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Data Sciences

Machine Learning Cookbook

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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 the performance of your machine-learning systems.

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Course Access

Unlimited Duration

Last Updated

July 29, 2021

Students Enrolled

20

Total Reviews

Posted by

Ernesto Lee

Certification

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.

Course Curriculum

    • Introduction to Machine Learning 00:00:00
    • What is machine learning? 00:00:00
    • An overview of classification 00:00:00
    • An overview of clustering 00:00:00
    • An overview of supervised learning 00:00:00
    • An overview of unsupervised learning 00:00:00
    • An overview of reinforcement learning 00:00:00
    • An overview of structured prediction 00:00:00
    • An overview of neural networks 00:00:00
    • An overview of deep learning 00:00:00
    • Classification 00:00:00
    • Introduction 00:00:00
    • Discriminant function analysis – geological measurements on brines from wells 00:00:00
    • Multinomial logistic regression – understanding program choices made by students 00:00:00
    • Tobit regression – measuring the students’ academic aptitude 00:00:00
    • Poisson regression – understanding species present in Galapagos Islands 00:00:00
    • Clustering 00:00:00
    • Introduction 00:00:00
    • Hierarchical clustering – World Bank sample dataset 00:00:00
    • Hierarchical clustering – Amazon rainforest burned between 1999-2010 00:00:00
    • Hierarchical clustering – gene clustering 00:00:00
    • Binary clustering – math test 00:00:00
    • K-means clustering – European countries protein consumption 00:00:00
    • K-means clustering – foodstuff 00:00:00
    • Model Selection and Regularization 00:00:00
    • Introduction 00:00:00
    • Shrinkage methods – calories burned per day 00:00:00
    • Dimension reduction methods – Delta’s Aircraft Fleet 00:00:00
    • Principal component analysis – understanding world cuisine 00:00:00
    • Nonlinearity 00:00:00
    • Generalized additive models – measuring the household income of New Zealand 00:00:00
    • Smoothing splines – understanding cars and speed 00:00:00
    • Local regression – understanding drought warnings and impact 00:00:00
    • Supervised Learning 00:00:00
    • Introduction 00:00:00
    • Decision tree learning – Advance Health Directive for patients with chest pain 00:00:00
    • Decision tree learning – income-based distribution of real estate values 00:00:00
    • Decision tree learning – predicting the direction of stock movement 00:00:00
    • Naive Bayes – predicting the direction of stock movement 00:00:00
    • Random forest – currency trading strategy 00:00:00
    • Support vector machine – currency trading strategy 00:00:00
    • Stochastic gradient descent – adult income 00:00:00
    • Unsupervised Learning 00:00:00
    • Introduction 00:00:00
    • Self-organizing map – visualizing of heatmaps 00:00:00
    • Vector quantization – image clustering 00:00:00
    • Reinforcement Learning 00:00:00
    • Introduction 00:00:00
    • Markov chains – the stocks regime switching model 00:00:00
    • Markov chains – the multi-channel attribution model 00:00:00
    • Markov chains – the car rental agency service 00:00:00
    • Continuous Markov chains – vehicle service at a gas station 00:00:00
    • Monte Carlo simulations – calibrated Hull and White short-rates 00:00:00
    • Structured Prediction 00:00:00
    • Introduction 00:00:00
    • Hidden Markov models – EUR and USD 00:00:00
    • Hidden Markov models – regime detection 00:00:00
    • Neural Networks 00:00:00
    • Introduction 00:00:00
    • Modelling SP 500 00:00:00
    • Measuring the unemployment rate 00:00:00
    • Deep Learning 00:00:00
    • Introduction 00:00:00
    • Recurrent neural networks – predicting periodic signals 00:00:00
    • Case Study – Exploring World Bank Data 00:00:00
    • Introduction 00:00:00
    • Exploring World Bank data 00:00:00
    • Case Study – Pricing Reinsurance Contracts 00:00:00
    • Introduction 00:00:00
    • Pricing reinsurance contracts 00:00:00
    • Case Study – Forecast of Electricity Consumption 00:00:00
    • Introduction 00:00:00

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