Machine Learning

Description

This Machine Learning training course teaches you the foundations of building models that learn from data to make predictions and uncover hidden patterns. You will master supervised, unsupervised, and reinforcement learning, work with scikit-learn, train and evaluate models, perform feature engineering, and deploy machine learning solutions for real business problems. Perfect for aspiring data scientists, engineers, and analysts.

Course Content

Module 1: Introduction to Machine Learning

What is machine learning
The ML workflow from data to deployment
Tools and platforms you 'll use

Module 2: Math and Statistics Foundations

Linear algebra essentials
Probability and statistics
Calculus for ML
Why math matters for practitioners

Module 3: Supervised Learning

Linear and logistic regression
Decision trees and random forests
Support vector machines
Gradient boosting: XGBoost, LightGBM

Module 4: Unsupervised Learning

Clustering: K-means, hierarchical, DBSCAN
Dimensionality reduction: PCA, t-SNE
Anomaly detection
Association rules and market basket analysis

Module 5: Feature Engineering

Data cleaning and preprocessing
Handling missing values
Feature selection and creation
Encoding categorical and text data

Module 6: Model Evaluation and Tuning

Train/test/validation splits
Cross-validation techniques
Hyperparameter tuning
Avoiding overfitting and underfitting

Module 7: Deep Learning Introduction

Neural networks fundamentals
Backpropagation and gradient descent
When to choose deep learning
Practical examples with PyTorch

Module 8: ML in Production

Deploying ML models to production
Monitoring and maintenance
ML ethics and fairness
Build an end-to-end ML project

Duration: 8 – 12 weeks

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