Coursera MOOC-Machine Learning Foundations: A Case Study Approach; University of Washington

Coursera MOOC-Machine Learning Foundations: A Case Study Approach; University of Washington

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About this course: Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems?

In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains.

This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications.

Learning Outcomes: By the end of this course, you will be able to:

  • -Identify potential applications of machine learning in practice.
  • -Describe the core differences in analyses enabled by regression, classification, and clustering.
  • -Select the appropriate machine learning task for a potential application.
  • -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
  • -Represent your data as features to serve as input to machine learning models.
  • -Assess the model quality in terms of relevant error metrics for each task.
  • -Utilize a dataset to fit a model to analyze new data.
  • -Build an end-to-end application that uses machine learning at its core.
  • -Implement these techniques in Python.


WEEK 1. Welcome
WEEK 2. Regression: Predicting House Prices (Linear Regression)
WEEK 3. Classification: Analyzing Sentiment (Logistic Regression)
WEEK 4. Clustering and Similarity: Retrieving Documents (k-means, Nearest Neighbors)
WEEK 5. Recommending Products (Matrix factorization)
WEEK 6. Deep Learning: Searching for Images (Neural network, Nearest Neighbors)


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Coursera MOOC-Practical Machine Learning; Johns Hopkins University

Coursera MOOC-Practical Machine Learning; Johns Hopkins University

Johns Hopkins University

About this course: One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.


Week 1: Prediction, Errors, and Cross Validation
This week will cover prediction, relative importance of steps, errors, and cross validation.

Week 2: The Caret Package
This week will introduce the caret package, tools for creating features and preprocessing.

Week 3: Predicting with trees, Random Forests, & Model Based Predictions
This week we introduce a number of machine learning algorithms you can use to complete your course project.

Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.


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Lagunita MOOC-Statistical Learning; Stanford University

Lagunita MOOC-Statistical Learning; Stanford University


This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes:

  • linear and polynomial regression, logistic regression and linear discriminant analysis;
  • cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso);
  • nonlinear models, splines and generalized additive models;
  • tree-based methods, random forests and boosting;
  • support-vector machines.

Some unsupervised learning methods are discussed:

  • principal components and clustering (k-means and hierarchical).

This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data analysis. Computing is done in R. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter.

The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013). The pdf for this book is available for free on the book website.


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Statistical Learning versus Machine Learning

• Machine learning arose as a subfield of Artificial Intelligence.

• Statistical learning arose as a subfield of Statistics.

• There is much overlap — both fields focus on supervised and unsupervised problems:

  1. Machine learning has a greater emphasis on large scale applications and prediction accuracy.
  2. Statistical learning emphasizes models and their interpretability, and precision and uncertainty.

• But the distinction has become more and more blurred, and there is a great deal of “cross-fertilization”.

• Machine learning has the upper hand in Marketing!

Comparison of methods in Machine Learning

Machine Learning

Unsupervised vs Supervised Learning

Supervised learning methods such as regression and classification. In that setting we observe both a set of features X1, X2, . . . , Xp for each object, as well as a response or outcome variable Y . The goal is then to predict Y using X1, X2, . . . , Xp.

Unsupervised learning, we where observe only the features X1, X2, . . . , Xp. We are not interested in prediction, because we do not have an associated response variable Y. The goal is to discover interesting things about the measurements: is there an informative way to visualize the data? Can we discover subgroups among the variables or among the observations? We discuss two methods: principal components analysis & clustering.


Udacity MOOC-Machine Learning for Trading; Georgia Tech

Udacity MOOC-Machine Learning for Trading; Georgia Tech

Georgia Institute of Technology

About this Course

This course introduces students to the real world challenges of implementing machine learning based trading strategies including the algorithmic steps from information gathering to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, KNN and regression trees and how to apply them to actual stock trading situations.

What You Will Learn

This course is composed of three mini-courses:

  • Mini-course 1: Manipulating Financial Data in Python
  • Mini-course 2: Computational Investing
  • Mini-course 3: Machine Learning Algorithms for Trading

Each mini-course consists of about 7-10 short lessons. Assignments and projects are interleaved.

Prerequisites and Requirements

Students should have strong coding skills and some familiarity with equity markets. No finance or machine learning experience is assumed.

Note that this course serves students focusing on computer science, as well as students in other majors such as industrial systems engineering, management, or math who have different experiences. All types of students are welcome!

The ML topics might be “review” for CS students, while finance parts will be review for finance students. However, even if you have experience in these topics, you will find that we consider them in a different way than you might have seen before, in particular with an eye towards implementation for trading.

Programming will primarily be in Python. We will make heavy use of numerical computing libraries like NumPy and Pandas.

Why Take This Course

By the end of this course, you should be able to:

  • Understand data structures used for algorithmic trading.
  • Know how to construct software to access live equity data, assess it, and make trading decisions.
  • Understand 3 popular machine learning algorithms and how to apply them to trading problems.
  • Understand how to assess a machine learning algorithm’s performance for time series data (stock price data).
  • Know how and why data mining (machine learning) techniques fail.
  • Construct a stock trading software system that uses current daily data.

