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General
Module 1: Foundations of Artificial Intelligence (AI)
History
What is AI?
Artificial Intelligence Based on Capabilities
Artificial Intelligence Based on Functionalities
Branches of AI: Method-Based and Application Based
Case Study 1: Healthcare
Case Study 2: Retail
Case Study 3: Finance
Case Study 4: Marketing
Module 2: Mathematical Concepts of AI
2.1 Vectors, Matrices, and Their Operations
2.2 Eigenvalues, Eigenvectors, and Linear Transformations
Determinants
Derivatives, Partial Derivatives, and Gradients
Optimization Techniques
Integration
Probability Distributions
Hypothesis Testing
Bayesian Inference
Sets and Logic
Graph Theory
Combinatorics
Module 3: Python for Developer
Getting Started with Python: History and Setup
Programming Fundamentals: Basic Syntax
Programming Fundamentals: Control Flow
Programming Fundamentals: Data Structure
Code Organization: Modules and Packages
NumPy: The Numerical Workhorse
Pandas: The Data Wrangler
Matplotlib and Seaborn
Module 4: Mastering Machine Learning
Machine Learning: Past and Present
Scope and Types of Machine Learning
Terminologies, and Lifecycle
Regression and its types
Classification: Logistic Regression, Support Vector Machines, and Random Forests
Clustering
Dimensionality Reduction
Metrics and Validation for ML Models
Module 5: Deep Learning
Building Blocks of Artificial Neural Networks
Deep Learning Frameworks and its Applications
CNN: Introduction, Layers, and Architecture
Understanding RNNs: Architecture, LSTM, and GRU
RNNs, LSTM, and GRU: Comparison, Selection, and Applications
Module 6: Computer Vision
Image Representation, Filtering, and Transformations
Object Detection: Overview, Process and Techniques
Region Proposal Methods
Single Shot MultiBox Detector (SSD)
Image Segmentation: Definition and Types
Architecture, Training, and Applications
Module 7: Natural Language Processing
Detailed Curriculam
Module 1: Foundation of Artificial Intelligence
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