Applied Python for Data Science & AI
- Python Installation and Environment Setup
- Syntax and Data Types
- Control Statements
- Loops
- Functions in Python
- Lambda Functions
- Modules and Packages
- Closures and Decorators in Python
- Object Oriented Programming Concepts- Basic & Advanced
- File Handling JSON, CSV
- Exception Handling
- Real World Programs
Data Structures
- List
- Tuple
- Sets
- Dictionaries
- Nested Data Structure
- Iterators
Statistics for Data Science
- Descriptive & Inferential Statistics
- Probability Theory
- Probability Distribution Function for Data
- Hypothesis Testing: Z-test, T-test and P-value
- Linear Algebra: Matrix operations, Eigenvalues
- Data Visualization of Statistical Properties
- Mini Project: Statistical Analysis for Stock Price Prediction
Feature Engineering, Data Analysis and Preprocessing, Data Visualization
- Data Collection: APIs, Web Scraping, Kaggle
- Data Cleaning, Transformation: Outliers, Nulls & Missing Values Feature Scaling
- Exploratory Data Analysis using Pandas
- Data Manipulation Using NumPy
- Feature Engineering: Encoding, Binning
- Visualization Tools: Matplotlib, Seaborn, Plotly, Streamlit
- Mini Project: Visualize and analyze Financial Data
Machine Learning Concepts: Training, Overfitting, Evaluation
- Supervised Learning:
- A. Regression: Linear Regression, Multiple Linear Regression, Polynomial Regression, Regularization with Ridge and Lasso, Model Evaluation (MAE, MSE, RMSE, R Square)
- B. Classification: Logistic Regression, Naive Bayes, K Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine
- Unsupervised Learning: k- Mean Clustering, Hierarchical Clustering, Principal Component Analysis, DBSCAN
- Ensemble Learning: Bagging, Boosting, XG Boost
- Evaluation Metrics: Confusion Matrix, Precision, Recall, Accuracy, F1 Score, ROC-AUC
- Hyper Parameter Tuning: Bais- Variance Tradeoff, Overfitting, Underfitting
- Mini Projects on Different Business Domains
Model Deployment
- Model Serialization: Pickel & Joblib
- Fast API Integration
Deep Learning & Neural Networks
- AI & Deep Learning
- Neural Network Basics: Perceptron, Layers, Activation Functions, Loss Function, Forward Propagation & Backpropagation
- Optimization Techniques
- Artificial Neural Network with PyTorch, TensorFlow & Keras
- Model Tuning
- Convolutional Neural Network for Image Classification
- Recurrent Neural Networks & Transformers for Sequence Models
- Transfer Learning: VGG, ResNet, MobileNet
- Model Evaluation & Deployment
- Mini Project: Image Processing and Classification
Natural Language Processing
- Text Preprocessing: Tokenization, Lemmatization, Stop Words
- Word Embeddings: TF-IDF, Word2Vec
- LSTM for Text Classification
- Mini Projects: Resume Classification Based on Job Description
Generative AI
- GenAI Basics
- Large Language Models
- Transformer & Modern NLP: BERT/ GPT
- Fine Tuning Large Language Models using Hugging Face
- Retrieval-Augmented Generation (RAG)
- Building Chatbots with Open Source APIs
Cloud Computing for AI & Machine Learning
- Cloud Concepts
- Virtual Machines
- Cloud Service Providers: Microsoft Azure, AWS & GCP
Git for Machine Learning & AI
- Git Setup & Basics
- Core Git Workflow
- Branching for Experiments
- Merging & Conflict Resolution
- Working with Remote Repositories (GitHub)
- Mini Project: Build Prediction Model, Track Preprocessing, Try 3 Models, Use Branches for each Model & Finally Merge Best Model
