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Machine Learning Overview

  • Course Code:
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level machine learning skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who wants to get guide to developing, training, and optimizing your machine learning models  
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This course guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this course will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this course, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference 

Working in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore: 

  • Your guide to learning efficient machine learning processes from scratch 
  • Explore expert techniques and hacks for a variety of machine learning concepts 
  • Write effective code in R, Python, Scala, and Spark to solve all your machine learning problems 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Get a quick rundown of model selection, statistical modeling, and cross-validation 
  • Choose the best machine learning algorithm to solve your problem 
  • Explore kernel learning, neural networks, and time-series analysis 
  • Train deep learning models and optimize them for maximum performance 
  • Briefly cover Bayesian techniques and sentiment analysis in your NLP solution 
  • Implement probabilistic graphical models and causal inferences 
  • Measure and optimize the performance of your machine learning models 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to get guide to developing, training, and optimizing your machine learning models 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them. 
  • Good foundational mathematics or logic skills 
  • No machine learning experience or advanced math skills necessary. 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Quantifying Learning Algorithms 
  • Quantifying Learning Algorithms 
  • Statistical models 
  • Learning curve 
  • Curve fitting 
  • Statistical modeling – the two cultures of Leo Breiman 
  • Training data development data – test data 
  • Bias-variance trade off 
  • Regularization 
  • Cross-validation and model selection 
  • Model selection using cross-validation 
  • 0.632 rule in bootstrapping 
  • Model evaluation 
  • Receiver operating characteristic curve 
  • H-measure 
  • Dimensionality reduction 
  1. Evaluating Kernel Learning 
  • Evaluating Kernel Learning 
  • Introduction to vectors 
  • Linear separability 
  • Hyperplanes  
  • SVM 
  • Kernel trick 
  • Kernel types 
  • SVM example and parameter optimization through grid search 
  1. Performance in Ensemble Learning 
  • Performance in Ensemble Learning 
  • What is ensemble learning? 
  • Bagging 
  • Decision tree 
  • Random forest algorithm 
  • Boosting 
  1. Training Neural Networks 
  • Training Neural Networks 
  • Neural networks 
  • Network initialization 
  • Overfitting 
  • Prevention of overfitting in NNs 
  • Vanishing gradient  
  • Recurrent neural networks 
  1. Time Series Analysis 
  • Time Series Analysis 
  • Introduction to time series analysis 
  • White noise 
  • Random walk 
  • Autoregression 
  • Autocorrelation 
  • Stationarity 
  • AR model 
  • Moving average model 
  • Autoregressive integrated moving average 
  • Optimization of parameters 
  • Anomaly detection 
  1. Natural Language Processing 
  • Natural Language Processing 
  • Text corpus 
  • TF-IDF 
  • Sentiment analysis 
  • Topic modeling  
  • The Bayes theorem 
  1. Temporal and Sequential Pattern Discovery 
  • Temporal and Sequential Pattern Discovery 
  • Association rules 
  • Apriori algorithm 
  • Frequent pattern growth 
  1. Probabilistic Graphical Models 
  • Probabilistic Graphical Models 
  • Key concepts 
  • Bayes rule 
  • Bayes network 
  1. Selected Topics in Deep Learning 
  • Selected Topics in Deep Learning 
  • Deep neural networks 
  • Backward propagation 
  • Forward propagation equation 
  • Backward propagation equation 
  • Parameters and hyperparameters 
  • Bias initialization 
  • Generative adversarial networks 
  • Hinton’s Capsule network 
  1. Causal Inference 
  • Causal Inference 
  • Granger causality 
  • F-test 
  • Graphical causal models 
  1. Advanced Methods 
  • Advanced Methods 
  • Introduction 
  • Kernel PCA 
  • Independent component analysis 
  • Compressed sensing 
  • Self-organizing maps 
  • Bayesian multiple imputatio 
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