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Introduction to AI - Machine Learning Basics

A basic session outlining the field of Artificial Intelligence to give the participants a solid demonstration of State-of-the-art Artificial Intelligence especially Natural Language Processing.


This will serve as an inspiration for those who want to pursue this field and paint with broad strokes for those who want to casually know basic concepts. This is the first part of the series and will cover the basics required for the rest of the sessions. The long term goal being understanding the above topicstheoretically and practically.
 

The demonstrations will contain:

  • Language Translations using State-of-the-art models like Neural Machine Translation,

  • Transformers

  • Speech recognition systems

  • Machine Comprehension

  • QA done by State-of-the-art models like Transformers, BERT, GPT-1.


The rest of the training is divided between:
Engineering (building data processing pipeline from scratch) and
Algorithms (Linear and Logistic Regression).

 

Best practices in machine learning (bias/variance theory) innovation process in machine learning and AI and practical examples to take home and practice.

Key skills covered:

  • Measuring and Tuning performance of ML algorithms

  • Most effective machine learning techniques

  • Use tools like Scikit for ML tasks

  • Best practices in innovation as it pertains to machine learning and AI

  • You'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems

  • You will learn how to Prototype and then productionize

Who should attend:

  • Data Scientists

  • Software Engineers

  • AI aspirants from across domains

  • Engineering Freshers who want to make a career in AI

Key skills covered:

  • This is an instructor led course provides lecture topics and the practical application of 

  • Machine Learning and the underlying technologies. It pictorially presents most

  • concepts and there is a detailed case study that strings together the technologies,

  • patterns and design.

Pre-requisites:

  • A familiarity with Probability Theory, Calculus, Linear Algebra and Statistics is required

  • Working Knowledge of Python

  • Experience in Programming

  • An understanding of Intro to Statistics would be helpful.

  • Workking knowledge of statistics is an added bonus

  • SOFTWARE
    Provide training and consulting on AI and more specifically on Deep Learning, and can work with your in-house team. Also we work in DevOps mode if needed to design, develop and maintain the AI solution. We deliver value by understanding your use cases and providing end to end strategy and implementation. It is very important to use open source tools and stay away from proprietary AI platforms. AI is still a moving target. By locking in to an AI platform, you risk wasting a lot of time and resources when new AI technologies appear. We only use open source tools and help you build AI and data science platforms which are platform agnostic and easy to maintain and support. Having done extreme optimizations(fine-tuning) and research on NMT/BERT/XLNET based architectures we are positioned uniquely to the following. Install, configure and optimize vanilla(google's) NMT/BERT/XLNET at your on-premise/cloud hardware Install Stillwater's pretrained and more finely tuned NMT/BERT/XLNET on your on-premise/cloud hardware Building AI platform for an Investment Banking Major for Automated Trading and Operational Optimization(Automated L1/L2/L3 support) using the above technology stack.
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