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Curriculum
- 10 Sections
- 96 Lessons
- 10 Weeks
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- LINEAR REGRESSION13
- Multiple Linear Regression12
- 2.1Introduction to Multiple Linear Regression
- 2.2StreetEasy Dataset
- 2.3Multiple Linear Regression: Scikit-Learn
- 2.4Training Set vs. Test Set
- 2.5Visualizing Results with Matplotlib
- 2.6Visualizing Results with Matplotlib
- 2.7Multiple Linear Regression Equation
- 2.8Multiple Linear Regression Equation
- 2.9Correlations
- 2.10Evaluating the Model’s Accuracy
- 2.11Rebuild the Model
- 2.12Review
- Understanding Polynomial Regression Model10
- 3.1What is Polynomial Regression?
- 3.2Simple Math to Understand Polynomial Regression
- 3.3Linear Regression vs Polynomial Regression
- 3.4Non-linear data in Polynomial Regression
- 3.5Overfitting vs Under-fitting
- 3.6Bias vs Variance Tradeoff
- 3.7Loss and Cost Function – Polynomial Regression
- 3.8Gradient Descent – Polynomial Regression
- 3.9Practical Application of Polynomial Regression
- 3.10Application of Polynomial Regression
- k-nearest neighbors (KNN)13
- 4.1K-Nearest Neighbors Classifier
- 4.2Introduction
- 4.3Distance Between Points – 2D
- 4.4Distance Between Points – 3D
- 4.5Data with Different Scales: Normalization
- 4.6Finding the Nearest Neighbors
- 4.7Count Neighbors
- 4.8Classify Your Favorite Movie
- 4.9Training and Validation Sets
- 4.10Choosing K
- 4.11Graph of K
- 4.12Using sklearn
- 4.13Review
- k-nearest neighbors (KNN) distance-formula5
- DECISION TREES11
- Random Forests7
- support vector machines (SVM)10
- 8.1Introduction
- 8.2What is a Support Vector Machine(SVM)?
- 8.3Logistic Regression vs Support Vector Machine (SVM)
- 8.4Types of Support Vector Machine (SVM) Algorithms
- 8.5Important Terms
- 8.6How Does Support Vector Machine Work?
- 8.7Mathematical Intuition Behind Support Vector Machine
- 8.8Margin in Support Vector Machine
- 8.9Optimization Function and its Constraints
- 8.10Implementation and hyperparameter tuning of Support Vector Machine in Python
- Naive Bayes Algorithms10
- Model evaluation5
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