Artificial Intelligence Program

At Scholars Venue, we'll help you connect what you learn to what you do in real-world projects, illustrating how data flows through systems step-by-step with clear and engaging visuals.

Download

Course Syllabus

Trail Videos Free

Video 1

Video 2

Video 3

Video 4

Python for Data Science

Introduction to Python

Why Python for Data Science?
Python Setup and Jupyter Notebook

Python Fundamentals

Variables and Data Types
Operators and Expressions
Conditions
Loops

Python Collections

Lists
Tuples and Sets
Dictionaries

Functions and Files

Functions
Modules and Packages
File Handling
Exception Handling

NumPy

NumPy Arrays
Array Operations
Array Indexing and Slicing

Pandas

Pandas DataFrames
Reading CSV, Excel, and JSON Data
Data Cleaning with Pandas
Data Transformation with Pandas
Grouping and Aggregation

Data Visualization

Introduction to Matplotlib
Bar, Line, and Pie Charts
Histograms and Scatter Plots
Data Visualization Best Practices

AI for Python

AI-Assisted Python Development

Industry Updates & New Features

Python Tools for Data Science

Maths and Statistics for Data Science

Mathematics for Data Science

Why Maths for Data Science?
Numbers, Variables, and Functions
Graphs and Functions

Linear Algebra

Vectors
Matrices
Matrix Operations
Linear Transformations

Calculus

Limits and Derivatives
Gradient and Optimization
Partial Derivatives

Probability

Probability Basics
Probability Distributions
Conditional Probability
Bayes Theorem

Statistics

Mean, Median, and Mode
Variance and Standard Deviation
Sampling and Population
Hypothesis Testing
Correlation and Covariance

AI for Maths and Statistics

AI for Solving Data Science Problems

Industry Updates & New Features

Statistics in Modern AI

Data Analysis and Exploration

Introduction to Data Analysis

Data Analysis Workflow
Understanding Business Problems

Data Collection

Data Sources
CSV, Excel, Database, and API Data
Data Collection Best Practices

Data Cleaning

Missing Values
Duplicate Data
Outliers
Data Type Issues

Exploratory Data Analysis

Univariate Analysis
Bivariate Analysis
Multivariate Analysis
Finding Patterns and Insights

Feature Engineering

Understanding Features and Labels
Encoding Categorical Data
Feature Scaling
Feature Selection

AI for Data Analysis

AI-Assisted Data Exploration

Industry Updates & New Features

Modern Data Analysis Tools

Machine Learning

Introduction to Machine Learning

What is Machine Learning?
Types of Machine Learning
Machine Learning Workflow

Scikit-Learn Setup

Introduction to Scikit-Learn
Training and Testing Data
Model Training Workflow

Regression

Linear Regression
Multiple Linear Regression
Regression Evaluation
Regularization Basics

Classification

Logistic Regression
K-Nearest Neighbors
Decision Trees
Random Forest
Classification Evaluation

Unsupervised Learning

Introduction to Clustering
K-Means Clustering
Hierarchical Clustering
Dimensionality Reduction

Model Improvement

Overfitting and Underfitting
Cross Validation
Hyperparameter Tuning
Feature Importance

AI for Machine Learning

AI-Assisted Model Development
AI for Model Evaluation

Industry Updates & New Features

Machine Learning Trends and Tools

Deep Learning Fundamentals

Introduction to Deep Learning

What is Deep Learning?
Neural Networks
Activation Functions

Neural Network Training

Forward Propagation
Backpropagation
Loss Functions and Optimizers

TensorFlow and Keras

TensorFlow Setup
Building Neural Networks
Training and Evaluating Models

Computer Vision Basics

Image Data and CNNs
Image Classification
Transfer Learning

NLP Basics

Text Data Processing
Text Classification
Introduction to Transformers

AI for Deep Learning

AI-Assisted Deep Learning Development

Industry Updates & New Features

Deep Learning and Generative AI Trends

Project Phase 1: Data Analysis Project

Project Foundation

Project Roadmap and Business Problem
Data Science Project Life Cycle

Data Understanding

Understanding Dataset and Business Questions
Defining KPIs and Target Variables

Data Preparation

Data Collection and Loading
Data Cleaning and Missing Values
Exploratory Data Analysis

Data Insights

Visual Analysis and Pattern Discovery
Business Insights and Recommendations
Project Documentation

Project Delivery

Final Analysis Presentation
Project Review and GitHub Upload

Project Phase 2: Machine Learning Project

Project Foundation

ML Business Problem and Success Criteria
ML Project Architecture

Data Preparation

Data Collection and Dataset Loading
Data Cleaning and Exploratory Analysis
Feature Engineering

Model Development

Train-Test Split and Baseline Model
Training Multiple ML Models
Model Evaluation
Hyperparameter Tuning

Model Delivery

Final Model Selection
Saving and Loading ML Models
Building a Prediction Application
Project Documentation and GitHub Upload

Final Presentation

ML Project Presentation
Project Review and Certification

Project Phase 3: Deep Learning Project

Project Foundation

Deep Learning Business Problem
Dataset and Model Architecture

Data Preparation

Preparing Image or Text Data
Data Augmentation and Preprocessing
Training and Validation Data

Model Development

Building a Neural Network Model
Training and Evaluating the Model
Improving Model Performance
Transfer Learning

Model Deployment

Saving the Deep Learning Model
Building a Prediction Application
Deploying the Application

Project Delivery

Final Project Presentation
GitHub Documentation
Project Review and Certification

Projects

Apply what you learn by building real-world projects from this specialization.

Image Classification with CNN

Build and train a convolutional neural network to classify images across multiple categories.

NLP Sentiment Analysis

Develop a sentiment analysis model using transformer-based NLP to analyze customer reviews.

Movie Recommendation System

Create a collaborative filtering recommendation system using machine learning algorithms.