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Detailed Syllabus & Weekly Breakdown

Module 1: Python & Data Prep for ML (Weeks 1–3)

Focus: Preparing data for machine learning models.

• Python for ML: Crash course in Python, focusing on Pandas and NumPy for data manipulation.
• Data Cleaning: Handling missing values, outliers, and inconsistent data.
• Feature Engineering: Creating new features from existing data to improve model performance.
• Data Visualization with Matplotlib/Seaborn: Exploring data to find patterns.

Module Outcome: Write Python scripts to clean, transform, and visualize datasets for ML tasks.

Module 2: Supervised Learning Algorithms (Weeks 4–6)

Focus: Building models that predict outcomes.

• Regression: Linear Regression, and understanding its assumptions.
• Classification: Logistic Regression, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM).
• Tree-Based Models: Decision Trees, Random Forests, and Gradient Boosting.
• Model Evaluation: Accuracy, Precision, Recall, F1-Score, and Confusion Matrix.

Module Outcome: Build, train, and evaluate various supervised learning models for regression and classification problems.

Module 3: Unsupervised Learning & Model Tuning (Weeks 7–9)

Focus: Finding hidden patterns and optimizing models.

• Clustering: K-Means Clustering for customer segmentation and anomaly detection.
• Dimensionality Reduction: Principal Component Analysis (PCA) to reduce feature complexity.
• Hyperparameter Tuning: Using GridSearchCV and RandomizedSearchCV to find the best model parameters.
• Cross-Validation: Techniques to prevent overfitting and build robust models.

Module Outcome: Apply unsupervised learning techniques and optimize supervised models for better performance.

Module 4: Intro to Deep Learning & Deployment (Weeks 10–12)

Focus: Moving towards advanced models and real-world application.

• Introduction to Neural Networks: Understanding perceptrons and backpropagation.
• Building a Neural Network with TensorFlow/Keras: A simple deep learning model for classification.
• Model Serialization: Saving and loading trained models using Pickle.
• Deploying with Flask: Creating a simple web API to serve your ML model.

Module Outcome: Build a basic neural network and deploy a trained machine learning model as a web service.

Comprehensive Learning Outcomes

• Algorithmic Understanding: Differentiate between various ML algorithms and know when to apply each one.
• Practical Model Building: Implement the end-to-end machine learning pipeline, from data cleaning to model evaluation.
• Python & Scikit-Learn Proficiency: Gain hands-on expertise with the most popular library for machine learning in Python.

Assessment Weightage

Assessment Type	Weightage	Focus Area
Data Cleaning & Feature Engineering Assignments	30%	Preparing real-world datasets for modeling.
Classification Model Project	40%	Building and comparing multiple classification models to solve a business problem.
Final Capstone Project & Deployment	30%	An end-to-end project including data prep, model training, and deployment via a simple API.

The "WhiteCollar" Career Advantage

Machine Learning is no longer a niche skill; it's a core competency for modern tech roles. This course provides the foundational knowledge and practical portfolio projects that recruiters look for, giving you a clear advantage in interviews for data analyst, ML engineer, and data scientist positions.