To be Scheduled

Machine Learning

Gain practical skills in supervised and unsupervised machine learning, feature engineering, and model evaluation to solve real-world problems using Python and modern ML libraries.

To be Scheduled_Weekends Only

Statistics & Advanced Analytics

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About this Training

Machine Learning is a practical course designed to equip learners with the skills to build intelligent systems that learn from data and make predictions. Participants will explore core concepts, algorithms, and techniques in machine learning while gaining hands-on experience with Python and libraries such as scikit-learn. Using real-world datasets and projects, they will learn to preprocess data, train models, evaluate performance, and deploy solutions that address real challenges in business, research, and technology.

What You'll Learn

Module 1: Introduction to Machine Learning – Concepts applications and types of ML
Module 2: Data Preprocessing & Feature Engineering – Handling missing data scaling and feature selection
Module 3: Supervised Learning – Regression – Building and evaluating regression models
Module 4: Supervised Learning – Classification – Algorithms for binary and multi-class classification
Module 5: Unsupervised Learning – Clustering dimensionality reduction and anomaly detection
Module 6: Ensemble Methods – Boosting bagging and random forests
Module 7: Model Evaluation & Optimization – Metrics cross-validation and hyperparameter tuning
Module 8: Real-World Project – End-to-end ML project from data to deployment

This training includes:

Hands on coding sessions with Python and scikit-learn

Practical exercises on regression and classification

Clustering and unsupervised learning projects

Feature engineering and preprocessing techniques

Hyperparameter tuning and model optimization

Evaluation frameworks and metrics for ML models

A capstone project applying ML to real-world data

Skills you'll gain:

Supervised and unsupervised learning

Feature engineering and preprocessing

Regression and classification modeling

Clustering and dimensionality reduction

Model evaluation and performance metrics

Using Python libraries such as scikit-learn Pandas NumPy Matplotlib

End-to-end ML project implementation