Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
Bank Loan Approval Prediction With Artificial Neural Nets
In this hands-on project, we will build and train a simple deep neural network model to predict the approval of personal loan for a person based on features like age, experience, income, locations, family, education, exiting mortgage, credit card etc.
By the end of this project, you will be able to:
- Understand the applications of Artificial Intelligence and Machine Learning techniques in the banking industry
- Understand the theory and intuition behind Deep Neural Networks
- Import key Python libraries, dataset, and perform Exploratory Data Analysis.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Personalised Medicine from a Nordic Perspective
The technical revolution has generated large amounts of data in healthcare and research, and a rapidly increasing knowledge about factors of importance for the individual’s health.
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Course by
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Self Paced
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14 ساعات
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الإنجليزية
Introduction to Embedded Machine Learning
Machine learning (ML) allows us to teach computers to make predictions and decisions based on data and learn from experiences. In recent years, incredible optimizations have been made to machine learning algorithms, software frameworks, and embedded hardware.
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Course by
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Self Paced
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17 ساعات
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الإنجليزية
Communicating Data Science Results
Important note: The second assignment in this course covers the topic of Graph Analysis in the Cloud, in which you will use Elastic MapReduce and the Pig language to perform graph analysis over a moderately large dataset, about 600GB. In order to complete this assignment, you will need to make use of Amazon Web Services (AWS). Amazon has generously offered to provide up to $50 in free AWS credit to each learner in this course to allow you to complete the assignment.
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Course by
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Self Paced
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8 ساعات
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الإنجليزية
Support Vector Machine Classification in Python
In this 1-hour long guided project-based course, you will learn how to use Python to implement a Support Vector Machine algorithm for classification. This type of algorithm classifies output data and makes predictions. The output of this model is a set of visualized scattered plots separated with a straight line.
You will learn the fundamental theory and practical illustrations behind Support Vector Machines and learn to fit, examine, and utilize supervised Classification models using SVM to classify data, using Python.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Bike Rental Sharing Demand Prediction with Machine Learning
In this 1-hour long project-based course, you will learn how to predict bike sharing demand with machine learning. Bike sharing services enable people to rent a bike from one location and drop it off at another location …
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Course by
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Self Paced
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الإنجليزية
Custom Prediction Routine on Google AI Platform
Please note: You will need a Google Cloud Platform account to complete this course. Your GCP account will be charged as per your usage. Please make sure that you are able to access Google AI Platform within your GCP account. You should be familiar with python programming, and Google Cloud Platform before starting this hands on project. Please also ensure that you have access to the custom prediction routine feature in Google AI Platform.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Analytic Combinatorics
Analytic Combinatorics teaches a calculus that enables precise quantitative predictions of large combinatorial structures. This course introduces the symbolic method to derive functional relations among ordinary, exponential, and multivariate generating functions, and methods in complex analysis for deriving accurate asymptotics from the GF equations. All the features of this course are available for free.
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Course by
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Self Paced
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17 ساعات
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الإنجليزية
Supervised Learning and Its Applications in Marketing
Welcome to the Supervised Learning and Its Applications in Marketing course! Supervised learning is the process of making an algorithm to learn to map an input to a particular output. Supervised learning algorithms can help make predictions for new unseen data. In this course, you will use the Python programming language, which is an effective tool for machine learning applications. You will be introduced to the supervised learning techniques: regression and classification. The course will focus on the applications of these techniques in the domain of marketing.
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Course by
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Self Paced
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22 ساعات
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الإنجليزية
Get Familiar with ML basics in a Kaggle Competition
In this 1-hour long project, you will be able to understand how to predict which passengers survived the Titanic shipwreck and make your first submission in an Machine Learning competition inside the Kaggle platform. Also, you as a beginner in Machine Learning applications, will get familiar and get a deep understanding of how to start a model prediction using basic supervised Machine Learning models. We will choose classifiers to learn, predict, and make an Exploratory Data Analysis (also called EDA).
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Demand Analytics
Welcome to Demand Analytics - one of the most sought-after skills in supply chain management and marketing! Through the real-life story and data of a leading cookware manufacturer in North America, you will learn the data analytics skills for demand planning and forecasting. Upon the completion of this course, you will be able to 1. Improve the forecasting accuracy by building and validating demand prediction models. 2.
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Course by
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Self Paced
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9 ساعات
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الإنجليزية
Using Machine Learning in Trading and Finance
This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading.
