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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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CHECK OUT SOME OF OUR ON-DEMAND TRAINING
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.
MACHINE LEARNING
Time Series Forecasting (with Python)
Complete Reinforcement Learning
Advanced Fraud Modeling
Network Analysis Made Simple
DS 101: Machine Learning & Modelling in Theory & Practice
Introduction to Machine Learning and the Bias-Variance Tradeoff
Supervised Learning : Interpretability
How to Do Data Science with Missing Data
Continuously Deployed Machine Learning
Data, I/O, and TensorFlow: Building a Reliable Machine Learning Data Pipeline
Missing Data in Supervised ML
Recommendation Systems in Python: Fairness in AI Using Open-source Tools & Bias-dashboards
Machine Learning Fundamentals – Calculus
Machine Learning Fundamentals – Unsupervised Learning Series
Introduction to Fraud and Anomaly Detection
Introduction to Data Literacy
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
Graph Powered Machine Learning
Data Annotation at Scale: Active and Semi-Supervised Learning in Python
How to Prepare Your Data for Supervised Machine Learning
DEEP 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
Deep Learning with TensorFlow 2 and PyTorch
Introduction to Face Processing With Computer Vision
Active Learning with PyTorch
An Introduction to Transfer Learning in NLP and HuggingFace Tools
DATA ENGINEERING & DATA ANALYTICS
Complete Python Fundamentals
SQL for Data Science
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
NATURAL LANGUAGE PROCESSING
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
S T U D E N T ⭐ T E S T I M O N I A L S
“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
