Description
TABLE OF CONTENT
Unit 1: Introduction to AI & Data Analytics
Overview of Artificial Intelligence (AI)
Role of Data Analytics in Decision Making
Importance of AI & Data Analytics in Various Industries
Unit 2: Foundations of Data
Understanding Data Types and Structures
Data Collection and Cleaning
Exploratory Data Analysis (EDA)
Unit 3: Basics of Statistics
Descriptive Statistics
Inferential Statistics
Probability Distributions
Unit 4: Machine Learning Overview
Introduction to Machine Learning
Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
Applications of Machine Learning in Real-world Scenarios
Unit 5: Data Preprocessing
Feature Scaling and Normalization
Handling Missing Data
Encoding Categorical Data
Unit 6: Model Building
Selecting and Evaluating Models
Model Training and Testing
Model Deployment
Unit 7: Introduction to Deep Learning
Neural Networks and Deep Learning
Deep Learning Applications
Overview of TensorFlow and PyTorch
Unit 8: Data Visualization
Importance of Data Visualization
Tools for Data Visualization (e.g., Matplotlib, Seaborn, Tableau)
Unit 9: Big Data Analytics
Introduction to Big Data
Hadoop and MapReduce
Apache Spark for Big Data Processing
Unit 10: AI Ethics and Bias
Ethical Considerations in AI & Data Analytics
Addressing Bias in Data and Algorithms