Best Artificial Intelligence Training Institute in Kottayam, Kerala

HTML5 - The Static Web Page Creation

• HTML5 Introduction
• Structure
• Elements
• Semantics
• Audio & Video
• Section & Article
• Canvas, Aside
• Drag & Drop
• Forms & Form Elements

CSS 3 - The Presentation Semantics

• CSS Properties, Selectors, Style Declaration Types
• Colors, Backgrounds, Text and Fonts
• Images, Links, Tables and List
• Borders, Padding, Margin
• Cursor, Dimension, Scrollbars, Visibility and Positioning
• Pseudo class & Elements, @Rules (import, font-face, charset)
• Filters, Media Types, Printing and Layouts
• Flexbox Layout, Alignment, and Responsive Design

Bootstrap

• Introduction to Bootstrap
• Bootstrap Grid System
• Creating Layouts with Bootstrap
• Bootstrap CSS - Understanding the CSS
• CSS Customization / Skins
• Responsive Web design with Bootstrap
• Single Page Responsive site with Bootstrap
• Bootstrap Layout Components
• Bootstrap Plug-ins: Transition, Modal, Dropdown, Scrollspy, Tab, Tooltip
• Building Websites with Bootstrap

JavaScript - The Interpreted Programming Language

• Interpreted Programming Languages
• Integrating JavaScript with HTML
• Variables in JavaScript
• Operators in JavaScript
• Expressions in JavaScript
• Arrays in JavaScript
• Handling Loops & Decision structures
• Executing Conditional statements
• Working with Functions

TypeScript - The Typed Superset of JavaScript

• Introduction to TypeScript
• Types and Variables
• Functions
• Objects, Interfaces & Classes
• Enums & Generics
• Advanced Type Concepts

AngularJS

• AngularJS Introduction
• Single Page Application (SPA)
• Directives, Filters and Data Binding
• Views, Controllers and Scope
• Modules, Routes and Factories

Python – Understanding the Preliminaries

• Installation and Environment Variables setting
• Statements and Syntax
• Variables Assignment
• Identifiers
• Basic Inputs
• Numbers
• Operators

Conditionals and Loops

• If else statement
• If elif else statement
• Switch
• While loop, For loop
• Break, Continue, Pass

Sequences: String, List and Tuple, Mapping and Set types

• Sequences
• Strings and operators
• String built-in Functions
• List and methods
• Tuples
• Mapping Type: Dictionaries
• Mapping type built-in functions
• Set Type – operators, built-in methods

Object-Oriented Programming

• Object-Oriented Programming
• Classes
• Class Attributes
• Instances
• Instances and Attributes
• Binding and Method Invocation
• Static Methods and Class Methods
• Inheritance

File handling (read/write CSV, JSON)

• Basic File Handling Concepts
• File Built-in Functions
• Handling CSV Files
• Handling JSON Files

Errors and Exceptions

• Exceptions in Python
• Detecting and Handling Exceptions
• Context Management
• Exceptions as Strings
• Raising Exceptions
• Assertions
• Standard Exceptions
• Creating Exceptions

NumPy and Pandas

• Introduction to NumPy
• NumPy arrays and operations
• Random Number Functions in NumPy
• Introduction to pandas
• DataFrames and Series in Pandas
• Reading and Writing data in Pandas
• Data manipulation: filtering, grouping, merging

Data Visualization

• Introduction to Matplotlib and Seaborn
• Line plots, bar charts, histograms, scatter plots
• Customizing plots

Python Framework - Django

• Introduction to Django
• Install Django
• MVC: Model, View and Template
• About the core files: models.py, urls.py, views.py
• Setting up database connections
• Managing Users & the Django admin tool
• Django URL Patterns and Views
• Django Forms

Relational Database Basics

• Brief History of MySQL
• Relational Databases and Popular Databases
• SQL Statements

Data Manipulation Language (DML)

• INSERT
• UPDATE
• DELETE
• SELECT

Data Definition Language (DDL)

• CREATE
• ALTER
• DROP

Sub-Queries, Joins and Unions with MySQL

• Order By, Like, And & Or, Where, Between
• Joins & Unions
• Aggregate Functions and Grouping

Constraints and Normalization

• Understanding Primary and Foreign Keys
• Understanding Database Normalization
• Views and Triggers

Introduction to Data Science

• What is Data Science?
• Data Science lifecycle
• Data Analyst vs Data Scientist
• Real-world applications

Data Wrangling and Preprocessing

• Handling Missing Values
• Removing Duplicates
• Encoding Categorical Variables
• Feature Scaling
• Outlier Detection & Treatment

Exploratory Data Analysis (EDA)

• Univariate Analysis
• Bivariate Analysis
• Multivariate Visualization
• Correlation and Covariance

Statistical Foundations

• Descriptive Statistics
• Probability Basics
• Inferential Statistics

Feature Engineering & Selection

• Feature Engineering
• Feature Selection
• Dimensionality Reduction (Intro Only)

Introduction to Machine Learning

• What is Machine Learning?
• Types of Machine Learning
• Supervised vs Unsupervised Learning
• Reinforcement Learning (Brief Intro)
• Real-world ML use cases

Scikit-learn & ML Workflow Basics

• Overview of Scikit-learn
• Steps in a typical ML workflow
• Importing data
• Preprocessing
• Model training and evaluation
• Basic classification/regression pipeline using Scikit-learn

Supervised Learning Algorithms

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• K-Nearest Neighbors (KNN)
• Support Vector Machines (SVM)

Unsupervised Learning

• K-Means Clustering
• Hierarchical Clustering
• PCA (Dimensionality Reduction)

Model Evaluation & Validation

• Train/test split
• Cross-validation
• Classification Metrics
• Accuracy, Precision, Recall, F1-score, ROC-AUC

Introduction to AI

• What is AI?
• History and evolution of AI
• AI vs ML vs DL
• AI subfields (NLP, CV, Robotics, etc.)

Search Algorithms & Problem Solving

• State space search
• BFS, DFS
• Heuristics and A* Algorithm
• Constraint satisfaction problems

Knowledge Representation & Reasoning

• Logic-based representation
• Propositional and First-order logic
• Inference rules, truth tables

Natural Language Processing

• Text preprocessing: tokenization, stemming, lemmatization
• Bag of Words, TF-IDF
• Sentiment analysis
• Named Entity Recognition (NER)

Neural Networks & Deep Learning Basics

• Perceptron and activation functions
• Feedforward networks and backpropagation
• Keras/TensorFlow intro
• Build and evaluate basic neural networks

AI Applications and Trends

• AI in healthcare, finance, robotics, etc.
• Ethical considerations in AI

• Structured Data: Organized in rows and columns (e.g., Excel, SQL databases)
• Unstructured Data: Data without a fixed format (e.g., images, videos, text)

AI is the simulation of human intelligence in machines that can perform tasks like reasoning, learning, and problem-solving.