on: Building an SVM model for classifying high-dimensional data.

• Hands-on Exercise

  • Building multiple classifiers and comparing their performance on a classification task (Iris or MNIST dataset).

Day 3: Supervised Learning: Regression Algorithms

Morning Session

• Introduction to Regression

  • Understanding regression tasks and evaluation metrics: mean squared error (MSE), R-squared, adjusted R-squared.
  • Difference between linear regression and logistic regression.
  • Hands-on: Building a simple linear regression model.

• Multiple Linear Regression

  • Handling multiple features in linear regression.
  • Checking for multicollinearity and overfitting.
  • Hands-on: Building a multiple linear regression model on a dataset.

• Ridge and Lasso Regression

  • Regularization techniques: Ridge (L2) and Lasso (L1) regression.
  • Importance of hyperparameter tuning in regression models.
  • Hands-on: Implementing Ridge and Lasso regression models on real-world data.

Afternoon Session

• Decision Trees for Regression

  • Introduction to decision trees for regression and handling non-linear relationships.
  • Pros and cons of decision trees in regression.
  • Hands-on: Building a decision tree regression model on a real-world dataset.

• Random Forests and Gradient Boosting Machines (GBM)

  • Overview of random forests and ensemble learning.
  • Introduction to Gradient Boosting Machines (GBM) and XGBoost.
  • Hands-on: Implementing a Random Forest and GBM model.

• Hands-on Exercise

  • Building a regression model using multiple algorithms and comparing their performance on a dataset.

Day 4: Unsupervised Learning Techniques

Morning Session

• Introduction to Unsupervised Learning

  • Overview of clustering and dimensionality reduction tasks.
  • Evaluation metrics for clustering: Silhouette Score, Adjusted Rand Index (ARI).
  • Hands-on: Clustering a dataset using K-Means and evaluating the results.

• K-Means Clustering

  • Understanding K-Means algorithm for unsupervised learning.
  • Choosing the best number of clusters using the Elbow Method.
  • Hands-on: Clustering a dataset using K-Means and interpreting the results.

• Hierarchical Clustering

  • Introduction to agglomerative and divisive hierarchical clustering.
  • Hands-on: Clustering using hierarchical methods and visualizing with dendrograms.

Afternoon Session

• Principal Component Analysis (PCA)

  • Understanding PCA for dimensionality reduction.
  • When and how to apply PCA to improve model performance.
  • Hands-on: Applying PCA to reduce dimensions in a dataset.

• t-SNE for Data Visualization

  • Introduction to t-SNE and its use in visualizing high-dimensional data.
  • Hands-on: Visualizing clustered data using t-SNE.

• Hands-on Exercise

  • Applying K-Means and PCA to explore and visualize large datasets (e.g., customer data or image features).

Day 5: Model Evaluation, Hyperparameter Tuning, and Deployment

Morning Session

• Model Evaluation Techniques

  • Introduction to cross-validation: K-fold, Stratified K-fold.
  • Grid Search and Randomized Search for hyperparameter tuning.
  • Hands-on: Implementing cross-validation and hyperparameter tuning with Scikit-learn.

• Hyperparameter Optimization

  • Grid Search and Randomized Search for tuning models in Scikit-learn.
  • Using Pipeline to streamline model evaluation and hyperparameter tuning.
  • Hands-on: Tuning hyperparameters of a Random Forest and SVM.

Afternoon Session

• Deploying Machine Learning Models

  • Introduction to model deployment using Flask and Django.
  • Creating APIs for real-time predictions and integrating models into web applications.
  • Hands-on: Deploying a trained machine learning model using Flask.

• Capstone Project & Final Presentations

  • Choose from:
    1. Building a complete machine learning pipeline for classification
    2. Deploying a regression model into a web app
    3. Applying unsupervised learning for customer segmentation
  • Participants present their projects and receive expert feedback.

• Certification & Networking Session

Post-Course Benefits

• Hands-on experience with Scikit-learn for machine learning tasks.
• Advanced skills in supervised and unsupervised machine learning algorithms.
• Proficiency in model evaluation, hyperparameter tuning, and deployment techniques.
• Portfolio-ready projects showcasing your ability to build and deploy machine learning models.
• Networking opportunities with peers and instructors.