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AI & Machine Learning

Er Yogendra Singh Rajput December 13, 2025
Winter Training - AI & Machine Learning
Winter Training 2025

Artificial
INTELLIGENCE

Unlock the power of Data. Learn Python, Machine Learning Algorithms, Deep Neural Networks, and deploy AI models.

Start Learning Course Report

ML

Algorithms

DL

TensorFlow

NLP

Processing

Input Hidden 1 Hidden 2 Output
model_training.py Epoch: 0/100
Loss: 0.8542 Accuracy: 0.1245
// Simulating Forward Propagation...
Initializing Weights...
Winter Training Curriculum

Detailed Topic Breakdown

A comprehensive journey from Python basics to deploying advanced AI models on the cloud.

Module 1

Python & Statistics

Building the foundation with Python programming and statistical mathematics.

Week 1 (Day 1-7)

Part A: Python for AI

  • Python Env Setup (Anaconda/Jupyter)
  • Variables, Data Types, Operators
  • Control Flow & Loops
  • Functions & Lambda Expressions
  • Data Structures (Lists, Dicts, Sets)
  • File Handling (CSV, JSON, TXT)
  • Exception Handling
  • NumPy Arrays & Matrix Operations
  • Pandas DataFrames Basics
  • Matplotlib for Plotting

Part B: Data & Stats

  • Mean, Median, Mode
  • Standard Deviation & Variance
  • Probability Distributions
  • Hypothesis Testing
  • Data Cleaning & Preprocessing
  • Handling Missing Values
  • Feature Scaling (Normalization)
  • Exploratory Data Analysis (EDA)
  • Seaborn Visualization
  • Correlation & Covariance
Module 2

Machine Learning

Core algorithms for supervised and unsupervised learning using Scikit-Learn.

Week 2 (Day 8-14)

Part A: Supervised Learning

  • Linear Regression (Simple/Multiple)
  • Logistic Regression
  • Decision Trees & Pruning
  • Random Forest Classifier
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
  • Naive Bayes Algorithm
  • Model Evaluation (Confusion Matrix)
  • ROC Curve & AUC Score
  • Bias-Variance Tradeoff

Part B: Unsupervised & Advanced

  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN Algorithm
  • Principal Component Analysis (PCA)
  • Dimensionality Reduction
  • Ensemble Learning (Bagging/Boosting)
  • XGBoost & Gradient Boosting
  • Hyperparameter Tuning (GridSearch)
  • Cross-Validation Techniques
  • Save/Load Models (Pickle/Joblib)
Module 3

Deep Learning

Neural Networks, TensorFlow, Keras, and Computer Vision basics.

Week 3 (Day 15-21)

Part A: Neural Networks

  • Intro to ANN (Artificial Neural Network)
  • Perceptrons & Activation Functions
  • Forward & Backward Propagation
  • Loss Functions & Optimizers (Adam/SGD)
  • TensorFlow & Keras Installation
  • Building Sequential Models
  • Training & Validation Splitting
  • Overfitting & Dropout
  • Model Checkpointing
  • Visualizing Training History

Part B: CNN & Vision

  • Convolutional Neural Networks (CNN)
  • Convolution Layers & Filters
  • Pooling Layers (Max/Average)
  • Flattening & Dense Layers
  • Image Augmentation
  • Transfer Learning (VGG16/ResNet)
  • Object Detection Basics
  • Intro to OpenCV for Preprocessing
  • Face Recognition Project
  • Digit Classification (MNIST)
Module 4

NLP & Deployment

Processing text data, RNNs, and deploying models as Web APIs.

Week 4 (Day 22-28)

Part A: NLP Basics

  • Natural Language Processing Intro
  • Tokenization & Lemmatization
  • Stop Words & Stemming (NLTK/SpaCy)
  • Bag of Words (BoW) & TF-IDF
  • Word Embeddings (Word2Vec)
  • RNN (Recurrent Neural Networks)
  • LSTM (Long Short Term Memory)
  • Sentiment Analysis Project
  • Text Classification
  • Chatbot Logic Basics

Part B: Projects & Deploy

  • Flask Web Framework for AI
  • Creating API Endpoints
  • Streamlit for Quick AI UI
  • Project 1: House Price Predictor
  • Project 2: Stock Market Analysis
  • Project 3: Spam Email Classifier
  • Project 4: Face Mask Detection
  • Deployment on Heroku/Render
  • Documentation & Portfolio
  • Final Capstone Presentation

Download Certification Resources

Access the complete 30+ page Project Report, Jupyter Notebooks, Datasets, and Slides.

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Disclaimer: This training covers open-source libraries like TensorFlow, Scikit-Learn, and PyTorch for educational purposes.

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