Curriculum
- 9 Sections
- 30 Lessons
- Lifetime
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- Module 1 :Introduction to AI & ML8
- 1.1What is AI, ML, and Data Science?
- 1.2AI , ML , Data Science Relationship
- 1.3Supervised vs. Unsupervised Learning
- 1.4Semi-Supervised Learning and Reinforcement Learning
- 1.5ML Workflow & Real-world Applications
- 1.6Databricks in the ML Workflow
- 1.7Assignment : AI Data Science Using Databricks (40 Hours) -Module 13 Days
- 1.8AI Data Science Using Databricks – Module 115 Minutes5 Questions
- Module 2 :Python for Machine Learning6
- Module 3 :Regression & Classification6
- 3.1Linear Regression & Polynomial Regression
- 3.2Logistic Regression & Classification Metrics
- 3.3Decision Trees & Random Forest
- 3.4Model Evaluation (Bias-Variance, Cross-validation, Hyperparameter Tuning)
- 3.5Assignment :AI Data Science Using Databricks (40 Hours)-Module 33 Days
- 3.6Quiz:AI Data Science Using Databricks (40 Hours)-Module 35 Questions
- Module 4:Clustering & Dimensionality Reduction4
- Module 5:Model Deployment & MLOps Basics4
- Module 6 : Deep Learning EssentialsDeep Learning Essentials3
- Module 7 ::Generative AI Basics3
- Module 8 :Databricks for Machine Learning2
- Module 9:Certification Prep & Capstone0
Databricks ML flow & Auto ML Basics
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