Deep learning is a new “superpower” that will let you build AI systems that just weren’t possible a few years ago.

This Deep Learning & Machine Learning Workshop teaches how Deep Learning actually works. So after taking it, participants will be able to understand how to apply deep learning to their own applications.

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this workshop, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

Deep Learning & Machine Learning Workshop Goals

Understanding of Data Science.

Machine Learning Problem Statement oriented approach.

Deep understanding of the supervised & unsupervised model of ML.

Regression and classification differentiation.

Understanding of UCI dataset and loading them into an appropriate framework.

Image processing & Face detection understanding & implementation.

Deep Learning & Machine Learning Workshop OVERVIEW – 2 DAYS


Machine Learning train your computer

Machine Learning & Deep Learning High-level Introduction

  • What is Machine learning
  • Deep understanding the importance of Machine Learning
  • Understanding of Data Science and Machine Learning together
  • Supervised, Unsupervised and Reinforcement Learning
  • How Big Data can be useful for ML
  • Introduction to Linux Kernel and RedHat Enterprise Linux with Ubuntu for ML
  • Importance of Linux for ML
  • Role of Python, R and Java programming.

Python Programming

  • Basic overview of Python
  • Basics of R
  • Basic Syntax of Python
  • List, Tuple and String etc.
  • Extracting and reading data from a file and URL
  • Using GitHub to commit your code


Getting started with Algo’s and Real Use Cases

Supervised Learning Implementation:

  • Using Numpy and pandas, matplotlib and many more libraries
  • Graph plotting and Jupyter Notebook Management
  • ML program with Hello world
  • Role of Classifier and Algorithm
  • Deep dive with Decision Tree Algorithm with custom data


Supervised Machine learning real-world Implementation:

  • Implementing KNN, Naive Bayes Algorithm
  • ML using data sets
  • Introduction of Azure ML platform and its uses
  • 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


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
  • Loading face and detecting it
  • Using face detection to view security parameter
  • OpenCV image processing model use cases
  • Linear regression for stock price and digital marketing cost prediction


  • Students.
  • College Faculty Members.
  • Any Technical Person.
  • Corporate Employees.


  • Certificate of Participation.
  • Certificate of Participation for Faculty Coordinators.


  • A Seminar hall/Classroom/Auditorium/Computer lab/Personal Laptops with a capacity to conduct hands-on session.
  • A computer lab with the below-given configuration:
  • Lab Computer systems should have at least – 4GB RAM / Intel i3 / i5 Processor / 100 GB Free Storage capacity.
  • Ubuntu OS (version 16.04) needs to be installed into the systems of the lab.
  • We suggest keeping a common Username & Password for all system post installation of Ubuntu OS.
  • Availability of 1 LAN wire/wireless network to connect all lab systems with Instructor’s Machine/Laptop.
  • A projector to connect with Instructor’s Machine/Laptop.
  • All systems must have an active Internet Connection.
  • One good quality cordless mic.
  • One small Stereo jack cord to connect in laptop for its sound system.
  • Students can carry Personal laptops with Ubuntu OS (latest version) installed in it.
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