Advance Machine Learning

The objective of this program is to help participants build a deep understanding of modern machine learning algorithms, model optimization strategies, and advanced techniques used in real-world applications. The course focuses on enhancing skills in supervised and unsupervised learning, feature engineering, ensemble methods, model tuning, and deploying ML models. Participants will learn to handle complex datasets and build high-performing models suitable for industry use.

Category: Analytics

Description

1. Learning Outcomes

Understand advanced ML concepts such as ensemble methods, regularization, boosting, and bagging.
Apply feature engineering, feature selection, and dimensionality reduction techniques.
Build and evaluate advanced ML models such as Random Forest, XGBoost, SVM, and Neural Networks.
Perform hyperparameter tuning using techniques like Grid Search, Random Search, and Bayesian Optimization.
Work with imbalanced datasets using resampling strategies and cost-sensitive learning.
Deploy machine learning models using APIs or simple deployment frameworks.

2. Program Highlights & Key Features

Hands-on exercises with complex, real-world datasets.
Practical training on advanced ML algorithms and optimization techniques.
Expert-led sessions on model tuning, error analysis, and performance improvement.
Case studies covering fraud detection, churn prediction, recommendation engines, and more.
Coverage of the latest trends in ML, including AutoML, explainable AI (XAI), and model governance.

Module 1: Advanced Machine Learning Concepts
Module 2: Advanced Supervised Learning
Module 3: Advanced Unsupervised Learning
Module 4: Time Series and Sequential Models
Module 5: Model Deployment, Ethics, and Optimization

Advanced Machine Learning Concepts

Review of Machine Learning Fundamentals
Bias–Variance Tradeoff
Feature Engineering and Feature Selection
Handling Imbalanced Datasets
Model Tuning and Optimization
Conclusion and QnA

Advanced Supervised Learning

Advanced Regression Techniques
Ensemble Methods (Bagging, Boosting, Stacking)
Tree-Based Models (Random Forest, Gradient Boosting)
Model Evaluation and Cross-Validation
Hyperparameter Tuning Techniques
Conclusion and QnA

Advanced Unsupervised Learning

Advanced Clustering Techniques
Dimensionality Reduction (PCA – Conceptual)
Anomaly and Outlier Detection
Customer and Data Segmentation Use Cases
Evaluation of Unsupervised Models
Conclusion and QnA

Time Series and Sequential Models

Advanced Time Series Concepts
Forecasting Techniques
Feature Engineering for Time-Based Data
Model Evaluation for Forecasting
Business Use Cases of Time Series Models
Conclusion and QnA

Model Deployment, Ethics, and Optimization

Model Deployment Basics
Monitoring Model Performance
Handling Model Drift
Model Interpretability and Explainability
Ethical and Responsible AI
Future Scope of Advanced Machine Learning
Conclusion and QnA

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