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Hands-on Supervised Machine Learning with Python [Video]
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First Step Towards Supervised Learning
- The Course Overview
- Getting Our Machine Learning Environment Setup
- Supervised Learning
- Hill Climbing and Loss Functions
- Model Evaluation and Data Splitting
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Implementing Parametric Models
- Introduction to Parametric Models and Linear Regression
- Implementing Linear Regression from Scratch
- Introduction to Logistic Regression Models
- Implementing Logistic Regression from Scratch
- Parametric Models –Pros/Cons
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Working with Non-Parametric Models
- The Bias/Variance Trade-off
- Introduction to Non-Parametric Models and Decision Trees
- Decision Trees
- Implementing a Decision Tree from Scratch
- Various Clustering Methods
- Implementing K-Nearest Neighbors from Scratch
- Non-Parametric Models –Pros/Cons
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Advanced Topics in Supervised ML
- Recommender Systems and an Introduction to Collaborative Filtering
- Matrix Factorization
- Matrix Factorization in Python
- Content-Based Filtering
- Neural Networks and Deep Learning
- Neural Networks
- Use Transfer Learning