Course Overview

Course Description:Step into the world of Artificial Intelligence with our comprehensive course designed for aspiring tech professionals and innovators.

Topics Covered:

• AI Basics
• Tools
• Python

Syllabus

Module 1: Introduction to AI

• What is AI? History and Evolution
• AI vs Machine Learning vs Deep Learning
• Real-world AI Applications (Chatbots, Face Recognition, AI in Healthcare, etc.)

Module 2: Introduction to Python for AI

• Basic Python Syntax
• Variables, Data Types, and Operators
• If-Else Statements and Loops

Module 3: Working with Data for AI

• What is Data?
• Structured vs Unstructured Data
• Introduction to NumPy and Pandas
• Loading and Displaying Data

Module 4: Introduction to Machine Learning

• What is Machine Learning?
• Types of ML (Supervised, Unsupervised, Reinforcement Learning)
• ML Workflow (Train → Test → Predict)

Module 5: Building a Simple AI Model

• Introduction to Scikit-Learn
• Training a Simple Machine Learning Model
• Making Predictions

Module 6: Introduction to Neural Networks

• What is a Neural Network?
• Structure of a Neural Network (Input, Hidden, Output Layers)
• Understanding Weights and Bias

Module 7: AI for Image Recognition

• Introduction to Image Processing
• Basics of Convolutional Neural Networks (CNNs)
• Training an Image Classifier

Learning Outcome

• Design and implement intelligent AI solutions using real-time data
• Perform automation and predictive analytics
• Develop smart decision-making applications across various industries

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Course Overview

Course Description:Intermediate skills in Artificial Intelligence, focusing on model development, data processing, and algorithm optimization.

Topics Covered:

• AI Basics
• Tools
• Python
• Projects
• Logic
• ML Models

Syllabus

Module 1: Introduction to AI

• What is AI?
• Types of AI (ANI, AGI, ASI)
• History & Milestones
• AI in Daily Life (Google, Netflix, Alexa, etc.)
• Careers in AI

Module 2: Python for AI

• Python Basics (variables, loops, functions)
• Data Types, Conditionals, OOP Overview
• Working with Libraries: NumPy, Pandas
• File Handling & Basic Visualization (Matplotlib)

Module 3: Data Fundamentals

• What is Data?
• Structured vs Unstructured Data
• Data Preprocessing, Cleaning, Normalization
• Exploratory Data Analysis (EDA)
• Hands-on: Cleaning Sample Datasets

Module 4: Basics of Machine Learning

• What is ML vs AI?
• Supervised vs Unsupervised Learning
• Simple Algorithms: Linear Regression, KNN, Decision Trees
• Model Evaluation Basics (Accuracy, Precision, Recall)

Module 5: Supervised Learning

• Regression Models: Linear, Logistic
• Classification Algorithms: SVM, Naive Bayes, Random Forest
• Model Evaluation Metrics (Confusion Matrix, ROC, F1)
• Hands-on: Spam Email Classification

Module 6: Unsupervised Learning

• Clustering: K-means, DBSCAN, Hierarchical
• Dimensionality Reduction: PCA, t-SNE
• Applications in Customer Segmentation
• Hands-on: Market Basket Analysis

Module 7: Introduction to NLP

• Text Preprocessing (Tokenization, Stop Words, Stemming)
• Bag of Words, TF-IDF
• Sentiment Analysis Basics
• Hands-on: Twitter Sentiment Analyzer

Module 8: Computer Vision Basics

• Image Representation & Preprocessing
• Edge Detection, Feature Extraction
• Introduction to CNNs (Conceptual)
• Hands-on: Face Detection using OpenCV

Module 9: Model Deployment Basics

• Saving & Loading Models (Pickle, Joblib)
• Introduction to Flask for AI Apps
• Hands-on: Deploy ML Model Locally

Module 10: Capstone Project

• Choose One:
• Customer Churn Prediction
• Movie Recommendation System
• Fake News Detection

Learning Outcome

• Design and implement intelligent AI solutions using real-time data
• Perform automation, predictive analytics, and smart decision-making
• Apply ML, NLP, and CV techniques to real-world problems

Enroll Now → →

Course Overview

Course Description:Master advanced AI development techniques, including deep learning models, natural language processing, and scalable AI solutions for real-world applications.

Topics Covered:

• AI Basics
• Tools
• Python
• Projects
• Logic
• ML Models
• Advanced AI
• Real Use

Syllabus

Module 1: Introduction to AI

• What is AI? Types of AI (ANI, AGI, ASI)
• History & milestones
• AI in daily life (Google, Netflix, Alexa)
• Careers in AI

Module 2: Python for AI

• Python basics (variables, loops, functions)
• Data types, conditionals, OOP overview
• Working with libraries: NumPy, Pandas
• File handling & basic visualization (Matplotlib)

Module 3: Data Fundamentals

• What is data? Structured vs unstructured
• Data preprocessing, cleaning, normalization
• Exploratory Data Analysis (EDA)
• Hands-on: Cleaning sample datasets

Module 4: Basics of Machine Learning

• What is ML vs AI?
• Supervised vs Unsupervised learning
• Simple algorithms: Linear Regression, KNN, Decision Trees
• Model evaluation basics (accuracy, precision, recall)

Module 5: Supervised Learning

• Regression models: Linear, Logistic
• Classification: SVM, Naive Bayes, Random Forest
• Evaluation metrics (Confusion Matrix, ROC, F1)
• Hands-on: Spam email classification

Module 6: Unsupervised Learning

• Clustering (K-means, DBSCAN, Hierarchical)
• Dimensionality Reduction (PCA, t-SNE)
• Applications in customer segmentation
• Hands-on: Market basket analysis

Module 7: Introduction to NLP

• Text preprocessing (tokenization, stop words, stemming)
• Bag of Words, TF-IDF
• Sentiment analysis basics
• Hands-on: Twitter sentiment analyzer

Module 8: Computer Vision Basics

• Image representation & preprocessing
• Edge detection, feature extraction
• Intro to CNNs (conceptual)
• Hands-on: Face detection using OpenCV

Module 9: Model Deployment Basics

• Saving & loading models (Pickle, Joblib)
• Intro to Flask for AI apps
• Hands-on: Deploy ML model locally

Module 10: Deep Learning Foundations

• Neural networks basics
• Activation functions & backpropagation
• Optimizers: SGD, Adam
• Hands-on: ANN for MNIST digits

Module 11: Computer Vision with CNNs

• CNN architecture (Conv, Pooling, Flatten)
• Transfer learning (ResNet, VGG)
• Hands-on: Image classifier with CNN

Module 12: Advanced NLP

• Word embeddings (Word2Vec, GloVe)
• Seq2Seq models
• Transformers, BERT, GPT
• Fine-tuning pre-trained models
• Hands-on: Chatbot using transformer models

Module 13: Reinforcement Learning

• RL fundamentals (agent, environment, reward)
• Q-learning & Deep Q Networks
• Applications: game AI, robotics
• Hands-on: Train AI to play a simple game

Module 14: AI in Production

• Model serving with FastAPI
• Scaling with Docker, Kubernetes
• Monitoring & retraining models

Module 15: Ethics & Future of AI

• Bias, fairness & explainability
• AI and jobs
• AI governance & regulations

Module 16: Final Capstone Project

• Fake news detection with transformers
• AI-powered recommendation engine
• Object detection system for security
• AI chatbot with real-time responses

Learning Outcome

Design and implement intelligent AI solutions using real-time data for automation, predictive analytics, and smart decision-making across industries.

Enroll Now → →