AI Engineering (Batch-02)

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Course Content

Introductory Class

  • AI Engineering (Batch-02) – Introductory Session
    02:13:00

Class 01 – Anaconda Setup | Intro to Python | Data Types (Numbers, Float, String) | Formatted Strings | Built-in Functions

Class 02 – Data Types (Lists, Tuples, Sets, Booleans, Dictionaries) | Built-in Functions

Class 03 – Python Fundamentals | if-elif-else | Comparison & Logical Operators | for & while Loops

Class 04 – Lambda & User-Defined Functions | Intro to Python Libraries | NumPy (1D/2D/3D Arrays, Indexing, Functions) | Matplotlib Basics & Plotting Graphs

Class 05 – Introduction to Pandas | Exploratory Data Analysis (EDA) | Data Pre-processing | Importing Excel/CSV Files | Visualizations with Matplotlib & Seaborn | Correlation & Heatmaps

Class 06 – Real-World Python Projects – PDF Merger, Watermarking, Text/Image Extraction | Email Sender with Static/Dynamic Content | Audio-to-Text Transcription & Urdu Translation | Use Cases & Opportunities Overview

Class 07 – Introduction to Machine Learning & Its Types (Supervised, Unsupervised, Reinforcement) | Features Explained | Converting Categorical to Numerical Data

Class 08 – Evaluation Metrics (Confusion Matrix, Accuracy, Precision, Recall, F1 Score) | Feature Scaling | Logistic Regression & SVM – Theory & Implementation | Making Predictions on New Data

Class 09 – Machine Learning: Standardization & Normalization | Decision Tree – Scikit-Learn & Manual Implementation

Class 10 – Machine Learning: Random Forest Implementation | Underfitting vs. Overfitting | Bias-Variance Tradeoff | ROC & AUC Curves | Introduction to KNN

Class 11 – Machine Learning: KNN Implementation | Linear Regression (Simple & Multiple) | Regression Metrics: MAE, MSE, RMSE, R²

Class 12 – Machine Learning: DBSCAN and HDBSCAN – Explanation and Implementation

Class 13 – Machine Learning: Association Rule Learning – Apriori & FP-Growth Implementation | Streamlit Setup & Configuration in VS Code

Class 14 – Machine Learning: Naive Bayes Algorithm – Explanation and Implementation | Streamlit Setup & Configuration

Class 15 – Machine Learning: Class Imbalance Handling | Resampling Techniques (SMOTE, Tomek Links, ENN) | Model-Level & Metric-Level Solutions | Implementation & Best Practices

Class 16 – Machine Learning: Ensemble Learning Methods | Bagging, Boosting, Stacking & Voting Classifier | XGBoost & AdaBoost Implementation | Advanced Ensemble Techniques

Class 17 – Flask Framework: Web Development Fundamentals | Routes, GET/POST Methods & Project Structure | Virtual Environment Setup | Project 01: To-Do List App | Project 02: Weather Forecasting App with OpenWeatherMap API

Class 18 – Flask Framework: Advanced Implementation | ML Model Deployment using Pickle Library | Student Issue Resolution | Project: IRIS Flower Prediction AI App | Model Serialization & Web Integration

Class 19 – Deep Learning: Introduction to Neural Networks | Neurons, Input/Hidden/Output Layers | Single & Multi-Layer Perceptron Theory & Implementation | Activation Functions & Backpropagation

Class 20 – Deep Learning: Convolutional Neural Networks (CNN) | CNN Architecture Components | Explanation of Kernels/Filters, Stride, Padding & Pooling Layers | Project: Face Mask Detection & Hand Gesture Recognition using MediaPipe & CNN

Class 21 –

Class 22 –