Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
Machine Learning for Trading
This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies.
-
Course by
-
Self Paced
-
الإنجليزية
Using R for Regression and Machine Learning in Investment
In this course, the instructor will discuss various uses of regression in investment problems, and she will extend the discussion to logistic, Lasso, and Ridge regressions. At the same time, the instructor will introduce various concepts of machine learning. You can consider this course as the first step toward using machine learning methodologies in solving investment problems. The course will cover investment analysis topics, but at the same time, make you practice it using R programming.
-
Course by
-
Self Paced
-
18 ساعات
-
الإنجليزية
Introduction to Discrete Mathematics for Computer Science
Discrete Mathematics is the language of Computer Science. One needs to be fluent in it to work in many fields including data science, machine learning, and software engineering (it is not a coincidence that math puzzles are often used for interviews). We introduce you to this language through a fun try-this-before-we-explain-everything approach: first you solve many interactive puzzles that are carefully designed specifically for this online specialization, and then we explain how to solve the puzzles, and introduce important ideas along the way.
-
Course by
-
Self Paced
-
الإنجليزية
Deep Learning and Reinforcement Learning
This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning.
-
Course by
-
32 ساعات
-
الإنجليزية
Preparing for Google Cloud Certification: Machine Learning Engineer
87% of Google Cloud certified users feel more confident in their cloud skills.
-
Course by
-
Self Paced
-
الإنجليزية
Learn SQL Basics for Data Science
This Specialization is intended for a learner with no previous coding experience seeking to develop SQL query fluency. Through four progressively more difficult SQL projects with data science applications, you will cover topics such as SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, Delta Lake and more.
-
Course by
-
Self Paced
-
الإنجليزية
Launching into Machine Learning - 한국어
이 과정에서는 먼저 데이터에 관해 논의하면서 데이터 품질을 개선하고 탐색적 데이터 분석을 수행하는 방법을 알아봅니다. Vertex AI AutoML과 코드를 한 줄도 작성하지 않고 ML 모델을 빌드하고, 학습시키고, 배포하는 방법을 설명합니다. 학습자는 Big Query ML의 이점을 이해할 수 있습니다. 그런 다음, 머신러닝(ML) 모델 최적화 방법과 일반화 및 샘플링으로 커스텀 학습용 ML 모델 품질을 평가하는 방법을 다룹니다.
-
Course by
-
Self Paced
-
الإنجليزية
Machine Learning: Algorithms in the Real World
This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application. After completing all four courses, you will have gone through the entire process of building a machine learning project.
-
Course by
-
Self Paced
-
الإنجليزية
Computational Social Science
For more information please view the Computational Social Science Trailer Digital technology has not only revolutionized society, but also the way we can study it. Currently, this is taken advantage of by the most valuable companies in Silicon Valley, the most powerful governmental agencies, and the most influential social movements. What they have in common is that they use computational tools to understand, and ultimately influence human behavior and social dynamics. An increasing part of human interaction leaves a massive digital footprint behind.
-
Course by
-
Self Paced
-
الإنجليزية
Reinforcement Learning in Finance
This course aims at introducing the fundamental concepts of Reinforcement Learning (RL), and develop use cases for applications of RL for option valuation, trading, and asset management.
-
Course by
-
Self Paced
-
17 ساعات
-
الإنجليزية
GPU Programming
This specialization is intended for data scientists and software developers to create software that uses commonly available hardware. Students will be introduced to CUDA and libraries that allow for performing numerous computations in parallel and rapidly. Applications for these skills are machine learning, image/audio signal processing, and data processing.
-
Course by
-
Self Paced
-
الإنجليزية
AI for Good
The AI for Good Specialization showcases how AI can be part of the solution when it comes to addressing some of the world’s biggest challenges in areas like public health, climate change, and disaster management. In these courses, you’ll learn from instructor Robert Monarch, who has over 20 years of experience building AI products in industry and working at the intersection of AI and public health and disaster management.
-
Course by
-
Self Paced
-
الإنجليزية
Hyperparameter Tuning with Keras Tuner
In this 2-hour long guided project, we will use Keras Tuner to find optimal hyperparamters for a Keras model. Keras Tuner is an open source package for Keras which can help machine learning practitioners automate Hyperparameter tuning tasks for their Keras models. The concepts learned in this project will apply across a variety of model architectures and problem scenarios. Please note that we are going to learn to use Keras Tuner for hyperparameter tuning, and are not going to implement the tuning algorithms ourselves.
