Computer vision is a branch of computer science which deals with recognizing objects, people and identifying patterns in visuals. It is basically analogous to the vision of an animal.

The agenda for this meetup is:

1. Overview of Machine Learning
2. Basics of Deep Learning
3. What is computer vision and its use-cases?
4. Various algorithms used in Computer Vision (mostly CNN)
5. Live hands-on demo of either Auto Cameraman or Face recognition system
6. What next?

Eligibility:

You should have some basic idea of machine learning. This webinar will be useful for those who are working in software development and are trying to understand the fundamentals as well as go hands-on with computer vision.

My suggestion is that even if you are a beginner, do join this session. I assure you that you won't be disappointed.

This session is mostly going to be online. So, you can ask all your friends to join from across the globe. We will be sharing the link to the meeting before the session.

There is massive growth in the big data space, and job opportunities are skyrocketing, making this the perfect time to launch your career in this space. CloudxLab is pleased to announce its free webinar on Big Data with Spark & Scala, to help you learn from Industry Experts with more than 15 years of experience.

Topics to be covered during the webinar

• Big Data & its importance.

• Big Data - examples and applications

• Apache Spark and its Components - Overview

• Hands-on demo on cloud-based lab

  Keynotes

• This is the first and the only free session of our Instructor-Led Course on Big Data with Spark & Scala. For more details, please click here

Artificial Intelligence has a significant presence in top emerging jobs in today's era. Its one of the fastest growing skills and has become inevitable, so taking a step ahead CloudxLab welcomes you to its Free Webinar on Introduction to Machine Learning Process (with Q&A Session) on 18 May'2019 with our Industry Experts.

Topics to be covered in the Webinar -

• Introduction to Artificial Intelligence

• Introduction to Machine Learning & Deep Learning

  • Approach
  • Difference between Machine Learning approach & Traditional approach
  • Types
  • Identify and understand the various types of Machine Learning problems
  • Basics
  • Learning the process of a typical Machine Learning project
  • Training and testing of ML model
  • Various challenges and the common pitfalls in splitting the data, including many biases involved.
• Representing your data
  • How to represent the data for the consumption of an algorithm
• Overfitting & Underfitting
  • To identify not-learning and too much learning
  • Difference between bias and variance
• Q&A session

Keynotes

• This is the first and the only free session of our Instructor Led Live Q&A training for AI for Managers course on CloudxLab, for more details, please click here

You will learn the below topics

• Introduction to Deep Learning
• Deep Learning Applications
• Introduction to Artificial Intelligence
• Getting started with TensorFlow
• Hands-on examples on TensorFlow

What is Big Data & why is it important

Big Data - examples and applications

Introduction to Apache Spark and its components

Hands-on demo on cloud-based lab

Let's meet up for a fireside chat on Introduction and Careers in AI at WeWork Prestige Atlanta. This meetup is all about Artificial Intelligence. The agenda of the meetup is:

1. Networking
2. Why AI and How to get Started?
3. Careers in AI - Questions & Discussion

Please register yourselves at https://goo.gl/pH2wbZ

Here's what you'll learn

• What is Big Data & why is it important

• Big Data - examples and applications

• Hadoop ecosystem & details of various components

• Hands-on with the components in Hadoop

• Understanding the Spark architecture

• CloudxLab hands-on demo

Here's what you'll learn

Overview of Python for Machine Learning

Introduction to Machine Learning

Machine Learning - Examples

Machine Learning - Applications

Hands-on Demo on Machine Learning

What is Big Data & why is it important

Big Data - examples and applications

Introduction to Apache Spark and its components

Hands-on demo on cloud-based lab

There shall be an online test for 1.5 hours which would consist of objective questions based on mathematics required for Machine Learning.

• Topics that it will cover are linear algebra, probability theory, statistics, multivariable calculus, algorithms, time complexity, aptitude and Data Interpretation,

• Number of questions will be 25,

• Each question will have a weightage equal to the score it is for,

• No negative marking, and

• The test link will be sent only 30 minutes before the scheduled start time.

Here's what you'll learn

• What is Big Data & why is it important

• Big Data - examples and applications

• Hadoop ecosystem & details of various components

• Hands-on with the components in Hadoop

• Understanding the Spark architecture

• CloudxLab hands-on demo

Here's what you'll learn

• What is Big Data & why is it important

• Big Data - examples and applications

• Hadoop ecosystem & details of various components

• Hands-on with the components in Hadoop

• Understanding the Spark architecture

• CloudxLab hands-on demo

You will learn the below topics

• Introduction to Deep Learning
• Deep Learning Applications
• Introduction to Artificial Intelligence
• Getting started with TensorFlow
• Hands-on examples on TensorFlow

Here's what you'll learn

What is Big Data & why is it important

Big Data - examples and applications

Introduction to Apache Spark and its components

Hands-on demo on cloud-based lab

Here's what you'll learn

Overview of Python for Machine Learning

Introduction to Machine Learning

Machine Learning - Examples

Machine Learning - Applications

Hands-on Demo on Machine Learning

Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment.

Sandeep will provide an introduction to Deep Learning, core technologies, methodologies and approach on how Deep Learning can enable IoT applications.

There shall be an online test for 1.5 hours which would consist of objective questions based on mathematics required for Machine Learning.

• Topics that it will cover are linear algebra, probability theory, statistics, multivariable calculus, algorithms, time complexity, aptitude and Data Interpretation,

• Number of questions will be 25,

• Each question will have a weightage equal to the score it is for,

• No negative marking, and

• The test link will be sent only 30 minutes before the scheduled start time.

This session would broadly cover the following:

• Introduction to Machine Learning and AI
• What is Classification?
• Hands-on example on binary classification using MNIST dataset
• Understanding Stochastic Gradient Descent (SGD) Classifier
• Training a binary classifier using SGD

• Performance measure

  • Cross Validation
  • Confusion Matrix
  • Precision and Recall
  • F1 Score
  • Precision / Recall Tradeoff
  • ROC Curve

This session would broadly cover the following:

• End-to-end Machine Learning project
• This bootcamp is designed to be an entirely hands-on experience for you.

In this workshop, you will be introduced to six modules. Each module introduces one or two core Machine Learning concepts while working through the practical implementation.

• Overview of Machine Learning

• Manipulation and plotting data

• Preprocessing and exploring data

• Training a binary classifier

• Measuring Performance

• Learning Multi-class classification