Day 1

Registration
9.00 AM -9.30 AM
Session 1
Motivation
Foundations and Terminology of Data Science
Machine Learning
Deep Learning

Session 2
• Introductions
• Why take a look under the hood?
• Data science in clear terms
• A pragmatic definition
• The OSEMN model
• Data science Venn diagram
• Overview of tools, platforms, and languages
• Why use a programming language?
• A crash course in Python programming
• Python Programming
• Basic overview of Python
• Basic Syntax of Python
• List, Tuple, and String, etc.
• Extracting and reading data from a file and URL
• Using GitHub to commit your code
• Variables
• Data frames
• Functions

SESSION 3
• Graph plotting and Jupyter Notebook Management
• ML program with Hello world (iris dataset)
• Role of Classifier and Algorithm
• Deep dive with Decision Tree Algorithm with custom data

DAY 2

SESSION 1
• Supervised Machine learning real-world Implementation:
• Implementing KNN, Naive Bayes Algorithm
• ML using data sets
• Analysis of Real-world data (iris, Diabetes, Digits, and Cancer)
• UCI datasets
• Uses of python scikit-learn, tensorflow library
• Workflow and data graphs
• Introduction to Image processing with ML:
• Understanding of Image Processing and uploading an image with python
• Creation of blank images with OpenCV and Numpy
• Threshold of images and other examples
• Linear regression for flat price and digital marketing cost prediction
• Linear Regression, Logistic Regression Gradient Descent

SESSION 2
• Face Detection with ML
• Real-time webcam face detection program
• Loading image and training it against cloud provider API
• Karios and facetime real-time image detection

SESSION 3
• Loading face and detecting it
• Using face detection to view security parameter
• OpenCV image processing model use cases