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Course Skill Level:


Course Duration:

4 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Developers with basic Python experience

Who should attend & recommended skills

  • This course is geared for developers interested in building machine learning applications with R.
  • Skill-level: Foundation-level Machine Learning Cookbook skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience)

About this course

Machine learning has become the new black. The challenge in todays 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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning Cookbook expert instructor, students will learn about and explore:
  • Implementing a wide range of algorithms and techniques for tackling complex data
  • Improving predictions and recommendations to have better levels of accuracy
  • Optimizing performance of your machine-learning systems
  • Getting equipped with a deeper understanding of how to apply machine-learning techniques
  • Implementing each of the advanced machine-learning techniques
  • Solving real-life problems that are encountered in order to make your applications produce improved results
  • Gaining hands-on experience in problem solving for your machine-learning systems
  • Understanding the methods of collecting data, preparing data for usage, training the model, evaluating the model’s performance, and improving the model’s performance

Course breakdown / modules

  • 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

  • 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

  • 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

  • Introduction
  • Shrinkage methods – calories burned per day
  • Dimension reduction methods – Delta’s Aircraft Fleet
  • Principal component analysis – understanding world cuisine

  • Generalized additive models – measuring the household income of New Zealand
  • Smoothing splines – understanding cars and speed
  • Local regression – understanding drought warnings and impact

  • 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

  • Introduction
  • Self-organizing map – visualizing of heatmaps
  • Vector quantization – image clustering

  • 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

  • Introduction
  • Hidden Markov models – EUR and USD
  • Hidden Markov models – regime detection

  • Introduction
  • Modelling SP 500
  • Measuring the unemployment rate

  • Introduction
  • Recurrent neural networks – predicting periodic signals

  • Introduction
  • Exploring World Bank data

  • Introduction
  • Pricing reinsurance contracts

  • Case Study – Forecast of Electricity Consumption
  • Introduction