Applying Machine Learning to your Data with GCP

Start Date: 08/30/2020

Course Type: Common Course

Course Link:

Explore 1600+ online courses from top universities. Join Coursera today to learn data science, programming, business strategy, and more.

About Course

In this module, we define what Machine Learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as build your own Machine Learning models using just SQL with BigQuery ML. PREREQUISITES To get the most out of this course, participants must complete the prior courses in this specialization: • Exploring and Preparing your Data • Storing and Visualizing your Data • Architecture and Performance >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: <<<

Course Syllabus

In this module we’ll explore an overview of what Machine Learning is from a high-level theoretical perspective and then tour how ML has become part of the culture here at Google. After that we will dive into pre-built and pre-trained ML models that we can access (like image recognition and sentiment analysis) within Cloud Datalab.

Deep Learning Specialization on Coursera

Course Introduction

Applying Machine Learning to your Data with GCP This is the second course in the specialization on applying machine learning to improve the quality of your data analysis. In the first course, we focused on applying basic machine learning methods to improve search results for a particular query. In this course, we focus on applying basic optimization methods to a variety of data sets, including those that you may have created yourself or from scratch. You will learn the basic concepts of machine learning and how to apply them to problems in data analysis. After completing this course, you will know how to apply basic optimization methods to analyze large datasets. You will also have a basic understanding of the different optimization algorithms that are used for different kinds of data sets. This will enable you to identify the ones that work for you and the data needs to be generated for you to be successful.Week 1: Sample Data and Basic Data Analysis Week 2: Optimization Week 3: Advanced Topic Applying Machine Learning to Improve Efficiency This is the second course in the specialization on applying machine learning to improve the efficiency of your data analysis. In the first course, we focused on applying basic machine learning methods to improve search results for a particular query. In this course, we focus on applying optimization methods to a variety of datasets, including those that you may have created yourself or from scratch. You will learn the basic concepts of machine learning and how to apply them to problems in data analysis.

Course Tag

Bigquery Machine Learning Google Cloud Platform Cloud Computing

Related Wiki Topic

Article Example
Machine learning Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies.
Machine learning Machine Learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use, thus digitizing cultural prejudices. Responsible collection of data thus is a critical part of machine learning.
Machine learning Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on "known" properties learned from the training data, data mining focuses on the discovery of (previously) "unknown" properties in the data (this is the analysis step of Knowledge Discovery in Databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to "reproduce known" knowledge, while in Knowledge Discovery and Data Mining (KDD) the key task is the discovery of previously "unknown" knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.
Waffles (machine learning) The Waffles machine learning toolkit contains command-line tools for performing various operations related to machine learning, data mining, and predictive modeling. The primary focus of Waffles is to provide tools that are simple to use in scripted experiments or processes. For example, the supervised learning algorithms included in Waffles are all designed to support multi-dimensional labels, classification and regression, automatically impute missing values, and automatically apply necessary filters to transform the data to a type that the algorithm can support, such that arbitrary learning algorithms can be used with arbitrary data sets. Many other machine learning toolkits provide similar functionality, but require the user to explicitly configure data filters and transformations to make it compatible with a particular learning algorithm. The algorithms provided in Waffles also have the ability to automatically tune their own parameters (with the cost of additional computational overhead).
Machine learning Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.
Machine learning Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed." Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or unfeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), learning to rank and computer vision.
Machine learning Machine learning and statistics are closely related fields. According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. He also suggested the term data science as a placeholder to call the overall field.
Active learning (machine learning) Recent developments are dedicated to hybrid active learning and active learning in a single-pass (on-line) context, combining concepts from the field of Machine Learning (e.g., conflict and ignorance) with adaptive, incremental learning policies in the field of Online machine learning.
Adversarial machine learning Machine learning algorithms are often re-trained on data collected during operation to adapt to changes in the underlying data distribution. For instance, intrusion detection systems (IDSs) are often re-trained on a set of samples collected during network operation. Within this scenario, an attacker may poison the training data by injecting carefully designed samples to eventually compromise the whole learning process. Poisoning may thus be regarded as an adversarial contamination of the training data. Examples of poisoning attacks against machine learning algorithms (including learning in the presence of worst-case adversarial label flips in the training data) can be found in.
Active learning (machine learning) Active learning is a special case of semi-supervised machine learning in which a learning algorithm is able to interactively query the user (or some other information source) to obtain the desired outputs at new data points. In statistics literature it is sometimes also called optimal experimental design.
Online machine learning In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update our best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms. It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data, or when the data itself is generated as a function of time, e.g. stock price prediction.
Machine learning Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component (e.g. typically a genetic algorithm) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning). They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.
Machine learning Leo Breiman distinguished two statistical modelling paradigms: data model and algorithmic model, wherein 'algorithmic model' means more or less the machine learning algorithms like Random forest.
Machine learning Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves `rules’ to store, manipulate or apply, knowledge. The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learners that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems.
Data mining It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms ("large scale") "data analysis" and "analytics" – or, when referring to actual methods, "artificial intelligence" and "machine learning" – are more appropriate.
Machine learning Some statisticians have adopted methods from machine learning, leading to a combined field that they call "statistical learning".
Machine learning Machine learning tasks are typically classified into three broad categories, depending on the nature of the learning "signal" or "feedback" available to a learning system. These are
Outline of machine learning Machine learning – subfield of computer science (more particularly soft computing) that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed". Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from an example "training set" of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.
Data-driven learning Data-driven learning is a learning approach (and in particular, an approach to learning foreign languages) in which learning is driven by research-like access to linguistic data.
Quantum machine learning In this context, many machine learning techniques can be used to more efficiently address experimentally relevant problems. Indeed, the problem of turning huge and noisy data sets into meaningful information is by no means unique to the quantum laboratory. This results in many machine learning techniques being naturally adapted to tackle these problems. Notable examples are those of extracting information on a given quantum state, or on a given quantum process. A list of problems that have been addressed with machine learning techniques includes: