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AI Intermediate: Machine Learning Internals and Basic Natural Language Processing

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. This training session provides a deep dive into machine learning, data mining, and statistical pattern recognition.
 

The demonstrations will contain:

  • Supervised learning (parametric/non-parametric algorithms, support vector machines,
    kernels). 

  • Unsupervised learning (clustering, dimensionality reduction, recommender systems). 

  • Deep dive into ARIMA based models for Time-series data.
     

The final part will completely focus on Natural Language-based
case studies and the models used for that. 

First Case study is Parts of Speech tagging and the second one being Recognizing Spoken words using probability-based Hidden Markov Model.

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 Scientist

  • People who want to take their skills to the next level especially to State-of-the-art NLP

  • Software Engineers

Key skills:

  • Solid foundation of some of the unsupervised learning Algorithms( PCA over covariance, PCA over SVD, Clustering(Kmean, Hierarchical, DBSCAN))

  • Solid foundation of probabilistic Techniques especially Hidden Markov models and their use in Natural Language Processing(NLP)

  • Solid foundation of some of the supervised learning Algorithms( ARIMA, Random Forest, Descision Trees)

  • Solid foundation of the basic Engineering that goes behind Machine Learning.

  • An idea of what State-of-the-art Artificial Intelligence can achieve

Pre-requisites:

Introduction to AI: Machine Learning Basics

  • 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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