What You Will Learn

This course covers key topics in Machine Learning, Data Science, and AI. You will learn to use Python for data analysis and model building. The course is structured into modules that guide you from basic to advanced concepts.

Module 1: Introduction to Python

• Understand Python basics, including variables and data types.

• Learn to use loops and functions in Python.

• Practice file handling techniques.

Module 2: Data Analysis

• Use NumPy for numerical data processing.

• Utilize Pandas for data manipulation and analysis.

• Learn data preprocessing techniques to clean and prepare data.

• Create data visualizations to represent findings.

Module 3: Statistics for ML

• Calculate mean, median, and variance of datasets.

• Understand correlation and its significance.

• Learn the basics of probability.

Module 4: Machine Learning Fundamentals

• Dive into supervised and unsupervised learning methods.

• Explore regression techniques for prediction.

• Understand classification tasks.

• Learn about clustering methods.

Module 5: ML Algorithms

• Implement Linear and Logistic Regression.

• Use Random Forest for decision-making.

• Understand Support Vector Machines (SVM).

• Practice using XGBoost for advanced predictions.

Module 6: Model Evaluation

• Evaluate model accuracy using various metrics.

• Understand Precision, Recall, and F1-score.

• Learn to use ROC-AUC for performance measurement.

• Analyze results with a Confusion Matrix.

Module 7: Advanced Concepts

• Explore Principal Component Analysis (PCA) for dimensionality reduction.

• Understand t-SNE for data visualization.

• Learn about SHAP for model explainability.

• Practice feature engineering for better model performance.

Module 8: Real-World Projects

• Implement a machine learning project from start to finish.

• Receive guidance on research and project development.

• Learn the basics of deploying machine learning models.