Online Training on Machine Learning Using Python

This training is from 24-Aug-2020 to 02-Sept-2020 total of 2hr/day (04:00 PM to 06:00 PM) for 9days. This repository consists of all the files, resources, and recorded session links which are discussed during Machine Learning using Python Online Training.

Note: Updated video Links they may expired at anytime if needed download ASAP

APSSDC-ML-Datasets → [Click Here]

Few resources avaliable @ [resources.md] file don't forget to use them

Instructions for attendance

Everyone should compulsory follow the below instruction in order to get the attendance --> Certificate

1. Login format rollnumber-name-college
2. Don't give spaces in roll number or shorcut of your roll number
3. Don't give spaces between rollnumber and name (only - single minus or hyphen character)
4. Make sure roll number should match with the registered roll number
5. Minimum 90/120 minutes should attend in 120/150 minutes session with same login format

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Day01 Introduction to Machine Learning (24-Aug-2020)

Discussed Concepts:

1. Introduction to Machine Learning
2. Machine Learning Classification

[Day01_Slides]

[Day01_Recorded_Video]

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Day02 Sklearn Package, and Linear Regression using Machine Learning (25-Aug-2020)

Discussed Concepts:

1. Day2 Agenda
2. Classification of ML
3. Classification of Supervised ML
4. Types of Algorithms
5. Types of data based on structure and according to statistics
6. What is regression
7. Types of regressions
8. Linear regression with one variable
9. Created ML model to predict the salary based on YearsOfExperience
10. Evaluated the Model

Day02 Jupyter Notebook [.ipynb format], [.pdf format]

[Day02_Slides]

[Day02_Recorded_Video]

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Day03 Multi Linear Regression & Polynomial Regression (05-Aug-2020)

Discussed Concepts:

1. Multi Linear Regression to predict CO2 Emissions Dataset
2. Non Linear regression
3. Polynomial Regression to predict CO2 Emissions Dataset

Day03_Multi_Linear_and_Polynomial_Regression_Jupyter_Notebook [.ipynb format], [.pdf format]

Day03_PolyNomial_Regression_Functions [.ipynb format], [.pdf format]

[Day03_Slides]

[Day_03_Recorded_Video]

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Day04(27-08-2020)

Topics Covered

• Introduction to Classification
• Types of Classification
• KNN(k-Nearest Neighbours) Algorithm

Materials

Click Here For Notes--> Class Jupyter Notebook
Click Here For Video --> Recorded Video
Classification PPT -->Click Here

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Day05(28-08-2020)

Topics Covered:

• Logistic Regression
• SVM (Support Vector Machine)

Materials

Click Here For Notes-->Class Jupyter Notebook
Click Here For Video -->Recorded Video

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Day-06(29-08-2020)

Covered Topics:

• Introduction to Decision Tree
• Terminology related to Decision Trees
• Types of Decision Trees
• Decision Trees Classifier

Materails

Class Jupyter Notebook -->Click Here
Class Recorded Video --> Click Here
Pdf uploaded in above folder

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Day-07(31-08-2020)

Topics Covered

• Introduction to DecisionTree Regressor
• DecisionTree Regressor Algorithm
• Random Forest

MAterials

Class Jupyter Notebook -->Click Here
Class Recorded Video --> Click Here
Pdf uploaded in above folder

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Day08 Unsupervised Learning and Clustering Introduction to Unsupervised Learning (01-Sept-2020)

1. Types of Unsupervised Learning
2. Introduction to clustering
3. Types of Clustering methods
4. KMeans Clustering
5. Applications

Day08 Jupyter Notebook [.ipynb format], [.pdf format]

[Day08_Slides]

[Day08_Recorded_Video]

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Day09 Dimensionality Reduction (02-Sept-2020)

Discussed Concepts:

1. Dimensionality reduction
2. Principal Component Analysis (PCA)

Day09 Jupyter Notebook [.ipynb format], [.pdf format]

[Day09_Slides]

[Day09_Recorded_Video]

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