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Course by
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Self Paced
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19 ساعات
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الإنجليزية
Resampling, Selection and Splines
"Statistical Learning for Data Science" is an advanced course designed to equip working professionals with the knowledge and skills necessary to excel in the field of data science. Through comprehensive instruction on key topics such as shrink methods, parametric regression analysis, generalized linear models, and general additive models, students will learn how to apply resampling methods to gain additional information about fitted models, optimize fitting procedures to improve prediction accuracy and interpretability, and identify the benefits and approach of non-linear models.
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Course by
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Self Paced
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16 ساعات
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الإنجليزية
Basic Recommender Systems
The Basic Recommender Systems course introduces you to the leading approaches in recommender systems. The techniques described touch both collaborative and content-based approaches and include the most important algorithms used to provide recommendations. You'll learn how they work, how to use and how to evaluate them, pointing out benefits and limits of different recommender system alternatives. After completing this course, you'll be able to describe the requirements and objectives of recommender systems based on different application domains.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Data Science Challenge
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 ساعات
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الإنجليزية
Introduction to Predictive Modeling
Welcome to Introduction to Predictive Modeling, the first course in the University of Minnesota’s Analytics for Decision Making specialization. This course will introduce to you the concepts, processes, and applications of predictive modeling, with a focus on linear regression and time series forecasting models and their practical use in Microsoft Excel.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Emergent Phenomena in Science and Everyday Life
Before the advent of quantum mechanics in the early 20th century, most scientists believed that it should be possible to predict the behavior of any object in the universe simply by understanding the behavior of its constituent parts. For instance, if one could write down the equations of motion for every atom in a system, it should be possible to solve those equations (with the aid of a sufficiently large computing device) and make accurate predictions about that system’s future. However, there are some systems that defy this notion.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Machine Learning with PySpark: Customer Churn Analysis
This 90-minute guided-project, "Pyspark for Data Science: Customer Churn Prediction," is a comprehensive guided-project that teaches you how to use PySpark to build a machine learning model for predicting customer churn in a Telecommunications company. This guided-project covers a range of essential tasks, including data loading, exploratory data analysis, data preprocessing, feature preparation, model training, evaluation, and deployment, all using Pyspark.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Advanced Recommender Systems
In this course, you will see how to use advanced machine-learning techniques to build more sophisticated recommender systems. Machine Learning is able to provide recommendations and make better predictions, by taking advantage of historical opinions from users and building up the model automatically, without the need for you to think about all the details of the model. At the end of the Advanced Recommender Systems, you will know how to manage hybrid information and how to combine different filtering techniques, taking the best from each approach.
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Course by
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15 ساعات
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الإنجليزية
Shaping Urban Futures
If we are to make sense of the future of humanity, we must understand the future of the city.
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Course by
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21 ساعات
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الإنجليزية
Explaining machine learning models
In this 2-hour long project-based course, you will learn how to understand the predictions of your model, feature relations, visualize and interpret feature & model relation with statistics and much more.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
The Impact of Technology
In this course, you will learn how to use theories and methods to make predictions of the potential impact of new technology – in general, as well as for a specific application. This will prepare you for the changes that we are facing in society, and also help you take advantage of transformations that are driven by technology. The Impact of Technology course takes a broad approach, including social and economic factors. You will study historical examples and learn to recognize recurring patterns and cycles.
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Course by
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Self Paced
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23 ساعات
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الإنجليزية
Graduate Admission Prediction with Pyspark ML
In this 1 hour long project-based course, you will learn to build a linear regression model using Pyspark ML to predict students' admission at the university. We will use the graduate admission 2 data set from Kaggle. Our goal is to use a Simple Linear Regression Machine Learning Algorithm from the Pyspark Machine learning library to predict the chances of getting admission. 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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2 ساعات
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الإنجليزية
Optimize ML Models and Deploy Human-in-the-Loop Pipelines
In the third course of the Practical Data Science Specialization, you will learn a series of performance-improvement and cost-reduction techniques to automatically tune model accuracy, compare prediction performance, and generate new training data with human intelligence. After tuning your text classifier using Amazon SageMaker Hyper-parameter Tuning (HPT), you will deploy two model candidates into an A/B test to compare their real-time prediction performance and automatically scale the winning model using Amazon SageMaker Hosting.
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Course by
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Self Paced
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11 ساعات
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الإنجليزية
Data Analysis with Python
Analyzing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the basics of data analysis with Python to building and evaluating data models.
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Course by
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Self Paced
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15 ساعات
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الإنجليزية