Lessons For All Our Courses

01

September

Exploratory Data Analysis

01

September

Feature Scaling

01

September

Data Cleaning

01

September

Feature Engineering

01

September

Linear Regression Intro

01

September

Gradient Descent

01

September

Linear Regression + Correlation Methods

01

September

Linear Regression Implementation

01

September

Logistic Regression

01

September

KNN Overview

01

September

parametric vs non-parametric models

01

September

EDA on Iris Dataset

01

September

The KNN Intuition

Implement the KNN algorithm from scratch

01

September

Implement the KNN algorithm from scratch

Compare the result with the sklearn library

01

September

Compare the result with the sklearn library

Hyperparameter tuning using the cross-validation

01

September

Hyperparameter tuning using the cross-validation

01

September

The decision boundary visualization

01

September

Manhattan vs Euclidean Distance

01

September

Feature scaling in KNN

01

September

Curse of dimensionality

01

September

KNN use cases

01

September

KNN pros and cons

01

September

Decision Trees Section Overview

01

September

EDA on Adult Dataset

01

September

What is Entropy and Information Gain?

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