AI & Data Science

20 weeksIntermediateData Science

4.8(200 reviews)

1000+ enrolled

AI & Data Science - Course Outline

Week 1:

Introduction to Artificial Intelligence

Topics:

History & Evolution of AI
AI vs. Machine Learning vs. Deep Learning
Applications of AI (Healthcare, Finance, Robotics, etc.)
AI in Business & Industry
Ethical Considerations in AI
Future of AI

Project:

Week 2:

Setting Up Python for AI

Topics:

Installing Python, Jupyter Notebook, and Essential Libraries
Variables, Data Types, and Operators
Writing Your First Python Program
Conditional Statements & Loops
Functions & Lambda Functions

Project:

Week 3:

Data Structures & Object-Oriented Programming in Python

Topics:

Lists, Tuples, Dictionaries, and Sets
OOP in Python (Classes, Objects, Inheritance, Encapsulation, Polymorphism)

Project:

Week 4:

Mathematics for AI – Linear Algebra & Probability

Topics:

Vectors, Matrices, and Operations
Probability, Statistics, and Data Distributions

Project:

Week 5:

Data Handling & Visualization

Topics:

Pandas for Data Analysis
Matplotlib & Seaborn for Data Visualization

Project:

Week 6:

NumPy & Scikit-Learn Basics

Topics:

NumPy for Numerical Computations
Scikit-Learn for ML Basics

Project:

Week 7:

Supervised Learning – Regression

Topics:

Linear Regression & Polynomial Regression
Decision Trees & Random Forest Regression
Feature Engineering & Data Preprocessing
Model Evaluation Metrics (RMSE, R² Score, MSE)

Project:

Week 9:

Supervised Learning – Classification

Topics:

Logistic Regression & K-Nearest Neighbors (KNN)
Support Vector Machines (SVM) & Naïve Bayes
Model Performance Metrics (Confusion Matrix, Precision, Recall, F1-score)
Handling Imbalanced Datasets & Hyperparameter Tuning

Project:

Week 11:

Unsupervised Learning

Topics:

Clustering – K-Means & Hierarchical
Principal Component Analysis (PCA) for Dimensionality Reduction
Anomaly Detection & Association Rule Learning
Implementing Clustering on Real Data

Project:

Week 15:

Neural Networks & TensorFlow

Topics:

Basics of Neural Networks (Perceptrons, Activation Functions)
Introduction to TensorFlow & Keras
Building a Simple ANN Model
Training & Evaluating Neural Networks

Project:

Week 17:

Convolutional Neural Networks (CNNs)

Topics:

CNN Architecture & Layers
Data Augmentation & Transfer Learning
Implementing CNNs for Image Classification
Hyperparameter Tuning for CNNs

Project:

Week 19:

Recurrent Neural Networks (RNNs) & NLP

Topics:

Introduction to NLP & Text Processing
Understanding RNNs & LSTMs
Implementing Sentiment Analysis
Building a Chatbot using NLP Techniques

Project:

Week 21:

Reinforcement Learning & GANs

Topics:

Markov Decision Process & Q-Learning
Introduction to Generative Adversarial Networks (GANs)
Building a Simple Reinforcement Learning Model
Implementing a GAN for Image Generation

Project:

Week 23:

Final Project & AI Ethics

Topics:

AI Ethics & Bias in Machine Learning
Deploying AI Models (Flask, FastAPI, Streamlit)
Capstone Project Planning
Presentation & Career Opportunities in AI

Project:

Our AI & Data Science courses equip you with skills in data analytics, machine learning, deep learning, computer vision, and more. Whether you are starting or advancing your career, we help you excel in these transformative fields. Gain the expertise needed to tackle real-world challenges and drive innovation in data-driven industries.

Your Instructor

Afaq Javed

Deep Learning & Data Science Expert

PhD in Machine Learning with over 8 years of experience in AI research and industry applications.

