ADAD

Overview

Participants will gain hands-on experience with popular tools and frameworks such as TensorFlow and scikit-learn. Through a combination of theoretical knowledge and practical exercises, learners will develop the skills necessary to excel in the dynamic fields of Artificial Intelligence and Data Science.

Objective

The objective of this course is to provide participants with a comprehensive understanding of Artificial Intelligence (AI) and Data Science concepts, tools, and techniques. The course aims to empower learners to apply these skills to solve real-world problems and make data-driven decisions.

Target Audience

School Final / Diploma / B.Sc / B.C.A / M.Sc / M.C.A / Engineering Students

Prerequisites

Participants should have a very basic understanding of any programming language. It is beneficial but not mandatory.

Course Coverage

Language Skills

Python

• Basic Syntax and Data Types
• Control Flow
• Functions and Modules
• File Handling
• Exception Handling
• List Comprehensions and Lambda Functions
• Object-Oriented Programming (OOP) Concepts

Data Management

MySQL

• Creating and Modifying Tables
• Data Manipulation (INSERT, UPDATE, DELETE)
• Querying Databases (SELECT, JOIN, GROUP BY)
• Aggregation Functions (SUM, AVG, COUNT)
• JOIN and Subqueries

MongoDB

• Introduction to NoSQL Databases
• CRUD Operations
• Data Indexing & Aggregation
• Working with Unstructured Data

Data Wrangling

NumPy

• Arrays and Array Operations
• Indexing and Slicing
• Mathematical Functions
• Linear Algebra Operations
• Random Module
• Broadcasting

Pandas

• Series and DataFrame
• Indexing and Selecting Data
• Data Cleaning and Manipulation
• Merging and Joining Data
• Grouping and Aggregating Data
• Handling Missing Data
• Time Series Data

Data Visualization

Matplotlib

• Basic Plotting (Line, Scatter, Bar)
• Customizing Plots (Labels, Titles, Legends)
• Multiple Subplots
• Histograms and Box Plots
• 3D Plotting

Machine Learning

Scikit-Learn

• Data Preprocessing
• Model Selection and Evaluation
• Supervised Learning (Regression, Classification)
• Unsupervised Learning (Clustering, Dimensionality Reduction)
• Pipelines

Deep Learning

TensorFlow & Keras

• Basics of Neural Networks
• CNN (Convolutional Neural Networks)
• RNN (Recurrent Neural Networks)
• NLP (Natural Language Processing)
• Building and Training Simple Models
• Layers and Activation Functions
• Loss Functions and Optimizers
• Model Evaluation and Validation
• Transfer Learning

Code Management

Git

• Setting up a Git Repository
• Version Control
• Branching & Merging
• Collaboration

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