Artificial Intelligence, Machine Learning and Data Science Training Program

Module 1: Python for AI/ML

Python Programming

• Introduction to Python Programming
• Variables & Data Types
• Operators
• Data Structures
• Functions
• Conditional Flow statements
• Lambda, Map, Filter & Reduce functions
• Types of Errors & Error Handling

Python for Data Science

• Mathematical Computing using NumPy (creating 1d, 2d and 3d arrays)
• Accessing Array Elements, Indexing, Slicing, Iteration
• Data Analysis using Pandas (Understanding Pandas, Series & DataFrames, DataFrame Operations, Handling Missing data, Filtering, Grouping and Joining, Loading Data from Datasets to DataFrames)
• Data Visualization using Matplotlib & Seaborn (Different types of Plots & Charts)

Module 2: Statistics & EDA

• Descriptive Statistics & Inferential Statistics
• Data & Variables in Statistics
• Measures of Central Tendency (Mean, Median & Mode)
• Variance & Standard Deviation
• Univariate Analysis (Histograms, Box plots & Bar charts)
• Bivariate/Multivariate Analysis (Line plots, Scatter plots, Heat maps)
• Probability Distribution & Central Limit Theorem
• Normal distribution & Standard Normal distribution
• Outlier detection
• Confidence Intervals & Hypothesis Testing
• Correlation & Regression

Module 3: Machine Learning

• Introduction to Machine Learning (Types of ML, Steps to build an ML Model, Supervised vs Unsupervised ML)
• Supervised Learning: Regression (Linear Regression: Simple & Multiple, Gradient Descent & Cost function, R^2 & MSE evaluation metrics, Polynomial, Lasso & Ridge regression, Overfitting & Underfitting, L1 & L2 Regularization, Bias Variance Trade off)
• Supervised Learning: Classification (Logistic Regression: Binary & Multi-class, SVM & KNN algorithm, Ensemble Methods: Decision Trees & Random Forest, Bagging, Boosting like Ada Boost, XG Boost, Gradient Boosting & Stacking)
• Unsupervised ML: Clustering (K-means, Hierarchical, Dbscan clustering)
• Regression & Classification Evaluation Metrics
• Feature Engineering, Model Tuning & Performance, Optimization Techniques

Note: each ML algorithm will be taught with real-time Projects.

Module 4: Deep Learning & NLP

• Introduction to Neural Networks
• Types of Neural Networks
• Computer Vision
• Natural Language Processing (NLP)

Module 5: Artificial Intelligence & Generative AI

• Introduction to Artificial Intelligence (History, Types, Areas and related disciplines, Subdomains, Applications, Tasks AI can solve)
• Introduction to Generative AI (Evolution, GenAI vs Traditional ML, How GenAI works, Applications, Popular GenAI Tools)
• Introduction to ChatGPT
• Large Language Models (LLM)
• Prompt Engineering
• Building GenAI models on Azure

Module 6: Azure Machine Learning

• Getting started with Azure AI Services
• Azure Machine Learning Studio & Workspace
• Train an ML Model strategy using Azure ML Designer
• Working with Automated Machine Learning (Auto ML)
• Finding the best Regression and Classification models with Auto ML
• NLP Text classification
• Computer Vision
• Generative AI using Azure OpenAI

Real World Projects

• Food Hub Order Analysis [Pandas, Statistics, & EDA]: Perform exploratory data analysis and provide actionable insights for a Retail Food company using Python Pandas, Seaborn, Statistics and EDA.
• Data Science Project
• Regression Project [Machine Learning]
• Classification Project [Machine Learning]
• Generative AI Project [Generative AI]

Bonuses & Target Outcomes

Bonuses Included:

• AI/ML Interview questions & answers
• Real-time scenarios and solutions
• Azure AI-102 and DP-100 certification support
• Resume/CV preparation & Building Project Portfolio
• LinkedIn profile optimization & Placement Assistance
• On-Job Support

Target Jobs:

• Machine Learning Engineer
• Data Scientist
• Azure Cloud AI/ML Engineer
• AI Data Analyst