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Our Courses
Machine Learning for Telecom Customers Churn Prediction
In this hands-on project, we will train several classification algorithms such as Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Random Forest Classifier to predict the churn rate of Telecommunication Customers.
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Course by
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Self Paced
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3 hours
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English
Introduction to Machine Learning: Supervised Learning
In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM. Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course.
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Course by
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Self Paced
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40 hours
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English
Google Data Analytics
Prepare for a new career in the high-growth field of data analytics, no experience or degree required. Get professional training designed by Google and have the opportunity to connect with top employers. There are 483,000 open jobs in data analytics with a median entry-level salary of $92,000.¹ Data analytics is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision making. Over 8 courses, gain in-demand skills that prepare you for an entry-level job.
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Self Paced
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English
Building Cloud Computing Solutions at Scale
With more companies leveraging software that runs on the Cloud, there is a growing need to find and hire individuals with the skills needed to build solutions on a variety of Cloud platforms. Employers agree: Cloud talent is hard to find.
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Self Paced
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English
Mining Quality Prediction Using Machine & Deep Learning
In this 1.5-hour long project-based course, you will be able to: - Understand the theory and intuition behind Simple and Multiple Linear Regression. - Import Key python libraries, datasets and perform data visualization - Perform exploratory data analysis and standardize the training and testing data. - Train and Evaluate different regression models using Sci-kit Learn library. - Build and train an Artificial Neural Network to perform regression. - Understand the difference between various regression models KPIs such as MSE, RMSE, MAE, R2, and adjusted R2. - Assess the performance of regressio
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Course by
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Self Paced
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2 hours
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English
Deploy Models with TensorFlow Serving and Flask
In this 2-hour long project-based course, you will learn how to deploy TensorFlow models using TensorFlow Serving and Docker, and you will create a simple web application with Flask which will serve as an interface to get predictions from the served TensorFlow model.
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Course by
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Self Paced
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3 hours
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English
Sports Performance Analytics
Sports analytics has emerged as a field of research with increasing popularity propelled, in part, by the real-world success illustrated by the best-selling book and motion picture, Moneyball.
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Course by
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English
Prediction and Control with Function Approximation
In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting.
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Course by
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22 hours
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English
Multiple Regression Analysis in Public Health
Biostatistics is the application of statistical reasoning to the life sciences, and it's the key to unlocking the data gathered by researchers and the evidence presented in the scientific public health literature. In this course, you'll extend simple regression to the prediction of a single outcome of interest on the basis of multiple variables. Along the way, you'll be introduced to a variety of methods, and you'll practice interpreting data and performing calculations on real data from published studies.
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Course by
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Self Paced
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14 hours
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English
Interpretable Machine Learning Applications: Part 4
In this 1-hour long guided project, you will learn how to use the "What-If" Tool (WIT) in the context of training and testing machine learning prediction models. In particular, you will learn a) how to set up a machine learning application in Python by using interactive Python notebook(s) on Google's Colab(oratory) environment, a.k.a.
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Course by
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Self Paced
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3 hours
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English
TensorFlow 2 for Deep Learning
This Specialization is intended for machine learning researchers and practitioners who are seeking to develop practical skills in the popular deep learning framework TensorFlow. The first course of this Specialization will guide you through the fundamental concepts required to successfully build, train, evaluate and make predictions from deep learning models, validating your models and including regularisation, implementing callbacks, and saving and loading models. The second course will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning mode
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Course by
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Self Paced
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English
Cloud Machine Learning Engineering and MLOps
Welcome to the fourth course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will build upon the Cloud computing and data engineering concepts introduced in the first three courses to apply Machine Learning Engineering to real-world projects. First, you will develop Machine Learning Engineering applications and use software development best practices to create Machine Learning Engineering applications. Then, you will learn to use AutoML to solve problems more efficiently than traditional machine learning approaches alone.
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Course by
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12 hours
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English
Data Science Coding Challenge: Loan Default Prediction
In this coding challenge, you'll compete with other learners to achieve the highest prediction accuracy on a machine learning problem. You'll use Python and a Jupyter Notebook to work with a real-world dataset and build a prediction or classification model. Important Information: How to register? To participate, you’ll need to complete simple steps. First, click the “Start Project” button to register. Next, you’ll need to create a Coursera Skills Profile, which only takes a few minutes.
