Events

Session 1: Introduction to Machine Learning
Supervised learning

Unsupervised learning

Reinforcement learning

Machine learning using python

Machine learning using matlab

ML in local machine vs ML in cloud

Session 2: Introduction to Mathematical concepts and setup installation
White paper training for ML Algorithms

Package installation

Installing using distribution package tools

Installing using python installer

Installing from source

Setting up python environment

Scipy

Session3: Python- Hands on
Introduction to Python

Working with Variables in Python

Python's Lists & Tuples

Creating Python Functions

Modules & Packages installation

Session 4: Machine learning algorithms-Hands on
Linear regression with one variable

Linear regression with two variable

Naive Bayes classification

Coding with python(without packages)

Coding with python (with packages)

Session 5: Data Manipulation and visualization
real time data gathering

Visualizing the data using 2D models

Visualizing the data using 3D models

Overview of neural networks

Experimenting ML with Google platform for Intelligence vehicle

Technology(if camera is availed)

Session 6: K-Nearest Neighbours
K Nearest Neighbors Classification

K Nearest Neighbors Decision Boundary Value of 'K' Affects Decision Boundary

Measurement of Distance in KNN

- Euclidean Distance -Euclidean Distance (L2 Distance)

- Manhattan Distance (L1 or City Block Distance)

Scale is Important for Distance Measurement (Feature Scaling)

Comparison of Feature Scaling Methods

Feature Scaling: The Syntax

Multiclass KNN Decision Boundary

Regression with KNN

Characteristics of a KNN Model

K Nearest Neighbors: The Syntax

Session 7 : Model Generation For Medical Dataset
K Value Affects Decision Boundary

Choosing Between Different Complexities

How Well Does the Model Generalize?

Under fitting vs Over fitting

Bias—Variance Trade-off - Training and Test Splits

Using Training and Test Data

Fitting Training and Test Data

Train and Test Splitting: The Syntax

Beyond a Single Test Set: Cross Validation (Training split, Test Split etc.)

Model Complexity vs Error - Cross Validation