Course Description: Data Science with Machine Learning

A comprehensive journey from foundational data science concepts to advanced machine learning techniques.

Key Highlights:

1. Beginner Level:

   • Introduction to Data Science and Machine Learning.
   • Basics of Python for Data Analysis (NumPy, Pandas, Matplotlib).
   • Understanding and visualizing data with exploratory data analysis (EDA).

2. Intermediate Level:

   • Data preprocessing: Handling missing data, scaling, and encoding.
   • Statistical analysis and hypothesis testing.
   • Supervised learning: Linear regression, logistic regression, decision trees, etc.
   • Model evaluation: Metrics like accuracy, precision, recall, and ROC-AUC.

3. Advanced Level:

   • Unsupervised learning: Clustering (K-Means, DBSCAN) and Dimensionality reduction (PCA).
   • Ensemble methods: Random Forest, Gradient Boosting, and XGBoost.
   • Neural networks and deep learning basics using TensorFlow or PyTorch.
   • Real-world project workflows: Deployment and performance optimization.

4. Hands-On Practice:

   • Real-world datasets and case studies (e.g., healthcare, finance, and e-commerce).
   • Capstone projects to solidify skills.

5. Career-Ready Skills:

   • Building machine learning pipelines.
   • Understanding ML in production environments.
   • Guidance on interviews, portfolio creation, and industry tools like Git.

This course ensures a smooth transition from novice to expert, equipping students with both theoretical and practical expertise.