ADAD
Advanced Diploma in Artificial Intelligence & Data Science
Duration: 4 Months
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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If you have any queries, Contact Us
Phone 📞: 8124788718 / 9444826464
Email 📧: cscminjur601203@gmail.com
Address: CSC Computer Education, 436, Hemachandra Nagar,
Minjur, Tamil Nadu 601203