Data Science & Machine Learning

45-Day Schedule

Week 1: Introduction to Data Science & Python Basics

Learn the basics of Data Science, Python programming, NumPy, and Pandas.

Week 2: Statistics & Probability for Data Science

Master statistical concepts, hypothesis testing, and probability basics.

Week 3: Introduction to Machine Learning

Understand supervised and unsupervised learning, linear regression, and evaluation metrics.

Week 4: Advanced Machine Learning Techniques

Dive into ensemble learning, hyperparameter tuning, and neural networks.

Week 5: Real-World Applications

Apply data science to time series, computer vision, and deployment.

Week 6: Advanced Topics and Final Project

Explore deep learning, ethical AI, and cloud-based data science.

Course Information

Prerequisites: Basic programming knowledge is recommended.

Skills Covered: Python, NumPy, Pandas, Machine Learning, Neural Networks, Statistics, Data Visualization, Deployment.

Course Outcomes: Build a complete data science and machine learning portfolio with hands-on projects.

Modules

Introduction to Data Science

Understand Data Science concepts, Python basics, and essential libraries.

Data Science Overview
Python Basics
Introduction to NumPy and Pandas

Statistics & Probability

Learn statistical methods, hypothesis testing, and probability.

Descriptive Statistics
Hypothesis Testing
Probability Rules

Introduction to Machine Learning

Learn the basics of supervised and unsupervised learning.

Linear Regression
Logistic Regression
K-Means Clustering

Advanced Machine Learning Techniques

Explore ensemble learning, hyperparameter tuning, and deep learning.

Random Forest
Gradient Boosting
Neural Networks

Final Project

Project Description: Build an end-to-end machine learning solution, including:

Data Cleaning and Preprocessing
Exploratory Data Analysis (EDA)
Model Building and Tuning
Deployment

Deployment: Deploy using Streamlit or Flask for real-world applications.