Digital Events

Ai4 trainings were designed specifically for individuals working within industry on real business problems. Explore our AI training curriculums for both technical & non-technical job roles.

Each course is not only an opportunity to learn from our top-tier instructors, but also to connect with the Ai4 community of professionals working to lead the AI revolution within industry.

See below for our 3-part AI training program for executives and managers! Each of the three courses are part of the same curriculum and designed to be taken in succession. You may purchase each course a-la-carte as well as opt out of either the beginner or even intermediate courses based on your own discretion.

Looking for our technical trainings for data scientists & engineers? Click here.
august 3-4, 2020 | 1:00pm EDT | $275

AI For Executives: Machine Learning Fluency

This introductory workshop will establish a baseline lexicon in machine learning that spans across industries. Upon completing the course, participants will be able to engage with their data science teams on a more substantive level — they will know how to ask the right questions and have the capacity to better understand the answers. Participants will learn various use cases for topic areas such as Machine Learning, Databases (SQL and NoSQL), and Artificial Intelligence. They will develop a deeper understanding of how machine learning teams clean, process, and visualize large data sets, as well as design and run A/B tests. By the end of this course, participants will gain best practices for building a data-driven organization and identifying key components that drive the success of machine learning projects.

what you'll learn
  • Engage with your data science teams on a more substantive level
  • Various use cases for topic areas such as Machine Learning, Databases (SQL and NoSQL), and Artificial Intelligence
  • Best practices for building a data-driven organization and identifying key components that drive the success of machine learning projects

Audience

Business executives & managers

Skill level

Beginner

Prerequisites
  • None
COURSE environment
  • Cloud-based lab environment will be provided to students, all you need is access to a computer

Andy Enkeboll is a data scientist and lead data science instructor with Flatiron School Enterprise, where he oversees delivery of data science reskilling and upskilling courses for Fortune 500 companies and universities. He received his undergraduate computer engineering degree from Vanderbilt University, a teaching certificate from Lipscomb University, and a master’s degree in data science and engineering from Columbia University. Prior to joining Flatiron School, Andy held data science and engineering roles at Venmo, SeatGeek, and Grow Progress.

august 3rd: 1:00-3:00PM
august 4th: 1:00-3:00PM

Statistics Overview
  • Statistical Significance
  • Hypothesis Testing
  • A/B Testing
Databases Overview
  • SQL vs. NoSQL
  • Data Warehouses & Data Lakes
  • Data Pipelines
  • What is “Big Data” and are you ready for it?
Machine Learning & Artificial Intelligence
  • “Regression” vs. “Classification”
  • Unsupervised learning (Clustering)
  • Metrics: How do I know when a model performs well?
  • Practical implications of deep learning and neural networks
august 5-7, 2020 | 1:00pm EDT | $425

AI For Executives: A/B Tests to Machine Learning Pipelines

This course builds on the baseline lexicon established in the beginner workshops and provides participants with the opportunity to create and run an actual statistical model: A/B Testing. Rather than just looking at slides on how machine learning works, participants will develop an understanding for these concepts by solving a real data-powered problem. Participants will complete their first A/B test and learn how to interpret the results, and how to know when the results are “too good to be true”. By experiencing this hands-on statistical project course participants will be able to ask better questions, not be tempted to peek at preemptive results, delve deeper into data analysis, and set far reaching goals for their teams.

what you'll learn
  • Ask better questions, delve deeper into data analysis, and set far reaching goals for their teams
  • Develop an understanding for ML concepts by solving a real machine learning problem
  • Create and run a machine learning algorithm for A/B Testing

Audience

Business executives & managers

Skill level

Intermediate

Prerequisites
  • Basic understanding of fundamental machine learning technology
self-check: Can you answer the following questions?
  • What is the difference between a production datastore and a data warehouse?
  • At what point should you stop an A/B test?
  • What is unsupervised learning? How is it different from supervised learning?
  • What makes deep learning "deep"?
  • What is a data pipeline? How is this related to a data lake?
COURSE environment
  • Cloud-based lab environment will be provided to students, all you need is access to a computer

Andy Enkeboll is a data scientist and lead data science instructor with Flatiron School Enterprise, where he oversees delivery of data science reskilling and upskilling courses for Fortune 500 companies and universities. He received his undergraduate computer engineering degree from Vanderbilt University, a teaching certificate from Lipscomb University, and a master’s degree in data science and engineering from Columbia University. Prior to joining Flatiron School, Andy held data science and engineering roles at Venmo, SeatGeek, and Grow Progress.

august 5th: 1:00-4:00PM
august 6th: 1:00-4:00PM
august 7th: 1:00-3:00PM

Intro to Python & Jupyter
  • Fundamentals of working in Jupyter Notebooks with Python
  • Overview of Python Data Science Ecosystem
Stats Refresher
  • Why stats matter in Data Science
  • Normal Distribution
  • p values
  • z-tests and t-tests
  • Introduction to SciPy’s stats module
Hypothesis Testing
  • Qualities of a good experiment
  • Using experiment statistics to accept or reject our hypotheses
  • Hands-on setup of our pipeline
A/B Test Engineering
  • Storing data in a database
  • Randomizing our audience and customers
  • Third-party tools that help us with this process
Results Analysis
  • Understanding elements of our results
    • Power
    • Effect Size
    • Statistical Significance
    • Reproducibility
  • Takeaways from our analysis
  • Productizing results
august 10-13, 2020 | 1:00pm EDT | $675

AI For Executives: ML Modeling with Industry Standard Tools

The goal of this course is to help participants dive deeper into key concepts in machine learning, providing them the opportunity to explore data and answer questions with machine learning algorithms. Participants that complete this course will have a solid foundational understanding of Relational Databases, programming with Python, and practical experience with popular machine learning libraries and frameworks such as Pandas and Scikit-learn. Participants will be able to write effective database queries, automatically categorize customer data, and run A/B tests. This course will enable company leaders to answer questions with data and communicate them effectively to technical and nontechnical stakeholders in a clear, concise manner.

what you'll learn
  • Explore data and answer questions with machine learning algorithms
  • Relational Databases, programming with Python, and practical experience with popular machine learning libraries and frameworks such as Pandas and SciKit Learn
  • Write effective database queries, automatically categorize customer data, and run A/B tests
  • Answer questions with data and communicate them effectively to technical and nontechnical stakeholders in a clear, concise manner

Audience

Business & data leaders

Skill level

Advanced

Prerequisites
  • Basic understanding of Python and basic statistical analysis
self-check: Can you answer the following questions?
  • If presented with a sentence like "The woman eats the apple", could you use Python to reverse the string ("elppa eht stae namow ehT")?
  • Could you do the same but only reversing the letters in each word? ("ehT namow stae eht elppa")
  • Can you turn the previous code into a function?
  • What statistical test would you use to confirm the results of an A/B test?  What about checking whether your customers from Alabama spend more or less than the rest of your customers?
  • Your engineers have created a recommendation system you want to implement in your product. At a high level, what needs to happen to get that model into production?
COURSE environment
  • Cloud-based lab environment will be provided to students, all you need is access to a computer

Andy Enkeboll is a data scientist and lead data science instructor with Flatiron School Enterprise, where he oversees delivery of data science reskilling and upskilling courses for Fortune 500 companies and universities. He received his undergraduate computer engineering degree from Vanderbilt University, a teaching certificate from Lipscomb University, and a master’s degree in data science and engineering from Columbia University. Prior to joining Flatiron School, Andy held data science and engineering roles at Venmo, SeatGeek, and Grow Progress.

august 10th: 1:00-5:00PM
august 11th: 1:00-5:00PM
august 12th: 1:00-5:00PM
august 13th: 1:00-5:00PM

Intermediate Python 
  • Review of more advanced Python topics
    • Function writing
    • Looping
    • Lambda functions
  • Intro to new modules
    • Numpy
    • Pandas
  • How Python packages fit into Data Science
Linear Regression
  • What is Linear Regression?
  • How to choose models
  • How to code Linear Regression models with both sklearn and statsmodels
  • How to interpret results from a regression
  • How to know if our model is good at what it claims to be good at
Logistic Regression
  • How to code a classification model using Logistic Regression in sklearn
  • Metrics for classification
    • Confusion Matrices
    • Accuracy/precision/recall/f1 score
    • ROC curves
    • AUC
Additional Classification models
  • Why classification is important
  • K Nearest Neighbors
  • Decision Trees
  • Random Forests
  • Support Vector Machines
Pipelines & Hyperparameter Tuning
  • Building a robust pipeline for selecting the best model
  • Tuning our models based on optimal hyperparameters
Unsupervised Learning
  • Clustering user data based on different clustering techniques
Recommendation Systems
  • Regression based recommender systems
  • Matrix decomposition recommender systems
Final Project
  • Putting it all together! Showcase a final project that generates insight out of customer data

