Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
XG-Boost 101: Used Cars Price Prediction
In this hands-on project, we will train 3 Machine Learning algorithms namely Multiple Linear Regression, Random Forest Regression, and XG-Boost to predict used cars prices. This project can be used by car dealerships to predict used car prices and understand the key factors that contribute to used car prices.
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 XG-Boost Algorithm
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Course by
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Self Paced
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3 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
Unsupervised Learning, Recommenders, Reinforcement Learning
In the third course of the Machine Learning Specialization, you will: • Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection. • Build recommender systems with a collaborative filtering approach and a content-based deep learning method. • Build a deep reinforcement learning model. The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online.
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Course by
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Self Paced
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28 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
Topics in Applied Econometrics
In this course, you will discover models and approaches that are designed to deal with challenges raised by the empirical econometric modelling and particular types of data. You will: – Explore the motivations of each approach by means of graphs, preliminary statistics and presentation of economic theories – Discuss the problem of identification of the parameters, and how to address this problem by modelling simultaneous equations and causality in economics.
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Course by
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Self Paced
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28 ساعات
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الإنجليزية
The Econometrics of Time Series Data
In this course, you will look at models and approaches that are designed to deal with challenges raised by time series data. The discussion covers the motivation for the use of particular models and the description of the characteristics of time series data, with a special attention raised to the potential memory. You will: – Discuss time series models, that refer to data that have been collected over a period on one or more variables for the same individual.
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Course by
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Self Paced
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31 ساعات
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الإنجليزية
The Classical Linear Regression Model
In this course, you will discover the type of questions that econometrics can answer, and the different types of data you might use: time series, cross-sectional, and longitudinal data.
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Course by
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Self Paced
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22 ساعات
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الإنجليزية
Survival Analysis in R for Public Health
Welcome to Survival Analysis in R for Public Health! The three earlier courses in this series covered statistical thinking, correlation, linear regression and logistic regression. This one will show you how to run survival – or “time to event” – analysis, explaining what’s meant by familiar-sounding but deceptive terms like hazard and censoring, which have specific meanings in this context.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Supervised Machine Learning: Regression and Classification
In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online.
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Course by
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Self Paced
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33 ساعات
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الإنجليزية
Supervised Machine Learning: Regression
This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models.
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Course by
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Self Paced
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21 ساعات
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الإنجليزية
Statistics with SAS
This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression.
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Course by
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Self Paced
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20 ساعات
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الإنجليزية
Statistics for Marketing
This course takes a deep dive into the statistical foundation upon which marketing analytics is built. The first part of this course will help you to thoroughly understand your dataset and what the data actually means. Then, it will go into sampling including how to ask specific questions about your data and how to conduct analysis to answer those questions.
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Course by
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Self Paced
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17 ساعات
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الإنجليزية
Statistics for Machine Learning for Investment Professionals
One of the biggest changes in the past decade is the rapid adoption of machine learning, AI, and big data in investment decision making. This course introduces learners with knowledge of the investment industry to foundational statistical concepts underpinning machine learning as well as advanced AI techniques. This course demonstrates core modeling frameworks along with carefully selected real-world investment practice examples. The course seeks to familiarize learners with two important programming languages — Python and R (no prior knowledge of Python or R necessary).
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Course by
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Self Paced
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18 ساعات
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الإنجليزية
Statistical Modeling for Data Science Applications
Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models.
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Course by
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Self Paced
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الإنجليزية
Statistical Analysis with R for Public Health
Statistics are everywhere. The probability it will rain today. Trends over time in unemployment rates. The odds that India will win the next cricket world cup. In sports like football, they started out as a bit of fun but have grown into big business. Statistical analysis also has a key role in medicine, not least in the broad and core discipline of public health. In this specialisation, you’ll take a peek at what medical research is and how – and indeed why – you turn a vague notion into a scientifically testable hypothesis.
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Course by
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Self Paced
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الإنجليزية
Simple Linear Regression for the Absolute Beginner
Hello everyone and welcome to this hands-on guided project on simple linear regression for the absolute beginner. In simple linear regression, we predict the value of one variable Y based on another variable X. X is called the independent variable and Y is called the dependent variable. This guided project is practical and directly applicable to many industries. You can add this project to your portfolio of projects which is essential for your next job interview.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Reinforcement Learning for Trading Strategies
In the final course from the Machine Learning for Trading specialization, you will be introduced to reinforcement learning (RL) and the benefits of using reinforcement learning in trading strategies. You will learn how RL has been integrated with neural networks and review LSTMs and how they can be applied to time series data.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Regression with Automatic Differentiation in TensorFlow
In this 1.5 hour long project-based course, you will learn about constants and variables in TensorFlow, you will learn how to use automatic differentiation, and you will apply automatic differentiation to solve a linear regression problem. By the end of this project, you will have a good understanding of how machine learning algorithms can be implemented in TensorFlow.
In order to be successful in this project, you should be familiar with Python, Gradient Descent, Linear Regression.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Regression Modeling in Practice
This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results.
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Course by
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11 ساعات
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الإنجليزية
Regression Modeling Fundamentals
This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Regression Analysis: Simplify Complex Data Relationships
This is the fifth of eight courses in the Google Advanced Data Analytics Certificate. Data professionals use regression analysis to discover the relationships between different variables in a dataset and identify key factors that affect business performance. In this course, you’ll practice modeling variable relationships. You'll learn about different methods of data modeling and how to use them to approach business problems. You’ll also explore methods such as linear regression, analysis of variance (ANOVA), and logistic regression.
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Course by
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Self Paced
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32 ساعات
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الإنجليزية
Regression Analysis with Yellowbrick
Welcome to this project-based course on Regression Analysis with Yellowbrick. In this project, we will build a machine learning model to predict the compressive strength of high performance concrete (HPC). Although, we will use linear regression, the emphasis of this project will be on using visualization techniques to steer our machine learning workflow. Visualization plays a crucial role throughout the analytical process. It is indispensable for any effective analysis, model selection, and evaluation. This project will make use of a diagnostic platform called Yellowbrick.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Regression Analysis
The "Regression Analysis" course equips students with the fundamental concepts of one of the most important supervised learning methods, regression. Participants will explore various regression techniques and learn how to evaluate them effectively. Additionally, students will gain expertise in advanced topics, including polynomial regression, regularization techniques (Ridge, Lasso, and Elastic Net), cross-validation, and ensemble methods (bagging, boosting, and stacking).
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Course by
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Self Paced
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40 ساعات
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الإنجليزية
Quantifying Relationships with Regression Models
This course will introduce you to the linear regression model, which is a powerful tool that researchers can use to measure the relationship between multiple variables. We’ll begin by exploring the components of a bivariate regression model, which estimates the relationship between an independent and dependent variable. Building on this foundation, we’ll then discuss how to create and interpret a multivariate model, binary dependent variable model and interactive model.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Python and Statistics for Financial Analysis
Course Overview: https://youtu.be/JgFV5qzAYno Python is now becoming the number 1 programming language for data science. Due to python’s simplicity and high readability, it is gaining its importance in the financial industry.
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Course by
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Self Paced
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13 ساعات
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الإنجليزية