MACHINE LEARNING (ML)

Categories: CSE / IT

About Course

This program turns students into data scientists capable of building intelligent, predictive systems. The first month focuses on the mathematical foundations and the “Big Three” of ML: Supervised, Unsupervised, and Reinforcement Learning. The second month focuses on deploying models into production and handling real-world, “messy” datasets to solve business problems.

Exploratory Data Analysis (EDA) & Preprocessing
Regression and Classification Algorithms
Clustering & Dimensionality Reduction
Neural Networks & Deep Learning Basics
Model Evaluation & Hyperparameter Tuning
Industry Tools: Python (Scikit-Learn, Pandas, NumPy), Jupyter Notebooks, TensorFlow, Kaggle
Hands-on Industry Projects

Course Content

Month 1: Theory & Concepts (Monday – Friday Classes)

Module 1: Foundations of AI & ML

Module 2: Data Preprocessing & EDA

Module 3: Supervised Learning – Regression

Module 4: Supervised Learning – Classification

Module 5: Unsupervised Learning

Module 6: Model Evaluation & Optimization

Month 2: Industry-Level Projects

Project 1: Predictive Real Estate Pricing Model

Project 2: Customer Segmentation for E-commerce

Student Ratings & Reviews

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Your Instructor

v93117148@gmail.com

MACHINE LEARNING (ML)

MACHINE LEARNING (ML)

About Course

What Will You Learn?

Course Content

Month 1: Theory & Concepts (Monday – Friday Classes)

Module 1: Foundations of AI & ML

Difference between AI, ML, and Deep Learning

AICTE Standards for Data Ethics and Bias

The ML Pipeline: Data Collection to Model Deployment

Module 2: Data Preprocessing & EDA

Handling Missing Values and Outliers

Feature Scaling (Standardization vs. Normalization)

Data Visualization with Matplotlib and Seaborn

Module 3: Supervised Learning – Regression

Linear and Multiple Regression

Polynomial Regression and Overfitting/Underfitting

Cost Functions and Gradient Descent

Module 4: Supervised Learning – Classification

Logistic Regression and Decision Trees

K-Nearest Neighbors (KNN) and Support Vector Machines (SVM)

Random Forest and Ensemble Techniques

Module 5: Unsupervised Learning

K-Means Clustering and Hierarchical Clustering

Principal Component Analysis (PCA) for Feature Reduction

Association Rule Learning (Apriori Algorithm)

Module 6: Model Evaluation & Optimization

Confusion Matrix, Precision, Recall, and F1-Score

Cross-Validation and Bias-Variance Tradeoff

Introduction to Neural Networks and Perceptrons

Month 2: Industry-Level Projects

Project 1: Predictive Real Estate Pricing Model

Building a regression model to predict house prices based on multi-variable inputs

Tuning the model for maximum accuracy using Scikit-Learn

Project 2: Customer Segmentation for E-commerce

Using Unsupervised Learning (K-Means) to group customers by purchasing behavior

Visualizing clusters to provide marketing insights for a business

Your Instructor

v93117148@gmail.com

MACHINE LEARNING (ML)

About Course

What Will You Learn?

Course Content

Month 1: Theory & Concepts (Monday – Friday Classes)

Module 1: Foundations of AI & ML

Difference between AI, ML, and Deep Learning

AICTE Standards for Data Ethics and Bias

The ML Pipeline: Data Collection to Model Deployment

Module 2: Data Preprocessing & EDA

Handling Missing Values and Outliers

Feature Scaling (Standardization vs. Normalization)

Data Visualization with Matplotlib and Seaborn

Module 3: Supervised Learning – Regression

Linear and Multiple Regression

Polynomial Regression and Overfitting/Underfitting

Cost Functions and Gradient Descent

Module 4: Supervised Learning – Classification

Logistic Regression and Decision Trees

K-Nearest Neighbors (KNN) and Support Vector Machines (SVM)

Random Forest and Ensemble Techniques

Module 5: Unsupervised Learning

K-Means Clustering and Hierarchical Clustering

Principal Component Analysis (PCA) for Feature Reduction

Association Rule Learning (Apriori Algorithm)

Module 6: Model Evaluation & Optimization

Confusion Matrix, Precision, Recall, and F1-Score

Cross-Validation and Bias-Variance Tradeoff

Introduction to Neural Networks and Perceptrons

Month 2: Industry-Level Projects

Project 1: Predictive Real Estate Pricing Model

Building a regression model to predict house prices based on multi-variable inputs

Tuning the model for maximum accuracy using Scikit-Learn

Project 2: Customer Segmentation for E-commerce

Using Unsupervised Learning (K-Means) to group customers by purchasing behavior

Visualizing clusters to provide marketing insights for a business

Student Ratings & Reviews

Your Instructor

v93117148@gmail.com

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