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Machine Learning Git Codebook

Lesson 1 : Introduction to Numpy (Video)
Lesson 2 : Data Wrangling using Pandas
Lesson 3 : Plotting in Python
Lesson 4 : Linear Models for Regression & Classification
Lesson 5 : Preprocessing Data
Lesson 6 : Decision Trees
Lesson 7 : Naive Bayes
Lesson 8 : Composite Estimators
Lesson 9 : Model Selection and Evaluation
Lesson 10 : Feature Selection Techniques
Lesson 11 : Nearest Neighbors
Lesson 12 : Clustering Techniques
Lesson 13 : Anomaly Detection
Lesson 14 : Support Vector Machines
Lesson 15 : Dealing with Imbalanced Classes
Lesson 16 : Ensemble Methods

Case Study of Classic ML Problems

Case 1 : Linear Regression
Case 2 : Cancer Prediction
Case 3 : Online Learning
Case 4 : Customer Churn Prediction
Case 5 : Income Prediction
Case 6 : Predicting Employee Exit
Case 7 : Face Generation
Case 8 : Finding Similar Houses

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AI Project - Web application for Object Identification - https://www.edyoda.com/course/1185

Numpy - https://www.edyoda.com/course/1263

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Introduction to Neural Nets - https://www.edyoda.com/channel/2500/

Videos from deep cognition studio - https://www.edyoda.com/channel/2380/

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