Starweaver

Segment - 01 - What is machine Learning?

Segment - 02 - For Mac Users

Segment - 03 - Introduction to Python

Segment - 04 - IPython in Browser

Segment - 05 - Python Data science to be used

Segment - 06 - What are Pandas?

Segment - 07 - Read in Data from CSV

Segment - 08 - Read in Online CSV

Segment - 09 - Read in Excel Data

Segment - 10 - Read in HTML Data

Segment - 11 - Read in Data from Databases

Segment - 12 - Remove Missing Values

Segment - 13 - Conditional Data Selection

Segment - 14 - Data Grouping

Segment - 15 - Data Subsetting

Segment - 16 - Ranking & Sorting

Segment - 17 - Concatenate

Segment - 18 - Merging & Joining Data Frames

Segment - 19 - Unsupervised Classification- Some Basic Concepts

Segment - 20 - K-Means Clustering: Theory

Segment - 21 - Implement K-Means on the Iris Data

Segment - 22 - Quantifying K-Means Clustering Performance

Segment - 23 - How To Select the Optimal Number of Clusters?

Segment - 24 - Hierarchical Clustering - Theory

Segment - 25 - Hierarchical Clustering-practical

Segment - 26 - Principal Component Analysis (PCA) - Theory

Segment - 27 - Principal Component Analysis (PCA)-Case Study 1

Segment - 28 - Principal Component Analysis (PCA)-Case Study 2

Segment - 29 - Linear Discriminant Analysis(LDA) for Dimension Reduction

Segment - 30 - T-SNE Dimension Reduction

Segment - 31 - Feature Selection to Select the Most Relevant Predictors

Segment - 32 - Recursive Feature Elimination (RFE)

Segment - 33 - Concepts Behind Supervised Learning

Segment - 34 - Data Preparation for Supervised Learning

Segment - 35 - Pointers on Evaluating the Accuracy of Classification Modelling

Segment - 36 - Using Logistic Regression as a Classification Model

Segment - 37 - KNN - Classification

Segment - 38 - Naive Bayes Classification

Segment - 39 - Linear Discriminant Analysis

Segment - 40 - SVM- Linear Classification

Segment - 41 - Non-Linear SVM Classification