Hands-on beginner to expert level training in the most in-demand topics in artificial intelligence. Dozens of courses and hundreds of recordings available, with more added weekly.
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Now Available On-demand

Network traffic data can be a rich source of information for cybersecurity and IT management. Malware is delivered and performs command and control communications over the network, and software sends and receives a wealth of data over the network.
The course provides an introduction to analyzing network traffic data with Python. Python is an ideal tool for this task because it is freely available and has numerous modules that support network traffic analysis, data science, and other tasks.
This course will build familiarity and skills in network traffic analysis via a series of hands-on exercises and examples. By examining, modifying, and building custom scripts for network traffic analysis, you’ll how to access and select network traffic data, analyze it, and interpret the results.


Now Available On-demand

By the end of this 4-part live, hands-on, online course, you’ll understand in detail how Gradient Boosting models are fit as an ensemble of decision trees and apply that understanding to the feature engineering process, the various parameters of Gradient Boosting and their relative importance and how to appropriately choose them and gain familiarity with the various Gradient Boosting packages and the capabilities, strengths, and weaknesses of each. You will also learn how to interpret, understand, and evaluate a model: both qualitatively and quantitatively.





Time Series Forecasting (with Python)

DS 101: Machine Learning & Modelling in Theory & Practice

Introduction to Machine Learning and the Bias-Variance Tradeoff

How to Do Data Science with Missing Data

Continuously Deployed Machine Learning

Data, I/O, and TensorFlow: Building a Reliable Machine Learning Data Pipeline

Recommendation Systems in Python: Fairness in AI Using Open-source Tools & Bias-dashboards

Ankur Patel, Head of Data

Machine Learning Fundamentals – Unsupervised Learning Series

Introduction to Fraud and Anomaly Detection

Sheamus McGovern, Founder, Engineer, AI Expert

Introduction to Data Literacy

Dr. Kirk Borne, Principal Data Scientist

DS 102: Machine Learning & Modelling in Theory & Practice

Non-linear Supervised Machine Learning Algorithms

Evaluation Metrics in Supervised Machine Learning

Hands-On Machine Learning Explainability

Explaining and Interpreting Gradient Boosting Models in Machine Learning

Data Science and Machine Learning in the Cloud for Cloud Novices

Data Annotation at Scale: Active and Semi-Supervised Learning in Python

How to Prepare Your Data for Supervised Machine Learning


PyTorch 101: Building a Model Step-by-Step

Convolutional Neural Networks Deep Dive

Deep Learning on Mobile Devices

Transformer Architecture and Its Cutting Edge Advances

Natural Language Processing Case Studies for Healthcare Models

Introduction to Face Processing With Computer Vision

An Introduction to Transfer Learning in NLP and HuggingFace Tools


Complete Python Fundamentals

Data Science in the Industry: Continuous Delivery for Machine Learning with Open-Source Tools

Modern Data Acquisition

Advanced Practical Pandas – From Multi-indexing to Styling

Getting Started with Pandas for Data Analysis

Streaming Decision Intelligence and Predictive Analytics with Spark 3


Advanced NLP 1: Overview of Basic to State-of-the-Art NLP

Advanced NLP 3: NLP in Production via Web Apps, Automated Pipelines, and APIs

Introduction to NLP and Conversational AI: From Beginner to Expert with Illustrations and Hands-On Examples

Advanced NLP 2: Modern NLP in Depth, from Theory to Action

Natural Language Processing Fundamentals in Python

Natural Language Processing Case-studies for Healthcare Models

On-Demand Learning Paths

Discover Your Path

S T U D E N T    T E S T I M O N I A L S

“Very interesting and helpful. I feel confident in exploring Google Cloud further after this presentation. I especially liked how Joy explained how to link Google Cloud and Google CoLab. Thanks for offering this session.”

Dr Stephanie Powers, Professor

“This is the future trend for great search start learning now!”

Nathan Keogh, CTO

“Highly recommend the interactive and engaging approach used by Dr Krohn. His courses always exceed my expectations. A high-quality and in-depth content. This is a great introductory session that will enable you to grasp the key areas of deep learning and it will be a ladder for you to develop your knowledge further in the field.”

Hasani Perera, Bioinformatic Researcher

The #1 Machine Learning Mini-Bootcamp

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