Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
Logistic Regression with NumPy and Python
Welcome to this project-based course on Logistic with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery, including gradient descent, cost function, and logistic regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals.
-
Course by
-
Self Paced
-
2 ساعات
-
الإنجليزية
Fake Instagram Profile Detector
In this hands-on project, we will build and train a simple artificial neural network model to detect spam/fake Instagram accounts. Fake and spam accounts are a major problem in social media. Many social media influencers use fake Instagram accounts to create an illusion of having so many social media followers. Fake accounts can be used to impersonate or catfish other people and be used to sell fake services/products.
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
-
Course by
-
Self Paced
-
4 ساعات
-
الإنجليزية
Data Analytics in Accounting Capstone
This capstone is the last course in the Data Analytics in Accountancy Specialization. In this capstone course, you are going to take the knowledge and skills you have acquired from the previous courses and apply them to a real-world problem.
-
Course by
-
Self Paced
-
19 ساعات
-
الإنجليزية
Basic Statistics in Python (Correlations and T-tests)
By the end of this project, you will learn how to use Python for basic statistics (including t-tests and correlations). We will learn all the important steps of analysis, including loading, sorting and cleaning data. In this course, we will use exploratory data analysis to understand our data and plot boxplots to visualize the data. Boxplots also allow us to investigate any outliers in our datasets. We will then learn how to examine relationships between the different data using correlations and scatter plots. Finally, we will compare data using t-tests.
-
Course by
-
Self Paced
-
3 ساعات
-
الإنجليزية
Introduction to Data Science and scikit-learn in Python
This course will teach you how to leverage the power of Python and artificial intelligence to create and test hypothesis. We'll start for the ground up, learning some basic Python for data science before diving into some of its richer applications to test our created hypothesis. We'll learn some of the most important libraries for exploratory data analysis (EDA) and machine learning such as Numpy, Pandas, and Sci-kit learn.
-
Course by
-
Self Paced
-
14 ساعات
-
الإنجليزية
تحليل البيانات ب R: التنبؤ بتحليل الانحدار
خلال هل مشروع راح تكون قادر تعمل predictive data analysis with regression يعني إستخدام البيانات للتحليل والتنبؤ من خلال طريقة الانحدار الخطّي ب-R Programming language.
بأستعمال و تعلّم كيف تنفّذ هيك مشروع بR رح تكون عم تستعمل أهم programing language لتحليل البيانات، و عم تبني model تعتبر الحجر الأساس بال machine learning و تستخدم طريقة الـ predictive regression المستعملة بأغلب مجالات الاقتصاد. هيدا المشروع مخصص للprogramers و الdata analysts الي عندها خبرة متواضعة بR و بال Machine Learning و عبالها تتعمّق أكثر و تكتشف كيف بينعمل التحليل التنبؤي بالانحدار.
-
Course by
-
Self Paced
-
2 ساعات
-
عربي
Basic Statistics in Python (ANOVA)
In this 1-hour long project-based course, you will learn how to set up a Google Colab notebook, source data from the internet, load data into Python, merge two datasets, clean data, perform exploratory data analysis, carry out ANOVA and create boxplots.
-
Course by
-
Self Paced
-
4 ساعات
-
الإنجليزية
Gestión del análisis de datos
This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results. This is a focused course designed to rapidly get you up to speed on the process of data analysis and how it can be managed.
-
Course by
-
Self Paced
-
الإسبانية
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.
-
Course by
-
Self Paced
-
21 ساعات
-
الإنجليزية
Image Compression with K-Means Clustering
In this project, you will apply the k-means clustering unsupervised learning algorithm using scikit-learn and Python to build an image compression application with interactive controls.
-
Course by
-
Self Paced
-
3 ساعات
-
الإنجليزية
Exploratory Data Analysis in AWS
Exploratory Data Analysis in AWS is the second course in the AWS Certified Machine Learning Specialty specialization. The main focus of this course is to analyze Data Streams and Data Analytics services in AWS along with exploring Data Analysis in AWS. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 2:00-2:30 Hours Video lectures that provide both Theory and Hands -On knowledge. Also, Graded and Ungraded Quiz are provided with every module in order to test the ability of learners.
-
Course by
-
5 ساعات
-
الإنجليزية
Fundamentals of Machine Learning for Supply Chain
This course will teach you how to leverage the power of Python to understand complicated supply chain datasets. Even if you are not familiar with supply chain fundamentals, the rich data sets that we will use as a canvas will help orient you with several Pythonic tools and best practices for exploratory data analysis (EDA). As such, though all datasets are geared towards supply chain minded professionals, the lessons are easily generalizable to other use cases.
