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How Google does Machine Learning
دوراتنا
Classification of COVID19 using Chest X-ray Images in Keras
In this 1 hour long project-based course, you will learn to build and train a convolutional neural network in Keras with TensorFlow as backend from scratch to classify patients as infected with COVID or not using their chest x-ray images. Our goal is to create an image classifier with Tensorflow by implementing a CNN to differentiate between chest x rays images with a COVID 19 infections versus without. The dataset contains the lungs X-ray images of both groups.We will be carrying out the entire project on the Google Colab environment.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
University Admission Prediction Using Multiple Linear Regression
In this hands-on guided project, we will train regression models to find the probability of a student getting accepted into a particular university based on their profile. This project could be practically used to get the university acceptance rate for individual students using web application.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Machine Learning Pipelines with Azure ML Studio
In this project-based course, you are going to build an end-to-end machine learning pipeline in Azure ML Studio, all without writing a single line of code! This course uses the Adult Income Census data set to train a model to predict an individual's income. It predicts whether an individual's annual income is greater than or less than $50,000. The estimator used in this project is a Two-Class Boosted Decision Tree classifier. Some of the features used to train the model are age, education, occupation, etc.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Life Expectancy Prediction Using Machine Learning
In this hands-on project, we will train a Linear Regression model to predict life expectancy. The dataset was initially obtained from the World Health Organization (WHO) and United Nations Websites. Data contains features such as year, status, life expectancy, adult mortality, infant deaths, percentage of expenditure, and alcohol consumption.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Medical Diagnosis using Support Vector Machines
In this one hour long project-based course, you will learn the basics of support vector machines using Python and scikit-learn. The dataset we are going to use comes from the National Institute of Diabetes and Digestive and Kidney Diseases, and contains anonymized diagnostic measurements for a set of female patients. We will train a support vector machine to predict whether a new patient has diabetes based on such measurements. By the end of this course, you will be able to model an existing dataset with the goal of making predictions about new data.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Demand Forecasting Using Time Series
This course is the second in a specialization for Machine Learning for Supply Chain Fundamentals. In this course, we explore all aspects of time series, especially for demand prediction. We'll start by gaining a foothold in the basic concepts surrounding time series, including stationarity, trend (drift), cyclicality, and seasonality. Then, we'll spend some time analyzing correlation methods in relation to time series (autocorrelation). In the 2nd half of the course, we'll focus on methods for demand prediction using time series, such as autoregressive models.
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Course by
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Self Paced
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9 ساعات
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الإنجليزية
Forecast bikeshare demand using time series models in R
In this project, you’ll help a bike rental company enhance its fleet management and pricing strategy by building a daily bike rental forecasting model using time series analysis techniques in R. Your objectives include loading, cleaning, processing, and analyzing daily rental transaction data, and developing and evaluating time series models for the most accurate predictions. The company will use your validated forecasting model to determine the optimal number of bikes to keep in each station and set dynamic pricing based on predicted demand.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Medical Insurance Premium Prediction with Machine Learning
In this 1-hour long project-based course, you will learn how to predict medical insurance cost with machine learning. The objective of this case study is to predict the health insurance cost incurred by Individuals based on their age, gender, Body Mass Index (BMI), number of children, smoking habits, and geo-location. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
What is Data Science?
Do you want to know why data science has been labeled the sexiest profession of the 21st century? After taking this course, you will be able to answer this question, understand what data science is and what data scientists do, and learn about career paths in the field. The art of uncovering insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and accurately predicted the Nile River's flooding every year. Since then, people have continued to use data to derive insights and predict outcomes.
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Course by
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Self Paced
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19 ساعات
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الإنجليزية
Population Health: Predictive Analytics
Predictive analytics has a longstanding tradition in medicine. Developing better prediction models is a critical step in the pursuit of improved health care: we need these tools to guide our decision-making on preventive measures, and individualized treatments. In order to effectively use and develop these models, we must understand them better. In this course, you will learn how to make accurate prediction tools, and how to assess their validity. First, we will discuss the role of predictive analytics for prevention, diagnosis, and effectiveness.
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Course by
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Self Paced
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17 ساعات
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الإنجليزية
Predicting the Weather with Artificial Neural Networks
In this one hour long project-based course, you will tackle a real-world prediction problem using machine learning. The dataset we are going to use comes from the Australian government. They recorded daily weather observations from a number of Australian weather stations. We will use this data to train an artificial neural network to predict whether it will rain tomorrow.