See below for our 6-part AI training program for data scientists & engineers! Each of the six courses are part of the same curriculum and designed to be taken in succession. You may purchase each course a-la-carte as well as opt out of earlier courses based on your own experience and discretion.

july 27-30, 2020 | 1:00pm EDT | $429

Python Basics

Python has recently become the most popular language. It excels at data science, artificial intelligence, and other tasks but is also an outstanding language for web and service programming and general application development.

This course will help beginners to Python become comfortable with Language Basics and getting started with Python.

What you will learn

Introducing Python Language

Audience

Developers, Architects

Skill level

Introductory

Prerequisites
  • Some background with Unix or Linux including the command line
  • Some knowledge of a programming language such as Java, C#, Node.js, etc.
Lab environment
  • A reasonably modern laptop or desktop
  • Unrestricted connection to the Internet. Laptops with overly restrictive VPNs or firewalls may not work properly
  • Chrome browser
  • SSH client for your platform

 

Sujee Maniyam

Sujee Maniyam is a seasoned practitioner and founder of Elephant Scale.  He teaches and consults in AI (machine learning and deep learning) and Big Data  (Hadoop, Spark, NoSQL) and and Cloud technologies.

He is an open source contributor, author ( ‘Hadoop illuminated‘ and ‘HBase Design Patterns‘)  and speaker at conferences.  He also advises and mentors various companies and organizations.

Speaking : http://elephantscale.com/speaking/
Publications : http://sujee.net/books/

Mark Kerzner

Mark is an experienced, hands-on software architect, practicing and teaching AI, Machine Learning, Blockchain, Spark, Hadoop, NoSQL, and more. He worked in a variety of verticals (Hightech, Healthcare, O&G, Legal, Fintech). His classes are hands-on and draw heavily on his industry experience. Mark is certified in Google Cloud (GCP), Amazon (AWS), and Hadoop.

Mark is an author and maintainer for a popular open source project for lawyers and researchers, FreeEed, which deals with search and massive scalability.

july 27TH: 1:00-5:00PM 
july 28TH: 1:00-5:00PM 
july 29TH: 1:00-5:00PM 
july 30TH: 1:00-3:00PM 

Python Introduction
  • Installing Python
  • Python Versions
  • IDEs
  • Jupyter Notebook
Python Language Overview and First Steps
  • Data Types
  • NumPy
  • Packages
  • Pandas
Python OOP
  • Classes
  • Modules/Packages
  • Python Packages
  • Data Types
Pandas
  • DataFrames
  • Schema inferences
  • Data exploration
Python – DB Programming
  • Database Connectivity
  • Pandas and DB
  • ORM
Python – Web Programming
  • Python Web Frameworks
  • Flask
  • Restful API with Flask
august 3-6, 2020 | 1:00pm EDT | $429

Data Analytics with Python

Python has become a powerful language and environment for performing data science.   It combines a robust, object-oriented language with a powerful library of data science packages, such as numpy, scipy, matlibplot, scikit-learn, and pandas.  These tools together make python one of the best combinations of robust programming language together with great library support.

  • Quick Python primer
  • A quick primer on data science algorithms
  • NumPy
  • SciPy
  • Pandas
  • Scikit-learn

Audience

Data Analysts, Software Engineers, Data scientists

Skill level

Beginner to Intermediate

Prerequisites
  • Experience and background in software development.  Helpful to have some background in analytics or machine learning.
  • Some background in Python highly recommended though a brief intro is included.
Lab environment
  • Zero Install : There is no need to install Hadoop software on students’ machines! A lab environment in the cloud will be provided for students.
Students will need the following
  • A SSH client (Linux and Mac already have ssh clients, for Windows Putty is recommended)
  • A browser to access the cluster. We recommend Chrome browser

Sujee Maniyam

Sujee Maniyam is a seasoned practitioner and founder of Elephant Scale.  He teaches and consults in AI (machine learning and deep learning) and Big Data  (Hadoop, Spark, NoSQL) and and Cloud technologies.

