Detailed Syllabus & Weekly Breakdown

Semester 1: Python Data Science & ML Algorithms (Weeks 1–12)

• Module 1: Python & Math Foundations (Weeks 1–6)
  • Week 1-2: Core Python for Data Science (NumPy, Pandas, SciPy)
  • Week 3-4: Applied Linear Algebra & Multivariate Calculus
  • Week 5-6: Probability, Distributions & Exploratory Data Analysis (EDA)
• Module 2: Supervised & Unsupervised Machine Learning (Weeks 7–12)
  • Week 7-8: Linear & Logistic Regression, Decision Trees, SVMs
  • Week 9-10: Ensemble Methods (Random Forests, Gradient Boosting/XGBoost)
  • Week 11-12: K-Means Clustering, PCA, Recommendation Systems

Semester 2: Deep Learning & Neural Architectures (Weeks 13–24)

• Module 3: Deep Learning Foundations (TensorFlow/PyTorch) (Weeks 13–18)
  • Week 13-14: Perceptrons, MLPs, Activation Functions, Backpropagation
  • Week 15-16: Hyperparameter Tuning, Dropout, Regularization, Optimization (Adam, SGD)
  • Week 17-18: Intro to TensorFlow & PyTorch Frameworks
• Module 4: Computer Vision & Advanced Networks (Weeks 19–24)
  • Week 19-20: Convolutional Neural Networks (CNNs), Pooling, Image Augmentation
  • Week 21-22: Transfer Learning (ResNet, VGG, YOLO)
  • Week 23-24: Object Detection & Image Segmentation

Semester 3: Advanced AI & MLOps Capstone (Weeks 25–36)

• Module 5: Natural Language Processing (NLP) & Sequences (Weeks 25–30)
  • Week 25-26: Text Preprocessing, Word Embeddings (Word2Vec, GloVe)
  • Week 27-28: Recurrent Neural Networks (RNNs), LSTMs, GRUs
  • Week 29-30: Sequence-to-Sequence Models & Sentiment Analysis
• Module 6: Enterprise Capstone & MLOps (Weeks 31–36)
  • Week 31-32: System Design & Architecture Planning for Capstone
  • Week 33-34: Core Model Development & Evaluation
  • Week 35-36: Model Deployment (Docker, Flask/FastAPI, AWS/GCP), Final Viva

Learning Outcomes

• Master mathematical foundations and write Python code for data processing and visualization.
• Build, tune, and evaluate classical Supervised and Unsupervised ML models.
• Architect Deep Learning models using PyTorch/TensorFlow for real-world applications.
• Deploy robust ML pipelines utilizing MLOps strategies and cloud architecture.

Assessment Weightage

Assessment Type	Weightage
Master Capstone Project & Live Deployment	30%
Hands-on Labs & Mini-Projects	30%
Module & Mid-Term Assessments	20%
Final Viva & Architecture Review	20%

The "WhiteCollar" Academic Rationale

This 9-month program bridges the gap between statistical theory and deep neural networks. By moving systematically from data preparation to mathematical modeling and scalable deployment, WhiteCollar Academy guarantees graduates are equipped with robust, end-to-end Machine Learning intelligence ready for enterprise implementation.