Expert Trainer | Hands-on Training
duration
Overview of Artificial Intelligence, Machine Learning, and Data Science
Types of machine learning: Supervised, Unsupervised, and Reinforcement
Real-world use cases and industry applications
Python fundamentals for ML
NumPy, Pandas, and data handling
Data visualization using Matplotlib and Seaborn
Data cleaning and handling missing values
Encoding categorical data and scaling features
Feature selection and transformation techniques
Linear and Logistic Regression
Decision Trees and Random Forests
K-Nearest Neighbors (KNN)
Clustering techniques (K-Means, Hierarchical)
Dimensionality reduction (PCA)
Association rule learning basics
Train-test split and cross-validation
Performance metrics and evaluation techniques
Hyperparameter tuning
Ensemble learning techniques
Basics of Reinforcement Learning
Introduction to deep learning
End-to-end machine learning project
Case studies from industry domains
Model deployment basics
Yes, personalized mentorship is available for working professionals.