He is an open source contributor, author ( ‘Hadoop illuminated‘ and ‘HBase Design Patterns‘)  and speaker at conferences.  He also advises and mentors various companies and organizations.

Speaking : http://elephantscale.com/speaking/
Publications : http://sujee.net/books/

Mark Kerzner

Mark is an experienced, hands-on software architect, practicing and teaching AI, Machine Learning, Blockchain, Spark, Hadoop, NoSQL, and more. He worked in a variety of verticals (Hightech, Healthcare, O&G, Legal, Fintech). His classes are hands-on and draw heavily on his industry experience. Mark is certified in Google Cloud (GCP), Amazon (AWS), and Hadoop.

Mark is an author and maintainer for a popular open source project for lawyers and researchers, FreeEed, which deals with search and massive scalability.

AUGUST 3rd: 1:00-5:00PM 
AUGUST 4th: 1:00-5:00PM 
AUGUST 5th: 1:00-5:00PM 
AUGUST 6th: 1:00-3:00PM 

Python language Overview
  • Basics of Python language
  • How to edit, run, and test python code
  • Introducing the Anaconda distribution of Python.
  • IDEs
  • Using Jupyter notebooks.
Pandas
  • Series and Dataframes
  • Loading data using Pandas
  • Labs
NumPy and SciPy
  • Arrays
  • Matrices
  • Linear Algebra
  • Labs
  • Visualizing data with matlibplot
Doing Data Science with Scikit-learn
  • Introducing Scikit-Learn
  • Clustering Data
  • Building a Classifier
Big Data With PySpark
  • Introduction to Spark and PySpark
  • Using the Spark framework for Big Data
  • Using MLLib or Data Science in PySpark
august 10-14, 2020 | 1:00pm EDT | $529

Machine Learning Essentials with Python

Machine Learning (ML) is changing the world. To use ML effectively, one needs to understand the algorithms and how to utilize them. This course provides an introduction to the most popular machine learning algorithms.

This course teaches doing Machine Learning using the popular SciKit-Learn package in Python language.

This course teaches Machine Learning from a practical perspective. In-depth coverage of Math / Stats is beyond the scope of this course.

What you will learn
  • Python and SciKit-Learn
  • ML Concepts
  • Regressions
    • Linear Regression
    • Logistic Regressions
  • Classifications
    • Naive Bayes
    • SVM
    • Decision Trees
    • Random Forest
  • Clustering algorithms (K-Means)
  • Principal Component Analysis (PCA)
  • Recommendations
Industry Use Cases Covered

Finance

  • Predicting house prices
  • Predicting loan defaults at Prosper
  • Predicting income from customs data

Healthcare

  • Predicting diabetes outcome

Customer service

  • Predicting customer turnover

Text analytics

  • Spam classification

Travel

  • Predicting Uber demand

Politics

  • Predicting election contributions

Recommendations

  • Predicting movie ratings
  • Recommending songs

Other

  • Predicting wine quality
  • Predicting college admissions

Audience

Data Analysts, Software Engineers, Data scientists

Skill level

Beginner to Intermediate

Prerequisites
  • Good programming background
  • Familiarity with Python would be a plus, but not required
  • No machine learning knowledge is assumed
Lab environment
  • Cloud based lab environment will be provided to students, no need to install anything on the laptop
Students will need the following
  • A reasonably modern laptop with unrestricted connection to the Internet. Laptops with overly restrictive VPNs or firewalls may not work properly
  • Chrome browser

Sujee Maniyam

Sujee Maniyam is a seasoned practitioner and founder of Elephant Scale.  He teaches and consults in AI (machine learning and deep learning) and Big Data  (Hadoop, Spark, NoSQL) and and Cloud technologies.

He is an open source contributor, author ( ‘Hadoop illuminated‘ and ‘HBase Design Patterns‘)  and speaker at conferences.  He also advises and mentors various companies and organizations.

Speaking : http://elephantscale.com/speaking/
Publications : http://sujee.net/books/

Mark Kerzner

Mark is an experienced, hands-on software architect, practicing and teaching AI, Machine Learning, Blockchain, Spark, Hadoop, NoSQL, and more. He worked in a variety of verticals (Hightech, Healthcare, O&G, Legal, Fintech). His classes are hands-on and draw heavily on his industry experience. Mark is certified in Google Cloud (GCP), Amazon (AWS), and Hadoop.

