What you will Learn ?

This course provides a comprehensive introduction to Machine Learning (ML), covering fundamental concepts, techniques, and real-world applications. By the end of this course, you will be able to:

1. Understand the Basics of Machine Learning

• Learn the core principles of ML and its real-world applications.
• Understand different types of ML algorithms, including supervised and unsupervised learning.

2. Master Statistical Foundations

• Gain proficiency in essential statistical concepts required for ML.
• Learn about probability, distributions, and key statistical measures.

3. Perform Data Cleaning & Preprocessing

• Handle missing data, outliers, and inconsistencies in datasets.
• Learn techniques for feature scaling, transformation, and encoding.

4. Build Regression Models

• Implement Linear and Multiple Regression for predictive modeling.
• Explore techniques for improving regression model performance.

5. Apply Logistic Regression for Classification

• Understand the principles of classification using Logistic Regression.
• Learn to evaluate model performance using accuracy metrics.

6. Develop Decision Trees for Classification and Regression

• Learn how Decision Trees work and how to build them.
• Explore pruning techniques to improve model efficiency.

7. Perform Model Selection and Cross-Validation

• Learn strategies for selecting the best-performing ML models.
• Apply Cross-Validation techniques to enhance model reliability.

8. Work with Ensemble Learning Models

• Understand and implement Random Forest and Boosting techniques.
• Learn how ensemble models improve prediction accuracy.

9. Enhance Data with Feature Engineering

• Learn methods to create and select relevant features.
• Improve model performance through feature transformation techniques.

10. Explore Natural Language Processing (NLP)

• Understand text mining and sentiment analysis in NLP applications.
• Learn how to process and analyze textual data for insights.

11. Conduct Hypothesis Testing

• Apply statistical hypothesis testing to validate ML models.

12. Work on End-to-End ML Projects

• Apply ML techniques to solve real-world problems.
• Gain hands-on experience with data preprocessing, model building, and evaluation.

This course equips you with the essential skills to build and optimize ML models, preparing you for careers in data science, AI, and analytics.