Practical Machine Learning (Scikit-learn + Real-world Projects)

About Course

This module is designed to bridge the gap between theoretical knowledge and practical application of Machine Learning. Focusing on the use of Scikit-learn, a leading Python library for machine learning, this course will help you implement real-world machine learning models. You’ll get hands-on experience working with real datasets and building production-level models.

Through a series of real-world projects and case studies, you’ll apply the algorithms and techniques you’ve learned to solve business problems such as customer segmentation, predictive modeling, classification, and more. This module is ideal for those who want to dive deeper into applying machine learning tools to solve industry-specific challenges.

Hands-on Machine Learning Projects: Implement real-world projects to get a feel of how machine learning models work in practice.
Using Scikit-learn for ML Implementation: Learn how to effectively use the Scikit-learn library to implement and evaluate machine learning models.
Data Preprocessing & Cleaning: Learn techniques like handling missing data, normalization, encoding categorical variables, and splitting data for training and testing.
Working with Imbalanced Data: Techniques for handling class imbalance using resampling methods and model evaluation adjustments.
Building End-to-End Machine Learning Pipelines: Automate workflows using Scikit-learn’s tools to create machine learning pipelines from raw data to prediction.
Model Evaluation and Hyperparameter Tuning: Improve your models with methods such as cross-validation, grid search, and random search.
Model Deployment and Scaling: Learn the basics of deploying machine learning models to production, either locally or in the cloud.

Course Content

Introduction to Scikit-learn

Overview of Scikit-learn and its features
Installing and setting up Scikit-learn in your Python environment
Essential modules and functions in Scikit-learn

Data Preprocessing and Cleaning

Supervised Learning Algorithms in Scikit-learn

Unsupervised Learning Algorithms in Scikit-learn

Model Evaluation and Optimization

Real-World Projects

End-to-End Pipeline Creation

Model Deployment & Productionalization

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About Course

What Will You Learn?

Course Content

Introduction to Scikit-learn

Overview of Scikit-learn and its features

Installing and setting up Scikit-learn in your Python environment

Essential modules and functions in Scikit-learn

Data Preprocessing and Cleaning

Handling missing values and imputation techniques

Encoding categorical features and normalizing/standardizing data

Splitting datasets for training and testing

Feature selection and dimensionality reduction

Supervised Learning Algorithms in Scikit-learn

Linear Regression: Building regression models using Scikit-learn

Logistic Regression: Classification problems, and evaluating binary classifiers

Decision Trees: Training decision trees for both regression and classification tasks

Random Forests: Building ensemble models to improve predictive accuracy

Support Vector Machines (SVM): Implementing and tuning SVMs for both classification and regression

Unsupervised Learning Algorithms in Scikit-learn

K-Means Clustering: Clustering data and finding hidden patterns

Hierarchical Clustering: Agglomerative clustering methods for unsupervised learning

Principal Component Analysis (PCA): Dimensionality reduction techniques to simplify complex datasets

Model Evaluation and Optimization

Model evaluation using accuracy, precision, recall, and F1 score

Cross-validation: Ensuring models generalize well

Hyperparameter tuning: Using grid search and random search to optimize models

Real-World Projects

Project 1: Predicting House Prices: Build a regression model to predict house prices using a real-world dataset.

Project 2: Customer Churn Prediction: Create a classification model to predict customer churn based on historical data.

Project 3: Handwritten Digit Classification: Implement a deep learning-based solution to classify digits using the MNIST dataset.

Project 4: Fraud Detection: Develop a model to detect fraudulent transactions in a credit card transaction dataset.

Project 5: Sales Forecasting: Create a model to forecast sales based on various features like seasonality, marketing budget, and historical data.

End-to-End Pipeline Creation

Implementing Scikit-learn pipelines: Automating the data preparation, model training, and evaluation processes

Deploying models: Introduction to deploying models using Flask or FastAPI

Model Deployment & Productionalization

Deploying models as APIs for production use

Introduction to cloud deployment: Deploying machine learning models to the cloud (AWS, Google Cloud, etc.)

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