Some limitations/constraints:

  • We use daily data. This is not an HFT course, but many of the concepts here are relevant.
  • We don’t interact (trade) directly with the market, but we will generate equity allocations that you could trade if you wanted to.


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edx MOOC-Principles of Economics with Calculus; CaltechX

edx MOOC – Principles of Economics with Calculus; CaltechX

Quantitative and model-based introduction to basic ideas in economics, and applications to a wide range of real world problems.

About this course

This course provides a quantitative and model-based introduction to basic economic principles, and teaches how to apply them to make sense of a wide range of real world problems. Examples of applications include predicting the impact of technological changes in market prices, calculating the optimal gasoline tax, and measuring the value of new products. This is a real Caltech class. It will be taught concurrently to Caltech and on-line students. This has two implications. On the costs side: the class is challenging, makes extensive use of calculus, and will demand significant effort. On the benefit side: successful completion of the class will provide you with an in-depth understanding of basic economics, and will permanently change the way you see the world.

What you’ll learn

  • Understand, apply, and analyze calculus-based economic models
  • Translate economic principles to the investigation of a wide range of real world problems
  • Elaborate on an in-depth understanding of basic economics and its applications


  • Unit 0. Introduction and Logistics
  • Unit 1. Principles of Optimizing Behavior
  • Unit 2. Consumer Theory
  • Unit 3. Producer Theory
  • Unit 4. Competitive Markets
  • Unit 5. Government Policy in Competitive Markets I: Efficiency
  • Unit 6. Government Policy in Competitive Markets II: Distribution and Incidence
  • Unit 7. Imperfect Competition I: Monopoly
  • Unit 8. Imperfect Competition II: Oligopoly and Monopolistic Competition
  • Unit 9. Externalities with and without Government Intervention

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Coursera MOOC-Machine Learning; Stanford University; Andrew Ng

Coursera MOOC-Machine Learning; Stanford University; Andrew Ng

About this course: Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Stanford University



  • Introduction
  • Linear Regression with One Variable
  • Linear Algebra Review


  • Linear Regression with Multiple Variables
  • Octave/Matlab Tutorial


  • Logistic Regression
  • Regularization


  • Neural Networks: Representation


  • Neural Networks: Learning


  • Advice for Applying Machine Learning
  • Machine Learning System Design


  • Support Vector Machines


  • Unsupervised Learning
  • Dimensionality Reduction


  • Anomaly Detection
  • Recommender Systems


  • Large Scale Machine Learning


  • Application Example: Photo OCR


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Coursera MOOC-Portfolio and Risk Management; University of Geneva

Coursera MOOC-Portfolio and Risk Management; University of Geneva

About this course: In this course, you will gain an understanding of the theory underlying optimal portfolio construction, the different ways portfolios are actually built in practice and how to measure and manage the risk of such portfolios. You will start by studying how imperfect correlation between assets leads to diversified and optimal portfolios as well as the consequences in terms of asset pricing. Then, you will learn how to shape an investor’s profile and build an adequate portfolio by combining strategic and tactical asset allocations. Finally, you will have a more in-depth look at risk: its different facets and the appropriate tools and techniques to measure it, manage it and hedge it. Key speakers from UBS, our corporate partner, will regularly add a practical perspective on these different topics as you progress through the course.

University of Geneva

  • General Introduction and Key Concepts
In this introductory week, you will first be presented with a few mistakes you will no longer make after following this course. In order to avoid making these mistakes, you will start by gaining a foundation and understanding of the three main types of information we need in order to build optimal portfolios: expected returns, risk and dependence.
  • Modern Portfolio Theory and Beyond
The focus of this second week is on Modern Portfolio Theory. By understanding how imperfect correlations between asset returns can lead to superior risk-adjusted portfolio returns, we will soon be looking for ways to maximize the effect of diversification, which is at the heart of Modern Portfolio Theory. But we won’t stop there: we will also explore the implications of Modern Portfolio Theory on real-world investment decisions and whether or not these implications are followed by investors. Finally, we will see how Modern Portfolio Theory can be built upon to derive the most popular asset pricing model: the Capital Asset Pricing Model.
  • Asset Allocation
This third week is dedicated to asset allocation. After a short introduction to investor profiling, we will delve into Strategic Asset Allocation (SAA). You will see how it relates to Modern Portfolio Theory and how it differs from Tactical Asset Allocation (TAA). We will look at how both asset allocations can be implemented separately but also in conjunction in order to build portfolios that fulfill investors’ needs and constraints while taking advantage of market opportunities.
  • Risk Management
This fourth and final week is dedicated to risk. We will start by looking in more depth at different sources of risk such as illiquidity and currency risk but also at the different tools available to investors to perform risk management. But how should we measure risk? We will see that it may be valuable to go a step beyond standard deviation, the risk measure we used so far, and look at the Value-at-Risk and Expected Shortfall which focus on potential large losses. Finally, we will use the financial instruments at our disposal to hedge market and currency risk.
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