-
Course by
-
Self Paced
-
3 ساعات
-
الإنجليزية
Preparing Data for Machine Learning Models
By the end of this project, you will extract colors pixels as training dataset into a form where you can feed it to your Machine Learning Model using numpy arrays.
In this project we will work with images, you will get introduced to computer vision basic concepts.
Moreover, you will be able to properly handle arrays and preprocess your training dataset and label it.
Extracting features and preparing data is a very crucial task as it influences your model.
-
Course by
-
Self Paced
-
3 ساعات
-
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.
-
Course by
-
Self Paced
-
33 ساعات
-
الإنجليزية
Scikit-Learn to Solve Regression Machine Learning Problems
Hello everyone and welcome to this new hands-on project on Scikit-Learn for solving machine learning regression problems. In this project, we will learn how to build and train regression models using Scikit-Learn library. Scikit-learn is a free machine learning library developed for python. Scikit-learn offers several algorithms for classification, regression, and clustering. Several famous machine learning models are included such as support vector machines, random forests, gradient boosting, and k-means. This project is practical and directly applicable to many industries.
-
Course by
-
Self Paced
-
3 ساعات
-
الإنجليزية
Predictive Analytics for Business with H2O in R
This is a hands-on, guided project on Predictive Analytics for Business with H2O in R. By the end of this project, you will be able apply machine learning and predictive analytics to solve a business problem, explain and describe automatic machine learning, perform automatic machine learning (AutoML) with H2O in R.
-
Course by
-
Self Paced
-
3 ساعات
-
الإنجليزية
Health Data Science Foundation
This course is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios. We cover deep learning (DL) methods, healthcare data and applications using DL methods.
-
Course by
-
24 ساعات
-
الإنجليزية
Employee Attrition Prediction Using Machine Learning
In this project-based course, we will build, train and test a machine learning model to predict employee attrition using features such as employee job satisfaction, distance from work, compensation and performance. We will explore two machine learning algorithms, namely: (1) logistic regression classifier model and (2) Extreme Gradient Boosted Trees (XG-Boost). This project could be effectively applied in any Human Resources department to predict which employees are more likely to quit based on their features.
-
Course by
-
3 ساعات
-
الإنجليزية
Machine Learning in the Enterprise - 日本語版
このコースでは、ML ワークフローに対する実践的なアプローチを取り上げます。具体的には、いくつかの ML のビジネス要件とユースケースに取り組む ML チームをケーススタディ形式で紹介します。このチームは、データ マネジメントとガバナンスに必要なツールを理解し、データの前処理に最適なアプローチを検討する必要があります。 2 つのユースケースに対して ML モデルを構築するための 3 つのオプションがチームに提示されます。このコースでは、チームの目標を達成するために、AutoML、BigQuery ML、カスタム トレーニングを使用する理由について説明します。
-
Course by
-
Self Paced
-
الإنجليزية
Solve Business Problems with AI and Machine Learning
Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This is the first of four courses in the Certified Artificial Intelligence Practitioner (CAIP) professional certification. This course is meant as an entry point into the world of AI/ML.
-
Course by
-
Self Paced
-
11 ساعات
-
الإنجليزية
Unsupervised Machine Learning for Customer Market Segmentation
In this hands-on guided project, we will train unsupervised machine learning algorithms to perform customer market segmentation. Market segmentation is crucial for marketers since it enables them to launch targeted ad marketing campaigns that are tailored to customer's specific needs.
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.
-
Course by
-
Self Paced
-
3 ساعات
-
الإنجليزية
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
-
Course by
-
Self Paced
-
3 ساعات
-
الإنجليزية
Machine Learning with H2O Flow
This is a hands-on, guided introduction to using H2O Flow for machine learning. By the end of this project, you will be able to train and evaluate machine learning models with H2O Flow and AutoML, without writing a single line of code! You will use the point and click, web-based interface to H2O called Flow to solve a business analytics problem with machine learning.
-
Course by
-
Self Paced
-
3 ساعات
-
الإنجليزية
Python and Machine Learning for Asset Management
This course will enable you mastering machine-learning approaches in the area of investment management. It has been designed by two thought leaders in their field, Lionel Martellini from EDHEC-Risk Institute and John Mulvey from Princeton University.
-
Course by
-
Self Paced
-
16 ساعات
-
الإنجليزية