Mark is an author and maintainer for a popular open source project for lawyers and researchers, FreeEed, which deals with search and massive scalability.

AUGUST 10TH: 1:00-5:00PM
AUGUST 11TH: 1:00-5:00PM 
AUGUST 12TH: 1:00-5:00PM 
AUGUST 13TH: 1:00-5:00PM 
AUGUST 14TH: 1:00-5:00PM 
 

Python Basics
  • Introduction to Python programming environment
  • Introduction to Numpy and Pandas
  • Labs
    • Working with Jupyter notebooks
    • Numpy and Pandas
Machine Learning (ML) Overview
  • Machine Learning landscape
  • Understanding Deep Learning use cases
  • Understanding AI / Machine Learning / Deep Learning
  • Data and AI
  • AI vocabulary
  • Hardware and software ecosystem
  • Understanding types of Machine Learning (Supervised / Unsupervised / Reinforcement)
Python Scikit-Learn Library
  • Scikit-Learn library overview
  • Lab:
    • Scikit-Learn utilities
Feature Engineering and Exploratory Data Analysis (EDA)
  • Preparing data for ML
  • Statistics Primer
  • Data cleanup
  • Extracting features, enhancing data
  • Visualizing Data
  • Labs:
    • Data cleanup
    • Exploring data
    • Visualizing data
Machine Learning Concepts
  • Training and Testing
  • Gradient Descent
  • Overfitting / Under-fitting
  • Cross validation, bootstrapping
  • Confusion Matrix
  • ROC curve, Area Under Curve (AUC)
Linear regression
  • Linear Regression
  • Errors, Residuals
  • Multiple Linear Regression
  • Evaluating model performance
  • Labs:
    • Use case: House price estimates
Logistic Regression
  • Understanding Logistic Regression
  • Calculating Logistic Regression
  • Evaluating model performance
  • Labs:
    • Credit card application
    • college admissions
Classification: SVM (Supervised Vector Machines)
  • SVM concepts and theory
  • SVM with kernel
  • Labs:
    -Customer churn data
Classification: Decision Trees & Random Forests
  • Classification and Regression Trees (CART) introduction
  • Decision Tree concepts
  • Pruning trees
  • Gini index
  • Bias Variance Tradeoff
  • Random Forest concepts
  • Random Forests features and examples
  • Labs:
    • Predicting loan defaults
    • Estimating election contributions
Classification: Naive Bayes
  • Naive Bayes theory
  • Running Naive Bayes algorithm
  • Evaluating model performance
  • Lab
    • Spam filtering
Unsupervised Algorithms
  • Overview of unsupervised algorithms
  • Supervised vs. unsupervised
  • Understanding unsupervised algorithms
Unsupervised: Clustering: K-Means
  • Theory behind K-Means
  • Running K-Means algorithm
  • Estimating the performance
  • Labs:
    • Predicting Uber demand
    • Clustering shopping trips
Unsupervised: Principal Component Analysis (PCA)
  • Understanding dimensions
  • ‘Curse of dimensionality’
  • Reducing dimensions
  • Overview of Principal Component Analysis (PCA)
  • Eigen vectors and values
  • Implementing PCA algorithm
  • Labs:
    • Predicting wine quality
    • Predicting income from census data
Recommendations
  • Recommendation use cases
  • Recommender systems
  • Collaborative Filtering (CF)
  • Implementing CF algorithm
  • Lab:
    • Movie ratings recommendation
    • Songs rating recommendation
Final workshop (time permitting)
  • This is a group workshop
  • Each group will analyze a couple of real world datasets and run ML algorithms
  • Each group will present their findings to the class
AUGUST 24-28, 2020 | 1:00PM EDT | $529

Intro to Deep Learning With TensorFlow & Keras

The abundance of data and affordable cloud scale has led to an explosion of interest in Deep Learning. Google has open sourced a library called TensorFlow which has become the de-facto standard, allowing state-of-the-art machine learning done at scale, complete with GPU-based acceleration.

This course introduces Deep Learning concepts and TensorFlow and Keras libraries to students.

This course teaches Machine Learning from a practical perspective. In-depth coverage of Math / Stats is beyond the scope of this course.