View LinkedIn Profile

What You'll Learn

Introduction to Data Science

Data Preprocessing and Cleaning

Statistical Analysis

Machine Learning Algorithms

Deep Learning and Neural Networks

Computer Vision Basics

Natural Language Processing

Model Deployment and Optimization

Course Features

Hands-On Projects

Real-World Data Sets

Machine Learning Frameworks (TensorFlow, PyTorch)

Advanced Analytics Tools (R, Python)

Career Support

Code Reviews

Portfolio Projects

Ameen Ijaz

Fantastic course with detailed lessons and practical projects. Highly recommend for anyone serious about AI.

Haider Ali

Great content and support, but more examples on deployment would be helpful.

Taha Akbar

Excellent course for beginners. The concepts are explained clearly with practical examples.

Abdulrehman

Great content and structure. Would recommend to anyone starting their programming journey.

AI & Data Science

View Outline

Introduction to Artificial Intelligence

History & Evolution of AI

AI vs. Machine Learning vs. Deep Learning

Applications of AI (Healthcare, Finance, Robotics, etc.)

AI in Business & Industry

Ethical Considerations in AI

Future of AI

Setting Up Python for AI

Installing Python, Jupyter Notebook, and Essential Libraries

Variables, Data Types, and Operators

Writing Your First Python Program

Conditional Statements & Loops

Functions & Lambda Functions

Data Structures & Object-Oriented Programming in Python

Lists, Tuples, Dictionaries, and Sets

OOP in Python (Classes, Objects, Inheritance, Encapsulation, Polymorphism)

Mathematics for AI – Linear Algebra & Probability

Vectors, Matrices, and Operations

Probability, Statistics, and Data Distributions

Data Handling & Visualization

Pandas for Data Analysis

Matplotlib & Seaborn for Data Visualization

NumPy & Scikit-Learn Basics

NumPy for Numerical Computations

Scikit-Learn for ML Basics

Supervised Learning – Regression

Linear Regression & Polynomial Regression

Decision Trees & Random Forest Regression

Feature Engineering & Data Preprocessing

Model Evaluation Metrics (RMSE, R² Score, MSE)

Supervised Learning – Classification

Logistic Regression & K-Nearest Neighbors (KNN)

Support Vector Machines (SVM) & Naïve Bayes

Model Performance Metrics (Confusion Matrix, Precision, Recall, F1-score)

Handling Imbalanced Datasets & Hyperparameter Tuning

Unsupervised Learning

Clustering – K-Means & Hierarchical

Principal Component Analysis (PCA) for Dimensionality Reduction

Anomaly Detection & Association Rule Learning

Implementing Clustering on Real Data

Neural Networks & TensorFlow

Basics of Neural Networks (Perceptrons, Activation Functions)

Introduction to TensorFlow & Keras

Building a Simple ANN Model

Training & Evaluating Neural Networks

Convolutional Neural Networks (CNNs)

CNN Architecture & Layers

Data Augmentation & Transfer Learning

Implementing CNNs for Image Classification

Hyperparameter Tuning for CNNs

Recurrent Neural Networks (RNNs) & NLP

Introduction to NLP & Text Processing

Understanding RNNs & LSTMs

Implementing Sentiment Analysis

Building a Chatbot using NLP Techniques

Reinforcement Learning & GANs

Markov Decision Process & Q-Learning

Introduction to Generative Adversarial Networks (GANs)

Building a Simple Reinforcement Learning Model

Implementing a GAN for Image Generation

Final Project & AI Ethics

AI Ethics & Bias in Machine Learning

Deploying AI Models (Flask, FastAPI, Streamlit)

Capstone Project Planning

Presentation & Career Opportunities in AI

Your Instructor

Afaq Javed

Deep Learning & Data Science Expert

PhD in Machine Learning with over 8 years of experience in AI research and industry applications.

What You'll Learn

Introduction to Data Science

Data Preprocessing and Cleaning

Statistical Analysis

Machine Learning Algorithms

Deep Learning and Neural Networks

Computer Vision Basics

Natural Language Processing

Model Deployment and Optimization