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Course by
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Self Paced
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3 hours
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English
Physical and Advanced Side-Channel Attacks
Software-based and physical side-channel attacks have similar techniques. But physical attacks can observe properties and side effects that are usually not visible on the software layer. Thus, they are often considered the most dangerous side-channel attacks. In this course, we learn both about physical side-channel attacks but also about more advanced software-based side channels using prefetching and branch prediction. You will work with these attacks and understand how to mitigate them.
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Course by
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Self Paced
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English
How to Use Microsoft Azure ML Studio for Kaggle Competitions
In this 90 minutes long project-based course, you will learn how to create a Microsoft Azure ML Studio account, a Kaggle account for competitions and use both of them to build a machine learning model which we will be using to make predictions.
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Course by
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Self Paced
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3 hours
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English
Improving Your Statistical Questions
This course aims to help you to ask better statistical questions when performing empirical research. We will discuss how to design informative studies, both when your predictions are correct, as when your predictions are wrong. We will question norms, and reflect on how we can improve research practices to ask more interesting questions.
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Course by
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Self Paced
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18 hours
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English
Python Data Products for Predictive Analytics
Python data products are powering the AI revolution. Top companies like Google, Facebook, and Netflix use predictive analytics to improve the products and services we use every day. Take your Python skills to the next level and learn to make accurate predictions with data-driven systems and deploy machine learning models with this four-course Specialization from UC San Diego. This Specialization is for learners who are proficient with the basics of Python. You’ll start by creating your first data strategy.
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Course by
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Self Paced
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English
Machine/Deep Learning for Mining Quality Prediction-Enhanced
In this hands-on project, we will train machine learning and deep learning models to predict the % of Silica Concentrate in the Iron ore concentrate per minute. This project could be practically used in Mining Industry to get the % Silica Concentrate at much faster rate compared to the traditional methods.
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Course by
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Self Paced
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3 hours
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English
Deploy a BigQuery ML Customer Churn Classifier to Vertex AI for Online Predictions
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will train, tune, evaluate, explain, and generate batch and online predictions with a BigQuery ML XGBoost model. You will use a Google Analytics 4 dataset from a real mobile application, Flood it!, to determine the likelihood of users returning to the application. You will generate batch predictions with your BigQuery ML model as well as export and deploy it to Vertex AI for online predictions.
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Course by
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Self Paced
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2 hours
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English
Interpretable Machine Learning Applications: Part 1
In this 1-hour long project-based course, you will learn how to create interpretable machine learning applications on the example of two classification regression models, decision tree and random forestc classifiers. You will also learn how to explain such prediction models by extracting the most important features and their values, which mostly impact these prediction models. In this sense, the project will boost your career as Machine Learning (ML) developer and modeler in that you will be able to get a deeper insight into the behaviour of your ML model.
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3 hours
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English
Introduction to Business Analytics and Information Economics
This specialization targets learners who seek to understand the opportunities that data and analytics present for their organization and those interested in the value of and implications for data as an asset to their organization. Individuals who manage data and make decisions about how data can be leveraged in their organization will find this specialization of particular value. Businesses run on data, and data offers little value without analytics.
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Course by
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Self Paced
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English
Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership
Machine learning runs the world. It generates predictions for each individual customer, employee, voter, and suspect, and these predictions drive millions of business decisions more effectively, determining whom to call, mail, approve, test, diagnose, warn, investigate, incarcerate, set up on a date, or medicate. But, to make this work, you've got to bridge what is a prevalent gap between business leadership and technical know-how. Launching machine learning is as much a management endeavor as a technical one. Its success relies on a very particular business leadership practice.
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Course by
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Self Paced
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14 hours
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English
Bayesian Statistics
This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm. The course will apply Bayesian methods to several practical problems, to show end-to-end Bayesian analyses that move from framing the question to building models to eliciting prior probabilities to implementing in R (free statistical software) the final posterior distribution.
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Course by
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Self Paced
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English
AI for Medicine
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 three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine. These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend taking the Deep Learning Specialization.
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Course by
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Self Paced
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English
Predictive Analytics: Basic Modeling Techniques
What is Predictive Analytics? These methods lie behind the most transformative technologies of the last decade, that go under the more general name Artificial Intelligence or AI. In this course, the focus is on the skills that will allow you to fit a model to data, and measure how well it performs. We will be doing enough data science so that you get hands-on familiarity with understanding a dataset, fitting a model to it, and generating predictions.
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26
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English