What you will learn
  • Deep Learning concepts
  • TensorFlow and Keras
  • Create neural networks with Tensorflow and Keras
  • Learn to use tools like Tensorboard to help with training neural networks
  • We will build neural networks to solve the following problems
    • Regression
    • Classification
Industry Use Cases Covered

Finance

  • Predicting loan defaults at Prosper
  • Predicting house prices

Healthcare

  • Predicting diabetes outcome

Customer service

  • Predicting customer turnover

Computer vision

  • Various image analysis

Time series

  • Analyze stock behavior

Audience

Developers, Data analysts, data scientists

Skill level

Introductory to Intermediate

Prerequisites
  • Basic knowledge of Python language and Jupyter notebooks is assumed.
    • Even if you haven’t done any Python programming, Python is such an easy language to learn quickly. We will provide Python resources.
Lab environment
  • Cloud based lab environment will be provided to students, no need to install anything on the laptop
Students will need the following
  • A reasonably modern laptop with unrestricted connection to the Internet. Laptops with overly restrictive VPNs or firewalls may not work properly
  • Chrome browser

Mark Kerzner

Mark is an experienced, hands-on software architect, practicing and teaching AI, Machine Learning, Blockchain, Spark, Hadoop, NoSQL, and more. He worked in a variety of verticals (Hightech, Healthcare, O&G, Legal, Fintech). His classes are hands-on and draw heavily on his industry experience. Mark is certified in Google Cloud (GCP), Amazon (AWS), and Hadoop.

Mark is an author and maintainer for a popular open source project for lawyers and researchers, FreeEed, which deals with search and massive scalability.

August 24TH: 1:00-5:00PM
August 25TH: 1:00-5:00PM
August 26TH: 1:00-5:00PM
August 27TH: 1:00-5:00PM
August 28TH: 1:00-5:00PM

Section 1: Introduction to Deep Learning
  • Understanding Deep Learning use cases
  • Understanding AI / Machine Learning / Deep Learning
  • Data and AI
  • AI vocabulary
  • Hardware and software ecosystem
  • Understanding types of Machine Learning (Supervised / Unsupervised / Reinforcement)
Section 2: Introducing TensorFlow
  • TensorFlow intro
  • TensorFlow features
  • Execution graph
  • TensorFlow on GPU and TPU
  • TensorFlow API
  • Lab: Setting up and Running TensorFlow
Section 3: Introducing Keras
  • Keras Intro
  • Keras concepts (models, layers)
  • Using Keras API
  • Lab
Section 4: Deep Learning Concepts
  • Introducing Perceptrons
  • Linear Perceptrons
  • Activation Functions (Sigmoid, Tanh, Relu, Softmax)
  • Backpropagation
  • Optimizers (Gradient Descent, Adam, RMSProp)
  • Loss functions for regressions and classifications
  • Vanishing/exploding gradient problem
  • Lab: Tensorflow playground
Section 5: Feedforward Network
  • FFNN architecture
  • Input layer, output layer
  • Hidden layers and Deep neural networks
  • Sizing neural networks
  • Lab: Feedforward Neural Networks
Section 6: Computer Vision
  • Introducing Convolutional Neural Networks (CNN)
  • CNN architecture
  • CNN concepts
  • Lab: Image recognition using CNNs
Section 7: Recurrent Neural Networks
  • Introducing RNNs
  • RNN architecture
  • RNN concepts
  • LSTM (Long Short Term Memory) networks
  • LSTM architecture
  • Lab: RNNs for text and sequence prediction
  • Section 8: Transfer Learning
  • Understanding transfer learning
  • Customizing available models
  • Lab: transfer learning lab
  • Lab: Benchmarking performance on CPU and GPU
Workshop (Time permitting)
  • Students will work in teams to solve a real world use case
august 31-september 4, 2020 | 1:00pm EDT | $529

AI For Computer Vision & Image Analysis

In the last few years, AI algorithms for image analysis have made tremendous progress. This is mainly due to the abundance of data, affordable computing, and exceptional libraries. Google has open-sourced a library called TensorFlow which has become the de facto standard, allowing the state of the art machine learning done at scale, complete with GPU based acceleration.

