Course overview and objectives
Importance of ML and AI in the industry
Understanding the learning path and outcomes

Introduction to Python
Variables, data types, and operators
Control structures Functions and modules
Exception handling & File handling

Introduction to Linux OS
Linux commands and file system
File manipulation and permissions
Shell scripting basics

Fundamentals of Structured Query Language
SQL Tables, Joins, Variables
SQL Functions, Subqueries, Rules, Views
Stored procedures, rank function, SQL ROLLUP, etc.
Record grouping, searching, sorting, etc.
Clustered indexes, common table expressions.

Data Handling with NumPy
Data Manipulation Using Pandas
Data Pre-processing
Data Visualization

Linear algebra essentials for ML
Probability and distributions
Descriptive and inferential statistics
Hypothesis testing

Introduction to ML and its types
Supervised learning: Regression & Classification
Unsupervised learning: Clustering and dimensionality reduction
Model evaluation and hyperparameter tuning

Understanding big data and challenges
Introduction to Apache Spark and its ecosystem
Spark RDDs and transformations
Spark SQL and DataFrames
Working with big datasets in Spark

Introduction to time series data
Time series pre-processing and feature engineering
Time series forecasting methods
ARIMA and Seasonal decomposition
Time series analysis using Python libraries

Introduction to neural networking
Backpropagation
Stochastic gradient descent
Deep neural networking and its principles
CNNs, RNNs, LSTMs, and MLPs
GANs and Generative deep learning
Calculus and linear algebra
TensorFlow, CuPy, Keras, and PyTorch

Natural Language Processing (NLP) and text mining
Data cleaning and Pre-processing
Text classification
Sentence structuring, language modeling, sequence tagging, and sequence tasks
Vector space models and semantics
Dialog system

Basics of computer vision and OpenCV
Use of neural networking for image processing
Classification and clustering of an image using GANs, multitask classifiers, and kmeans
Detection of object
Image segmentation
Computer vision trends

Introduction to reinforcement learning
Markov Decision Processes (MDPs)
Q-learning and policy gradients
Deep Reinforcement Learning
Applications of reinforcement learning

Importance of data structures and algorithms
Arrays, linked lists, stacks, and queues
Trees, graphs, and hash tables
Sorting and searching algorithms
Algorithm complexity analysis