machine learning

  1. Coursera

    Coursera ($) Applying Machine Learning to your Data with GCP

    Overview >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: Terms of Service | Qwiklabs <<< Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your...
  2. Coursera

    Coursera Probabilistic Graphical Models 1: Representation

    Overview Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics...
  3. Coursera

    Coursera Probabilistic Graphical Models 2: Inference

    Overview Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics...
  4. Coursera

    Coursera Probabilistic Graphical Models 3: Learning

    Overview Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics...
  5. Coursera

    Coursera How Google does Machine Learning

    Overview >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: Terms of Service | Qwiklabs <<< What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of...
  6. Coursera

    Coursera ($) Launching into Machine Learning

    Overview Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of data science problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that...
  7. Coursera

    Coursera ($) Intro to TensorFlow

    Overview We introduce low-level TensorFlow and work our way through the necessary concepts and APIs so as to be able to write distributed machine learning models. Given a TensorFlow model, we explain how to scale out the training of that model and offer high-performance predictions using Cloud...
  8. Coursera

    Coursera ($) Feature Engineering

    Overview >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: Terms of Service | Qwiklabs <<< Want to know how you can improve the accuracy of your machine learning models? What about how to find which data columns make the most useful...
  9. Coursera

    Coursera ($) Art and Science of Machine Learning

    Overview Welcome to the art and science of machine learning. In this data science course you will learn the essential skills of ML intuition, good judgment and experimentation to finely tune and optimize your ML models for the best performance. In this course you will learn the many knobs and...
  10. Coursera

    Coursera Mathematics for Machine Learning: Linear Algebra

    Overview In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve...
  11. Coursera

    Coursera Mathematics for Machine Learning: Multivariate Calculus

    Overview This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient...
  12. Coursera

    Coursera Mathematics for Machine Learning: PCA

    Overview This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles...
  13. Coursera

    Coursera Machine Learning with Python

    Overview This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world...
  14. Coursera

    Coursera Machine Learning Foundations: A Case Study Approach

    Overview 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...
  15. Coursera

    Coursera Machine Learning: Regression

    Overview Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can...
  16. Coursera

    Coursera Machine Learning: Classification

    Overview Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this...
  17. Coursera

    Coursera Machine Learning

    Overview 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...
  18. Coursera

    Coursera Introduction to Deep Learning

    Overview The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for...
  19. Coursera

    Coursera Bayesian Methods for Machine Learning

    Overview Bayesian methods are used in lots of fields: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in...
  20. Coursera

    Coursera Practical Reinforcement Learning

    Overview Welcome to the Reinforcement Learning course. Here you will find out about: - foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. --- with math & batteries included - using deep neural networks for RL tasks --- also known as "the hype train" - state...
Top