This course introduces Deep Learning concepts and TensorFlow and Keras libraries to students.

what you'll learn
  • Deep Learning concepts
  • TensorFlow and Keras
  • Create neural networks with Tensorflow and Keras
  • Learn to use tools like Tensorboard to help with training neural networks
  • Deep Neural Networks
  • Convolutional Neural Networks (CNN)
  • Generative Adversarial Networks (GAN)
  • Auto Encoders
use cases covered
  • Image classifications
  • Anomaly detection
  • Generating computer images

Audience

Developers, Data analysts, data scientists

Skill level

Beginner to Intermediate

Prerequisites
  • Basic knowledge of Python language and Jupyter notebooks is assumed.
  • Even if you haven’t done any Python programming, Python is such an easy language to learn quickly. We will provide Python resources.
Lab environment
  • Cloud-based lab environment will be provided to students, no need to install anything on the laptop

Sujee Maniyam

Sujee Maniyam is a seasoned practitioner and founder of Elephant Scale.  He teaches and consults in AI (machine learning and deep learning) and Big Data  (Hadoop, Spark, NoSQL) and and Cloud technologies.

He is an open source contributor, author ( ‘Hadoop illuminated‘ and ‘HBase Design Patterns‘)  and speaker at conferences.  He also advises and mentors various companies and organizations.

Speaking : http://elephantscale.com/speaking/
Publications : http://sujee.net/books/

Mark Kerzner

Mark is an experienced, hands-on software architect, practicing and teaching AI, Machine Learning, Blockchain, Spark, Hadoop, NoSQL, and more. He worked in a variety of verticals (Hightech, Healthcare, O&G, Legal, Fintech). His classes are hands-on and draw heavily on his industry experience. Mark is certified in Google Cloud (GCP), Amazon (AWS), and Hadoop.

Mark is an author and maintainer for a popular open source project for lawyers and researchers, FreeEed, which deals with search and massive scalability.

August 31st: 1:00-5:00PM 
september 1st: 1:00-5:00PM
september 2nd: 1:00-5:00PM 
september 3rd: 1:00-5:00PM 
september 4th: 1:00-5:00PM 

Introduction to Deep Learning
  • Understanding Deep Learning use cases
  • Understanding AI / Machine Learning / Deep Learning
  • Data and AI
  • AI vocabulary
  • Hardware and software ecosystem
  • Understanding types of Machine Learning (Supervised / Unsupervised / Reinforcement)
Introducing TensorFlow
  • TensorFlow intro
  • TensorFlow features
  • Execution graph
  • TensorFlow on GPU and TPU
  • TensorFlow API
  • Lab: Setting up and Running TensorFlow
Introducing Keras
  • Keras Intro
  • Keras concepts (models, layers)
  • Using Keras API
  • Lab
Deep Learning Concepts
  • Introducing Perceptrons
  • Linear Perceptrons
  • Activation Functions (Sigmoid, Tanh, Relu, Softmax)
  • Backpropagation
  • Optimizers (Gradient Descent, Adam, RMSProp)
  • Loss functions for regressions and classifications
  • Vanishing/exploding gradient problem
  • Lab: Tensorflow playground
Feedforward Network
  • FFNN architecture
  • Input layer, output layer
  • Hidden layers and Deep neural networks
  • Sizing neural networks
  • Lab: Feedforward Neural Networks
Computer Vision
  • Introducing Convolutional Neural Networks (CNN)
  • CNN architecture
  • CNN concepts
  • Lab: Image recognition using CNNs
Generative Adversarial Network (GAN)
  • GAN Overview
  • Generating Images
  • Lab : GAN lab
  • Auto Encoder
  • Auto Encoder overview
  • Auto Encoder use cases
  • Lab: Auto encoder
Transfer Learning
  • Understanding transfer learning
  • Customizing available models
  • Lab: transfer learning lab
  • Lab: Benchmarking performance on CPU and GPU
Workshop (Time permitting)
  • Students will work in teams to solve a real world use case
september 14-18, 2020 | 1:00pm EDT | $529

AI For Natural Language Processing

We live in an era of so much data – a lot of it is text (emails, tweets, customer tickets, Yelp reviews, product reviews, etc.)

In the field of AI, there is a revolution going on in the past few years. The researchers from companies like Google, Facebook, Microsoft and Baidu has come up with breakthrough algorithms that can understand text data more than ever before.

The applications are wide-ranging, including understanding documents, processing customer service tickets and analyzing reviews.

