Machine Learning (ML) Professional Training in Amritsar

Definition and Scope of AI
History and Evolution of AI
Applications of AI in Real Life
Types of AI: Narrow, General, Super
Weak AI vs Strong AI
AI vs Machine Learning vs Deep Learning
Symbolic AI and Classical Approaches
Intelligent Agents
Turing Test and Rational Agents
Ethics and Social Implications of AI

Introduction to Python Programming
Data Types, Variables, and Operators
Control Structures and Loops
Functions and Lambda Expressions
Lists, Tuples, Dictionaries, Sets
File Handling in Python
Exception Handling
Modules and Packages
Object Oriented Programming
Introduction to Virtual Environments

Introduction to NumPy and Its Importance
Creating and Working with ndarrays
Array Indexing, Slicing, and Iteration
Array Operations
Mathematical and Statistical Functions
Reshaping, Stacking, and Splitting Arrays
Linear Algebra Operations (Dot Product, Matrix Inversion, Determinants)
Random Module – Generating Random Numbers
Vectorization for Performance Optimization
Real-world Examples: Image Representation, Computation Tasks

Introduction to Pandas: Series and DataFrames
Importing and Exporting Data (CSV, Excel, JSON)
Indexing, Slicing, and Subsetting Data
Data Cleaning: Handling Missing Values, Duplicates, Outliers
Data Transformation: Apply, Map, Lambda Functions
Filtering and Conditional Selection
Merging, Joining, and Concatenating DataFrames
Grouping and Aggregation (GroupBy)
Pivot Tables and Crosstabs
Time Series Handling
Basic Descriptive Statistics and EDA

Introduction to Data Visualization and Matplotlib
Basic Plots: Line, Bar, Scatter, Histogram, Pie
Customizing Plots: Titles, Labels, Legends, Colors, Styles
Multiple Plots and Subplots
Working with Figures and Axes Objects
Annotations and Texts
Plot Styling and Themes
Saving Figures in Various Formats
3D Plotting (Optional: mpl_toolkits)
Integrating Matplotlib with NumPy and Pandas

Linear Algebra Basics (Vectors, Matrices)
Matrix Multiplication & Inversion
Calculus Basics (Derivatives & Integrals)
Partial Derivatives and Chain Rule
Probability and Statistics
Descriptive vs Inferential Statistics
Mean, Variance, Standard Deviation
Bayes’ Theorem
Gradient and Gradient Descent
Eigenvalues and Eigenvectors

Understanding Structured and Unstructured Data
Data Collection Methods
Data Cleaning and Imputation
Feature Engineering
Feature Scaling (Normalization, Standardization)
Handling Missing and Outlier Values
Encoding Categorical Variables
Data Splitting: Train/Test/Validation
Data Visualization for EDA
Data Pipelines and Automation

Machine Learning Introduction
Feature Extraction
Fine Tuning Hyperparameters
Semi-Supervised
Gradient descent
Overfitting and Underfitting
Learning Curves and Model Diagnostics
Regression
GoogleColab

Introduction to Supervised Learning
Linear Regression
Logistic Regression
Decision Trees
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
Naive Bayes Classifier
Random Forests
Gradient Boosting Machines (XGBoost, LightGBM)

Introduction to Unsupervised Learning
K-Means Clustering
Hierarchical Clustering
DBSCAN
Principal Component Analysis (PCA)
t-SNE for Dimensionality Reduction
Anomaly Detection
Association Rule Mining
Recommendation Systems
Evaluation of Clustering

Basics of RL and Agents
Environment and Reward System
Markov Decision Process (MDP)
Q-Learning
Deep Q-Networks (DQN)
Policy Gradient Methods
Actor-Critic Models
Exploration vs Exploitation
OpenAI Gym Environments
Real-life Applications of RL

Introduction to Scikit-learn and Its Core Components
Data Preparation and Preprocessing
Building Supervised Learning Models
Implementing Unsupervised Learning Techniques
Evaluating Model Performance
Improving Models with Validation and Tuning
Automating Workflows with Pipelines
Saving and Loading Models for Reuse
Hands-on Mini Projects

Introduction to Robotics
Supervised Learning for Perception
Unsupervised Learning for Environment Mapping
Reinforcement Learning for Control and Navigation
Imitation Learning
Sensor Fusion using ML Models

Bias in AI Models
Explainable AI (XAI)
AI Governance and Compliance
Data Privacy and Protection
AI and Job Displacement
Fairness Metrics
Red-teaming and Safety Protocols
Adversarial Attacks
Responsible AI Development
AI Regulation (EU, India, Global)

Model Serialization (Pickle, Joblib)
REST APIs with FastAPI / Flask
Model Deployment with Docker
Model Monitoring and Logging
Version Control for Models
CI/CD for ML Pipelines
Using Streamlit and Gradio
Cloud Deployments (AWS, GCP, Azure)
MLflow and Model Registry

Understanding Evaluation Metrics for Classification
Evaluation Metrics for Regression
Confusion Matrix Analysis
Cross-Validation Techniques
Bias-Variance Tradeoff and Model Generalization
Hyperparameter Tuning
Model Comparison and Benchmarking
Handling Imbalanced Data
Ensemble Techniques for Model Selection
Model Interpretability and Trustworthiness

Defining Project Objectives
Problem Formulation
Data Collection and Cleaning
Model Selection
Training and Tuning
Evaluation and Metrics
Deployment Strategy
Feedback Loop and Maintenance
Documentation and Reporting
Case Study Development