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Hands-on training in:

MACHINE LEARNING

  DATA SCIENCE

 DEEP LEARNING

DATA ANALYTICS

 DATA ENGINEERING

NLP & NLU

RESPONSIBLE AI

CYBERSECURITY

COMPUTER VISION

ROBOTICS

Using the latest AI languages, tools, and frameworks

Taught by renowned, expert instructors:

Live training

LIVE TRAINING: June 10th, 2021

Part 2: Computer Science

Through the measured exposition of theory paired with interactive examples, you’ll develop a working understanding of all of the essential data structures across the list, dictionary, tree, and graph families

LIVE TRAINING: JUNE 15th, 2021

Matt Brems will cover introduction to Natural Language Processing including cleaning text data, transforming data with CountVectorizer and and TFIDFVectorizer as well as how to Fit machine learning models in scikit-learn and evaluate their performance. You will also learn how to build pipelines and GridSearch over NLP hyperparameters.

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LIVE TRAINING: DECEMBER 1ST and 8TH

The Seaborn data visualization library in Python provides a simple and intuitive interface for making beautiful plots directly from a Pandas DataFrame. When users arrange their data in tidy form, the Seaborn plotting functions perform the heavy lifting by grouping, splitting, aggregating, and plotting data, often with a single line of code. This course provides comprehensive coverage of how to use all of the Seaborn plotting functions with real-life data.

 

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LIVE TRAINING: DECEMBER 14TH

After this 4-hour session,you will have a foundation to carry out PRACTICAL, real-life social media mining. By taking this course, you are taking an important step forward in your data science journey to become an expert in harnessing the power of social media for deriving insights and identifying trends. This course will help you gain fluency both in the different aspects of text analysis and NLP working through a real-life example of cryptocurrency tweets, Wall Street Bet Reddit posts, restaurant reviews and financial news using a powerful clouded based python environment called GoogleColab.

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LIVE TRAINING: JANUARY 11TH

In this course, you will discover how to use Kubeflow to create a pipeline that retrains a machine learning model automatically and then redeploy the new model on top of an existing one. This process is a complex case that is common in MLOps. Allowing the company to automate the retraining and redeployment process. This material is based on 100% open-source tools that are most popular today in MLOps.

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LIVE TRAINING: JANUARY 18TH

In this course, we will explore the basics of epidemiology and the epidemic model in a practical and intuitive way. We will leverage our understanding of Python and NetworkX to introduce the fundamental concepts and models of epidemic modeling and use them in conjunction with public datasets to better understand the way in which CoVID-19 is spreading, grasp concepts such as R0, herd immunity, and vaccine efficacy, the impact of seasonality, and different competing strains.

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LIVE TRAINING: JANUARY 20TH

Pandas can be tricky, and there is a lot of bad advice floating around. This training sessions will cut through some of the biggest issues the instructor has seen with Pandas code after working with the library for a while and writing two books on. By undertaking this course, you will be able to understand the benefits of using the correct types, write pandas code that is fit for EDA but also deployment and master the best practices to be more efficient.

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Live BOOTCAMP

LIVE TRAINING: JANUARY 26TH

In this training, you will learn how to measure data governance efforts to increase the probability of success. Hot career opportunities in this field will also be discussed. Join Dr. Jonathan Reichental, a renowned business and technology expert, author, and professor, as he takes you on a journey to explore the remarkable benefits of a robust data governance strategy. 

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LIVE BOOTCAMP: FEBRUARY- APRIL

This 6-weeks Bootcampis  an introduction to deep neural networks that brings high-level theory to life with working, interactive examples featuring TensorFlow 2, Keras, and PyTorch — all three of the principal Python libraries for deep learning. Essential theory will be covered in a manner that provides students with a complete intuitive understanding of deep learning’s underlying foundations.

Paired with hands-on code demos in Jupyter notebooks as well as strategic advice for overcoming common pitfalls, this foundational knowledge will empower individuals with no previous understanding of artificial neural networks to build production-ready deep learning applications across all of the contemporary families

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CONFERENCE LIVE TRAINING 

Don’t miss the biggest training event of the year from the leading data science conference :

ODSC East 2022

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On-Demand training

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Certificates & Bootcamps

Live training student experience

Time Series Forecasting with Python

All clear and well explained by our very knowledgable instructor, Marta. This was all extremely applicable to my job. Wish this was a full day instead as Marta had a lot of amazing content. Thanks to the instructor and organizers.

Micheleen Harris
Micheleen Harris
Senior Data Scientist

Hands-On Intro to Unsupervised Learning

I had a bit of previous experience with some of the techniques and was looking for more in-depth. Got all I wanted. Ankur has a great presentation style and excellent examples. I particularly appreciated Ankur’s answers to my numerous questions. Those answers alone were worth the price because they provided insight into real-world applications of these models. I also liked the use of live demo in the TensorFlow projector to visualize the results of every algorithm. Will adopt it right away.

Michael Livshutz
Michael Livshutz
Technical Business Analyst

NLP Fundamentals in Python

Matt took us through difficult terrain with ease, making sure everybody was on the same page throughout. Really great teaching! Incredibly useful!

Huw Hallam
Huw Hallam
Senior Data and Insight Analyst

Advanced Fraud Modeling

Aric is a great teacher and the entire class was structured very well. I learned a lot of new interesting techniques. A mix of lecture and live coding with real-time incorporation of student feedback and questions into the class. Best for intermediate to advanced data folks because it assumes you know some Python. I would highly recommend if you have an interest to learn fraud.

 Shery Cheong
Shery Cheong
Data Scientist

Practical Advanced Pandas

I like the hands-on approach to teaching the additional techniques of Pandas, and I greatly appreciate the availability of the course’s code for me to review later.

Tyrel Cherry
Tyrel Cherry
System Engineer
Open Data Science

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