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Machine Learning Cookbook

  • Course Code: Data Science - Machine Learning Cookbook
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 4 Days Audience: This course is geared for those who wants to Build Machine Learning applications with R.

Course Snapshot 

  • Duration: 4 days 
  • Skill-level: Foundation-level Machine Learning Cookbook skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Build Machine Learning applications with R. 
  • 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 has become the new black. The challenge in today’s world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations. The first half of the course provides recipes on fairly complex machine-learning systems, where you’ll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more. The second half of the course focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one. 

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

  • Implement a wide range of algorithms and techniques for tackling complex data 
  • Improve predictions and recommendations to have better levels of accuracy 
  • Optimize performance of your machine-learning systems 

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

  • Get equipped with a deeper understanding of how to apply machine-learning techniques 
  • Implement each of the advanced machine-learning techniques 
  • Solve real-life problems that are encountered in order to make your applications produce improved results 
  • Gain hands-on experience in problem solving for your machine-learning systems 
  • Understand the methods of collecting data, preparing data for usage, training the model, evaluating the model’s performance, and improving the model’s performance 

Audience & Pre-Requisites 

This course is designed for developers interested in Building Machine Learning applications with R 

Pre-Requisites:  Students should have familiar with  

  • Basics of Python  
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

  1. Introduction to Machine Learning 
  • Introduction to Machine Learning 
  • What is machine learning? 
  • An overview of classification 
  • An overview of clustering 
  • An overview of supervised learning 
  • An overview of unsupervised learning 
  • An overview of reinforcement learning 
  • An overview of structured prediction 
  • An overview of neural networks 
  • An overview of deep learning 
  1. Classification 
  • Classification 
  • Introduction 
  • Discriminant function analysis – geological measurements on brines from wells 
  • Multinomial logistic regression – understanding program choices made by students 
  • Tobit regression – measuring the students’ academic aptitude 
  • Poisson regression – understanding species present in Galapagos Islands 
  1. Clustering 
  • Clustering 
  • Introduction 
  • Hierarchical clustering – World Bank sample dataset 
  • Hierarchical clustering – Amazon rainforest burned between 1999-2010 
  • Hierarchical clustering – gene clustering 
  • Binary clustering – math test 
  • K-means clustering – European countries protein consumption 
  • K-means clustering – foodstuff 
  1. Model Selection and Regularization 
  • Model Selection and Regularization 
  • Introduction 
  • Shrinkage methods – calories burned per day 
  • Dimension reduction methods – Delta’s Aircraft Fleet 
  • Principal component analysis – understanding world cuisine 
  1. Nonlinearity 
  • Nonlinearity 
  • Generalized additive models – measuring the household income of New Zealand 
  • Smoothing splines – understanding cars and speed 
  • Local regression – understanding drought warnings and impact 
  1. Supervised Learning 
  • Supervised Learning 
  • Introduction 
  • Decision tree learning – Advance Health Directive for patients with chest pain 
  • Decision tree learning – income-based distribution of real estate values 
  • Decision tree learning – predicting the direction of stock movement 
  • Naive Bayes – predicting the direction of stock movement 
  • Random forest – currency trading strategy 
  • Support vector machine – currency trading strategy 
  • Stochastic gradient descent – adult income 
  1. Unsupervised Learning 
  • Unsupervised Learning 
  • Introduction 
  • Self-organizing map – visualizing of heatmaps 
  • Vector quantization – image clustering 
  1. Reinforcement Learning 
  • Reinforcement Learning 
  • Introduction 
  • Markov chains – the stocks regime switching model 
  • Markov chains – the multi-channel attribution model 
  • Markov chains – the car rental agency service 
  • Continuous Markov chains – vehicle service at a gas station 
  • Monte Carlo simulations – calibrated Hull and White short-rates 
  1. Structured Prediction 
  • Structured Prediction 
  • Introduction 
  • Hidden Markov models – EUR and USD 
  • Hidden Markov models – regime detection 
  1. Neural Networks 
  • Neural Networks 
  • Introduction 
  • Modelling SP 500 
  • Measuring the unemployment rate 
  1. Deep Learning 
  • Deep Learning 
  • Introduction 
  • Recurrent neural networks – predicting periodic signals 
  1. Case Study – Exploring World Bank Data 
  • Case Study – Exploring World Bank Data 
  • Introduction 
  • Exploring World Bank data 
  1. Case Study – Pricing Reinsurance Contracts 
  • Case Study – Pricing Reinsurance Contracts 
  • Introduction 
  • Pricing reinsurance contracts 
  1. Case Study – Forecast of Electricity Consumption 
  • Case Study – Forecast of Electricity Consumption 
  • Introduction 

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