By the end of this project, you will have created a machine learning model using industry standard tools, including Python and sklearn.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Financial Analysis of Organizations
This course focuses on adopting and implementing a financially analytic mindset when analyzing organizational activities, position and performance. This course begins with an overview of an organization’s financial statements, including the balance sheet, income statement, cash flow statement, as well as the transactions that comprise these statements. You will learn about tools and ratios that help analyze these financial statements and transactions. Financial statement analysis will help you understand how the company has performed in the past and its current position.
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Course by
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Self Paced
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16 ساعات
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الإنجليزية
Diabetes Prediction With Pyspark MLLIB
In this 1 hour long project-based course, you will learn to build a logistic regression model using Pyspark MLLIB to classify patients as either diabetic or non-diabetic. We will use the popular Pima Indian Diabetes data set. Our goal is to use a simple logistic regression classifier from the pyspark Machine learning library for diabetes classification. We will be carrying out the entire project on the Google Colab environment with the installation of Pyspark.You will need a free Gmail account to complete this project.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Titanic Survival Prediction Using Machine Learning
In this 1-hour long project-based course, we will predict titanic survivors’ using logistic regression and naïve bayes classifiers. The sinking of the Titanic is one of the key sad tragedies in history and it took place on April 15th, 1912. The numbers of survivors were low due to lack of lifeboats for all passengers. This practical guided project, we will analyze what sorts of people were likely to survive this tragedy with the power of machine learning. Note: This course works best for learners who are based in the North America region.
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Course by
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3 ساعات
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الإنجليزية
Build Regression, Classification, and Clustering Models
In most cases, the ultimate goal of a machine learning project is to produce a model. Models make decisions, predictions—anything that can help the business understand itself, its customers, and its environment better than a human could. Models are constructed using algorithms, and in the world of machine learning, there are many different algorithms to choose from.
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Course by
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Self Paced
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20 ساعات
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الإنجليزية
Data Science Tutorial
Start learning Data science with the W3Schools course. Data Science is about data gathering, analysis and decision-making. Data Science is about finding patterns in data, through analysis, and make future predictions. This is a structured and interactive version of the W3Schools Data science Tutorial. The course is self-paced with text based modules, practical interactive examples and exercises to check your understanding as you progress. W3schools is the world's largest web developer learning site. Start learning with our proven tutorials used by millions of learners!
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Course by
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Self Paced
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7 ساعات
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الإنجليزية
Predict Baby Weight with TensorFlow on AI Platform
In this lab you train, evaluate, and deploy a machine learning model to predict a baby’s weight. You then send requests to the model to make online predictions. This lab is part of a series of labs on processing scientific data.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Interpretable machine learning applications: Part 5
You will be able to use the Aequitas Tool as a tool to measure and detect bias in the outcome of a machine learning prediction model. As a use case, we will be working with the dataset about recidivism, i.e., the likelihood for a former imprisoned person to commit another offence within the first two years, since release from prison. The guided project will be making use of the COMPAS dataset, which already includes predicted as well as actual outcomes.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Production Machine Learning Systems
In this course, we dive into the components and best practices of building high-performing ML systems in production environments. We cover some of the most common considerations behind building these systems, e.g. static training, dynamic training, static inference, dynamic inference, distributed TensorFlow, and TPUs. This course is devoted to exploring the characteristics that make for a good ML system beyond its ability to make good predictions.
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Course by
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Self Paced
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19 ساعات
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الإنجليزية
AI For Medical Treatment
AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine. Medical treatment may impact patients differently based on their existing health conditions. In this third course, you’ll recommend treatments more suited to individual patients using data from randomized control trials.
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Course by
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22 ساعات
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الإنجليزية
AI for Medical Prognosis
AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine. Machine learning is a powerful tool for prognosis, a branch of medicine that specializes in predicting the future health of patients. In this second course, you’ll walk through multiple examples of prognostic tasks.
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Course by
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30 ساعات
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الإنجليزية
Sequences, Time Series and Prediction
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In this fourth course, you will learn how to build time series models in TensorFlow. You’ll first implement best practices to prepare time series data. You’ll also explore how RNNs and 1D ConvNets can be used for prediction.
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Course by
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Self Paced
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23 ساعات
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الإنجليزية
Process Mining: Data science in Action
Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis.
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Course by
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Self Paced
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22 ساعات
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الإنجليزية
Bioinformatic Methods I
Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on.
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Course by
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Self Paced
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20 ساعات
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الإنجليزية
AI for Medical Diagnosis
AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. As an AI practitioner, you have the opportunity to join in this transformation of modern medicine. If you're already familiar with some of the math and coding behind AI algorithms, and are eager to develop your skills further to tackle challenges in the healthcare industry, then this specialization is for you. No prior medical expertise is required!
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Course by
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Self Paced
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20 ساعات
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الإنجليزية