-
Course by
-
Self Paced
-
13 ساعات
-
الإنجليزية
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
-
Course by
-
Self Paced
-
2 ساعات
-
الإنجليزية
Analyze Data in a Model Car Database with MySQL Workbench
In this project, you’ll perform exploratory data analysis for Mint Classics Company, a retailer of model cars. The company is looking to close one of its storage facilities. Your objective is to recommend inventory reduction strategies that won’t negatively impact customer service. Using MySQL Workbench, you’ll familiarize yourself with the sample database, run SQL queries to identify factors affecting storage space and propose inventory reduction approaches.
-
Course by
-
Self Paced
-
3 ساعات
-
الإنجليزية
Analyze Datasets and Train ML Models using AutoML
In the first course of the Practical Data Science Specialization, you will learn foundational concepts for exploratory data analysis (EDA), automated machine learning (AutoML), and text classification algorithms. With Amazon SageMaker Clarify and Amazon SageMaker Data Wrangler, you will analyze a dataset for statistical bias, transform the dataset into machine-readable features, and select the most important features to train a multi-class text classifier.
-
Course by
-
Self Paced
-
14 ساعات
-
الإنجليزية
Create Machine Learning Models in Microsoft Azure
Machine learning is the foundation for predictive modeling and artificial intelligence. If you want to learn about both the underlying concepts and how to get into building models with the most common machine learning tools this path is for you. In this course, you will learn the core principles of machine learning and how to use common tools and frameworks to train, evaluate, and use machine learning models. This course is designed to prepare you for roles that include planning and creating a suitable working environment for data science workloads on Azure.
-
Course by
-
Self Paced
-
13 ساعات
-
الإنجليزية
Data Science with R - Capstone Project
In this capstone course, you will apply various data science skills and techniques that you have learned as part of the previous courses in the IBM Data Science with R Specialization or IBM Data Analytics with Excel and R Professional Certificate. For this project, you will assume the role of a Data Scientist who has recently joined an organization and be presented with a challenge that requires data collection, analysis, basic hypothesis testing, visualization, and modeling to be performed on real-world datasets.
-
Course by
-
Self Paced
-
26 ساعات
-
الإنجليزية
Vertex AI: Qwik Start
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will use BigQuery for data processing and exploratory data analysis, and the Vertex AI platform to train and deploy a custom TensorFlow Regressor model to predict customer lifetime value (CLV). The goal of the lab is to introduce to Vertex AI through a high value real world use case - predictive CLV. Starting with a local BigQuery and TensorFlow workflow, you will progress toward training and deploying your model in the cloud with Vertex AI.
-
Course by
-
Self Paced
-
2 ساعات
-
الإنجليزية
Analyze Box Office Data with Plotly and Python
Welcome to this project-based course on Analyzing Box Office Data with Plotly and Python. In this course, you will be working with the The Movie Database (TMDB) Box Office Prediction data set. The motion picture industry is raking in more revenue than ever with its expansive growth the world over. Can we build models to accurately predict movie revenue? Could the results from these models be used to further increase revenue? We try to answer these questions by way of exploratory data analysis (EDA) and feature engineering. We will primarily use Plotly for data visualization.
-
Course by
-
Self Paced
-
3 ساعات
-
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
Predict Sales Revenue with scikit-learn
In this 2-hour long project-based course, you will build and evaluate a simple linear regression model using Python. You will employ the scikit-learn module for calculating the linear regression, while using pandas for data management, and seaborn for plotting. You will be working with the very popular Advertising data set to predict sales revenue based on advertising spending through mediums such as TV, radio, and newspaper.
By the end of this course, you will be able to:
- Explain the core ideas of linear regression to technical and non-technical audiences
-
Course by
-
Self Paced
-
3 ساعات
-
الإنجليزية
Exploratory Data Analysis for the Public Sector with ggplot
Learn about the core pillars of the public sector and the core functions of public administration through statistical Exploratory Data Analysis (EDA). Learn analytical and technical skills using the R programming language to explore, visualize, and present data, with a focus on equity and the administrative functions of planning and reporting. Technical skills in this course will focus on the ggplot2 library of the tidyverse, and include developing bar, line, and scatter charts, generating trend lines, and understanding histograms, kernel density estimations, violin plots, and ridgeplots.
-
Course by
-
Self Paced
-
18 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
Go Beyond the Numbers: Translate Data into Insights
This is the third of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll learn how to find the story within data and tell that story in a compelling way. You'll discover how data professionals use storytelling to better understand their data and communicate key insights to teammates and stakeholders. You'll also practice exploratory data analysis and learn how to create effective data visualizations.
-
Course by
-
Self Paced
-
33 ساعات
-
الإنجليزية
Probability & Statistics for Machine Learning & Data Science
Newly updated for 2024! Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply math concepts through programming. And so, in this specialization, you’ll apply the math concepts you learn using Python programming in hands-on lab exercises.
-
Course by
-
Self Paced
-
29 ساعات
-
الإنجليزية