Course Curriculum
| Session 0: Course Material | |||
| ML – Course Material | 00:00:00 | ||
| Session 1: Introduction to Data Science & ML | |||
| ML-0 Introduction | 00:03:00 | ||
| ML-1. Introduction to Data Science and Machine Learning | 00:24:00 | ||
| Session 2: Foundations of Statistics for ML | |||
| ML-2. Basic Statistics Part-1 | 00:43:00 | ||
| ML-3. Basic Statistics Part-2 | 00:43:00 | ||
| Session 3: Data Preparation & Cleaning Techniques | |||
| ML-4. Data Cleaning-1 | 00:38:00 | ||
| ML-5. Data Cleaning-2. | 00:40:00 | ||
| Session 4: Regression Models & Predictive Analytics | |||
| ML-6. Regression Analysis-1 | 01:03:00 | ||
| ML-7. Regression Analysis-2 | 01:01:00 | ||
| ML-8. Logistic Regression | 00:57:00 | ||
| Session 5: Decision Trees & Model Evaluation | |||
| ML-9. Decision Tree-1 | 00:55:00 | ||
| ML-10. Decision Tree-2 | 00:45:00 | ||
| ML-11. Decision Tree-3 | 01:02:00 | ||
| ML-12. Model Selection | 00:48:00 | ||
| ML-13. Model Cross Validation | 00:42:00 | ||
| Session 6: Ensemble Learning Techniques | |||
| ML-14. Ensemble Model – Random Forest | 01:07:00 | ||
| ML-15. Ensemble Model – Boosting | 00:43:00 | ||
| Session 7: Feature Engineering | |||
| ML-16. Feature Engineering. | 00:53:00 | ||
| Session 8: NLP & Text Analytics | |||
| ML-17. NLP and Text Mining Concepts | 01:08:00 | ||
| ML-18. Sentiment Analysis | 00:31:00 | ||
| Session 9: Hypothesis Testing | |||
| ML-19. Testing of Hypothesis | 00:55:00 | ||
1845 STUDENTS ENROLLED
