LIVE TRAINING

SAVE THE DATE: October 6th, 2021 @12pm EST

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Ankur is an applied machine learning specialist in both unsupervised learning and natural language processing, and he is the author of Hands-on Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data and Applied Natural Language Processing in the Enterprise: Teaching Machines to Read, Write, and Understand.

LIVE TRAINING: Introduction to PYTHON for Programming 

October 12th @12 PM EST

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Price: $147

Regular price $210, discounted 10%
  • 4 hour immersive session

  • Hands-on training with Q&A

  • Recording available on-demand

  • Certification of Completion

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Subscribe and get an additional 10% to 35% off ALL live training session
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Pricing: $189

Regular price $210, discounted 10%
  • 3 hour immersive session

  • Hands-on training with Q&A

  • Recording available on-demand

  • Certification of Completion

10% Discount Ends in:

Subscribe and get an additional 10% to 35% off ALL live training session
View Plans

Meet Your Instructor

Ankur Patel 

Ankur Patel is the co-founder & Head of Data at Glean, an AI-powered spend intelligence solution for managing vendor spend, and the co-founder of Mellow, a fully managed machine learning platform for SMBs. Prior to founding Glean and Mellow, Ankur led data science and machine learning teams at several startups including 7Park Data, ThetaRay, and R-Squared Macro and was the lead emerging markets trader at Bridgewater Associates. He is a graduate of Princeton University and currently resides in New York City.

Course Overview

What’s the plan? 

This hands-on course is organized into four lessons. In lesson one, we will provide an introduction to NLP, reviewing its evolution over the past 70 years. We will explain why NLP matters and how it powers many of the most popular applications we use every day. We will also perform basic NLP tasks using one of the most popular open-source NLP libraries today: spaCy.

In lesson two, we will introduce two more popular open-source NLP libraries (fast.ai and Hugging Face) and perform state-of-the-art NLP. We will develop a sentiment analysis model for IMDb movie reviews. We will also cover modern NLP concepts such as attention mechanisms, transformers, pretrained language models, transfer learning, and fine-tuning.

In lesson three, we will retrace how NLP advanced over the last decade and experienced its breakout moment in 2018. Since 2018, NLP has soared in popularity among companies and has become a mainstream topic of interest. After we cover the theory, we will discuss modern NLP tasks such as sequence classification, question answering, language modeling, text generation, named entity recognition, summarization, and translation.

In lesson four, we will put this theory to practice and develop our own named entity recognition and text classification models using spaCy, including annotating our data using an annotation platform called Prodigy. We will draw on what we’ve learned to perform transfer learning and fine-tuning, and we will compare the fine-tuned model’s performance against an out-of-the-box named entity recognition model.

By the end of this course, you should have a good understanding of the fundamental concepts in NLP, both in theory and in practice.

Course Outline

What’s the plan? 

Matt Harrison has been working with Python and data since 2000. He has a computer science degree from Stanford. He has worked at many amazing companies, created cool products, wrote a couple books, and taught thousands Python and Data Science. Currently, he is working as a corporate trainer, author, and consultant through his company Metasnake, which provides consulting and teaches corporations how to be effective with Python and data science.

Course Overview

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Course Outline

Module 1:
Introduction to NLP

  • What is NLP and history of NLP over past 70 years
  • Popular NLP applications today
  • Introduce first modern open-source NLP software library: spaCy
  • Perform basic NLP tasks using spaCy: tokenization, part-of-speech tagging, dependency parsing, chunking, lemmatization, and stemming

Module 2:
State-of-the-Art (SOTA) NLP

  • Introduce two more modern open-source NLP software libraries: fast.ai and Hugging Face
  • Discuss attention mechanisms, transformers, pretrained language models, transfer learning, and fine-tuning
  • Build NLP application (IMDb movie review sentiment analysis) using fast.ai

Module 3:
Progress in NLP over the past 10 years

  • The path to NLP’s watershed “ImageNet” moment in 2018
  • Word embeddings: one-hot encoding, word2vec, GloVe, fastText, and context-aware pretrained word embeddings
  • Sequential models: vanilla recurrent neural networks (RNNs), long short-term memory (LSTMs), and gated recurrent units (GRUs)
  • Attention mechanisms and transformers
  • ULMFiT, ELMo, BERT, BERTology, GPT-1, GPT-2, and GPT-3
  • Introduction to common NLP tasks via Hugging Face: sequence classification, question answering, language modeling, text generation, named entity recognition, summarization, and translation

Module 4:
Named entity recognition and text classification applications

  • Explore dataset: AG news dataset
  • Application #1: Named Entity Recognition (NER)
  • Perform inference using out-of-the-box spaCy NER model
  • Annotate data using Prodigy
  • Develop custom named entity recognition model using spaCy
  • Compare custom NER model against the out-of-the-box spaCy NER model
  • Application #2: Text Classification
  • Annotate data using Prodigy
  • Develop text classification model using spaCy
  • Lists

  • Slicing

  • Dictionaries

  • Comprehensions

  • Exercise

Key on emojidex Key Details

DATE

TIME:

DURATION:

LEVEL:

OCTOBER 6TH, 2021

TIME: 12 PM EST, 9 AM PST

3 HOURS

INTERMEDIATE

Prerequisites

  • Python coding experience
  • Familiarity with pandas, numpy, and scikit-learn
  • Understanding of basic machine learning concepts, including supervised learning
  • Experience with deep learning and frameworks such as TensorFlow or PyTorch is a plus

 

Upcoming Live Training

October 12th

Introduction to Python Programming

In this live training session you will understand object model of Python and get familiar with basic data structures. You will also build functions and classes to aid in data science work in this 4-hour, hands-on workshop.

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