Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
Build and deploy a stroke prediction model using R
In this project, you’ll help a leading healthcare organization build a model to predict the likelihood of a patient suffering a stroke. The model could help improve a patient’s outcomes.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
AI Workflow: Feature Engineering and Bias Detection
This is the third course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. Course 3 introduces you to the next stage of the workflow for our hypothetical media company. In this stage of work you will learn best practices for feature engineering, handling class imbalances and detecting bias in the data. Class imbalances can seriously affect the validity of your
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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 Rapid Prototyping with IBM Watson Studio
An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. This automation will provide rapid-prototyping of models and allow the Data Scientist to focus their efforts on applying domain knowledge to fine-tune models. This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studio’s AutoAI experiment tool, explaining the underlying technology at work as developed by IBM Research.
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Course by
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Self Paced
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9 ساعات
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الإنجليزية
Exploring and Analyzing Fifa's Datasets Using Python
In this 1-hour long project-based course, you will learn how to load a dataset into a pandas dataframe, you will learn how to tidy a messy dataset (Data Tidying), you will get to also visualize the dataset using Matplotlib and seaborn, you will learn how to engineer new features, you will also get to learn how to merge datasets (Data Integration)
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Data for Machine Learning
This course is all about data and how it is critical to the success of your applied machine learning model.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
MLOps Platforms: Amazon SageMaker and Azure ML
In MLOps (Machine Learning Operations) Platforms: Amazon SageMaker and Azure ML you will learn the necessary skills to build, train, and deploy machine learning solutions in a production environment using two leading cloud platforms: Amazon Web Services (AWS) and Microsoft Azure.
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Course by
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Self Paced
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13 ساعات
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الإنجليزية
Multiple Linear Regression with scikit-learn
In this 2-hour long project-based course, you will build and evaluate multiple linear regression models using Python. You will use scikit-learn to calculate the regression, while using pandas for data management and seaborn for data visualization. The data for this project consists of the very popular Advertising dataset to predict sales revenue based on advertising spending through media such as TV, radio, and newspaper.
By the end of this project, you will be able to:
- Build univariate and multivariate linear regression models using scikit-learn
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Fraud Detection on Financial Transactions with Machine Learning on Google Cloud
This is a self-paced lab that takes place in the Google Cloud console. Explore financial transactions data for fraud analysis, apply feature engineering and machine learning techniques to detect fraudulent activities using BigQuery ML.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Machine Learning Data Lifecycle in Production
**Starting May 8, enrollment for the Machine Learning Engineering for Production Specialization will be closed.
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Course by
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Self Paced
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22 ساعات
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الإنجليزية
Build Random Forests in R with Azure ML Studio
In this project-based course you will learn to perform feature engineering and create custom R models on Azure ML Studio, all without writing a single line of code! You will build a Random Forests model in Azure ML Studio using the R programming language. The data to be used in this course is the Bike Sharing Dataset. The dataset contains the hourly and daily count of rental bikes between years 2011 and 2012 in Capital bikeshare system with the corresponding weather and seasonal information.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Automatic Machine Learning with H2O AutoML and Python
This is a hands-on, guided project on Automatic Machine Learning with H2O AutoML and Python. By the end of this project, you will be able to describe what AutoML is and apply automatic machine learning to a business analytics problem with the H2O AutoML interface in Python. H2O's AutoML automates the process of training and tuning a large selection of models, allowing the user to focus on other aspects of the data science and machine learning pipeline such as data pre-processing, feature engineering and model deployment.
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Course by
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Self Paced
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2 ساعات
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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.
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Course by
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Self Paced
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3 ساعات
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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.
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Course by
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Self Paced
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3 ساعات
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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.
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Course by
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Self Paced
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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.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Machine Learning on Google Cloud
What is machine learning, and what kinds of problems can it solve? How can you build, train, and deploy machine learning models at scale without writing a single line of code?
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Course by
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Self Paced
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الإنجليزية
Hyperparameter Tuning with Neural Network Intelligence
In this 2-hour long guided project, we will learn the basics of using Microsoft's Neural Network Intelligence (NNI) toolkit and will use it to run a Hyperparameter tuning experiment on a Neural Network. NNI is an open source, AutoML toolkit created by Microsoft which can help machine learning practitioners automate Feature engineering, Hyperparameter tuning, Neural Architecture search and Model compression. In this guided project, we are going to take a look at using NNI to perform hyperparameter tuning.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Predictive Modeling and Machine Learning with MATLAB
In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB and Data Processing and Feature Engineering with MATLAB to increase your ability to harness the power of MATLAB to analyze data relevant to the work you do. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background.
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Course by
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Self Paced
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22 ساعات
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الإنجليزية
Feature Engineering
This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
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Course by
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Self Paced
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8 ساعات
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الإنجليزية
Data Processing and Feature Engineering with MATLAB
In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB to lay the foundation required for predictive modeling. This intermediate-level course is useful to anyone who needs to combine data from multiple sources or times and has an interest in modeling. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background.
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Course by
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Self Paced
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20 ساعات
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الإنجليزية
Exploratory Data Analysis for Machine Learning
This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data.
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Course by
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Self Paced
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14 ساعات
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الإنجليزية