Course Content

Introduction to Data Science & Machine Learning
Understand the complete data science lifecycle and real-world applications in business, healthcare, finance, and automation.

Python for Data Science
Work with NumPy, Pandas, and Matplotlib for data manipulation and visualization.

Data Collection & Importing Data
Handle datasets from CSV, Excel, JSON, APIs, and databases.

Data Cleaning & Preprocessing
Manage missing values, outliers, encoding, feature scaling, and data transformation.

Exploratory Data Analysis (EDA)
Analyze patterns and trends using statistical methods and visualization techniques.

Statistics for Data Science
Understand probability, distributions, mean, variance, correlation, and hypothesis testing.

Feature Engineering
Select, transform, and create meaningful features to improve model performance.

Supervised Learning – Regression
Implement Linear Regression and evaluate models using MAE, MSE, and R² score.

Supervised Learning – Classification
Learn Logistic Regression, KNN, Decision Trees, Random Forest, and Support Vector Machines.

Unsupervised Learning
Apply clustering techniques like K-Means and Hierarchical Clustering.

Model Evaluation & Validation
Use train-test split, cross-validation, confusion matrix, accuracy, precision, recall, and F1-score.

Ensemble Learning
Understand bagging and boosting techniques to enhance model performance.

Introduction to Deep Learning
Learn basics of Neural Networks and frameworks like TensorFlow or Keras.

SQL for Data Science
Perform data extraction and manipulation using SQL queries.

Model Deployment
Deploy ML models using Flask or Streamlit and convert them into web applications.

Capstone Project
Build an end-to-end project from data collection and preprocessing to model building, evaluation, and deployment.