In this course, we will teach how to handle text data and introduce you to modern AI NLP technologies.

what you'll learn
  • How to prepare text for machine learning
  • Stemming, tokenizing and filtering stop words in text
  • Analyzing documents using word-frequency, bag-of-words techniques
  • Visualizing text data
  • Classic toolsets for text processign: NLTK, Textblob, TF-IDF
  • Naive Bayes for text classifications
  • Modern techniques for text: Spacy, Word2Vec
  • Topic modeling with Gensim
  • Neural Network frameworks: Tensorflow & Keras
  • NN models for text processing: LSTM, RNN
  • Modern NN models for text processing: ELMO, ULMFIT, BERT
industry use cases covered
  • Determining if a text message is a spam (Telco)
  • Sentiment analysis of Tweets (Social)
  • IMDB Movie ratings and reviews analysis

Audience

Developers, Data analysts, data scientists

Skill level

Beginner to Intermediate

Prerequisites
  • Programming background
  • Basic knowledge of Python language and Jupyter notebooks is recommended.
    • Even if you haven’t done any Python programming, Python is such an easy language to learn quickly. We will provide Python resources.
Lab environment
  • Cloud-based lab environment will be provided to students, no need to install anything on the laptop

Sujee Maniyam

Sujee Maniyam is a seasoned practitioner and founder of Elephant Scale.  He teaches and consults in AI (machine learning and deep learning) and Big Data  (Hadoop, Spark, NoSQL) and and Cloud technologies.

He is an open source contributor, author ( ‘Hadoop illuminated‘ and ‘HBase Design Patterns‘)  and speaker at conferences.  He also advises and mentors various companies and organizations.

Speaking : http://elephantscale.com/speaking/
Publications : http://sujee.net/books/

Mark Kerzner

Mark is an experienced, hands-on software architect, practicing and teaching AI, Machine Learning, Blockchain, Spark, Hadoop, NoSQL, and more. He worked in a variety of verticals (Hightech, Healthcare, O&G, Legal, Fintech). His classes are hands-on and draw heavily on his industry experience. Mark is certified in Google Cloud (GCP), Amazon (AWS), and Hadoop.

Mark is an author and maintainer for a popular open source project for lawyers and researchers, FreeEed, which deals with search and massive scalability.

september 14th: 1:00-5:00PM
september 15th: 1:00-5:00PM
september 16th: 1:00-5:00PM
september 17th: 1:00-5:00PM
september 18th: 1:00-5:00PM

Machine Learning Overview
  • Machine Learning landscape
  • Understanding AI use cases
  • Data and AI
  • AI vocabulary
  • Hardware and software ecosystem
  • Understanding types of Machine Learning (Supervised / Unsupervised / Reinforcement)
Text Preparation
  • Filtering
  • Stopwords
  • Stemming
  • Parsing and tokenization
  • Word-clouds
  • Working with Unicode
Text Algorithms
  • N-grams
  • Bag-of-words
  • NLTK
  • TextBlob
  • TF-IDF
Text Classification
  • Naive Bayes
  • SVM
Text datasets and Benchmarks
  • Public text datasets
  • Benchmarks (GLUE, SQUAD)
Topic Modeling
  • LDA (Latent Dirichlet Allocation)
  • Gensim
Introduction to Neural Networks
  • Perceptrons
  • Feedforward networks
  • Activation functions
  • Optimizers
  • Backpropagation
  • Deep Neural Networks
Tensorflow
  • TensorFlow intro
  • TensorFlow features
  • TensorFlow on GPU and TPU
  • TensorFlow API
  • Lab: Setting up and Running TensorFlow
NLP and Deep Learning
  • Word embeddings
  • Skipgram
  • Training the model
  • Visualizing the embeddings
  • Word2Vec
  • SpaCy for named entity recognition
Recurrent Neural Networks (RNN)
  • Introduction to RNNs
  • Text prediction
  • Named entity extraction
  • Automatic translation (seq2seq)
  • Text generation
Transformers
  • Attention concept
  • Transformer architecture
  • Bidirectional LSTM
  • Pre-trained Models for Text Processing (ElMO, ULMFIT, BERT)
Conversational AI
  • Understanding natural language
  • Generating natural language
  • Introduction to RASA framework
Final Workshop (Time Permitting)
  • This a group exercise
  • Students will use the learned techniques to solve a real world problem
  • And present their solutions to the class
  • Discussions and Takeaways