Invitation to Join Weekend Program

Machine Learning / Artificial Intelligence

(Certification Program)
(Deep Learning and Data Science)

4-days / 30+ Hours


Program Details

We are scheduling a 30+ hours hands-on workshop for Advanced Data Science, Machine Learning and Deep Learning Workshop at Ahmadabad.

Workshop Coach

NIKHIL SHAH: https://in.linkedin.com/in/shahnikhil
ANUBHAV CHATURVEDI: https://in.linkedin.com/in/anubhav-chaturvedi-a7465a72


WHO SHOULD ATTEND THE WORKSHOP
1) Students./ MSc.IT/MCA/BE/Masters/PhD.
2) College Faculty Members.
3) Software/IT Professionals
4) MBA, CA, other Technical Person can join for the first day for Tech and Market overview and foundations. We will be covering case studies from various sectors


WORKSHOP REQUIREMENTS
Computer systems should have at least – 4GB RAM / Intel i3 / i5 Processor / 100 GB Free Storage capacity


WORKSHOP FEES:
8499 / - Inclusive of all taxes
Registration Fee: 1000 INR to reserve the seat (non-refundable) -
Register here https://pages.razorpay.com/pl_Cx2pPvwuNPRcP0/view

Remaining Fees on the first day of the workshop (7499 INR) - Check Payable to "Vnuture Technologies" / or Pay Online
BANK DETAIL:
Kotak Mahindra Bank
Vnurture Technologies
Account No.:7511675161
IFSC :KKBK0002570
Branch: Prernatirth Derasar,Paldi,Ahmedabad.





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

Day 3

Session 1
• Convolutional Neural Network
• Computer Vision with CNNs

Session 2
• Natural Language Processing
• Virtual agents and chatbots creation
• Word embeddings and vectors
• Time Series Analysis
• Recurrent Neural Networks & LSTM
• Hands-On Labs

Session 3
• Assessment
• Recurrent Neural Network
• Sequence Modelling with RNNs, generative modelling in Deep Learning
• Convolutional Neural Networks
• Basics on Convolution
• Filter, 2D,3D convolution, Max Pooling
• CNN Architectures

DAY 4

SESSION 1
• Image Classification with DIGITS (lab)
• Deep Neural Networks with Keras
• Deep Learning Hacks

SESSION 2
• System/project level tricks and regularization strategies
• Building Deep Models and Hyperparameter Tuning
• Deep learning with TensorFlow

SESSION 3
• Object Detection with DIGITS (lab)
• Python Notebook on CNN Transfer Learning and Auto Encoders (lab)
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