Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
Advanced Learning Algorithms
In the second course of the Machine Learning Specialization, you will: • Build and train a neural network with TensorFlow to perform multi-class classification • Apply best practices for machine learning development so that your models generalize to data and tasks in the real world • Build and use decision trees and tree ensemble methods, including random forests and boosted trees 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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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
Machine Learning: Theory and Hands-on Practice with Python
In the Machine Learning specialization, we will cover Supervised Learning, Unsupervised Learning, and the basics of Deep Learning. You will apply ML algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Starting with supervised learning, we will cover linear and logistic regression, KNN, Decision trees, ensembling methods such as Random Forest and Boosting, and kernel methods such as SVM. Then we turn our attention to unsupervised methods, including dimensionality reduction techniques (e.g., PCA), clustering, and recommender systems.
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Course by
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Self Paced
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الإنجليزية
Perform Sentiment Analysis with scikit-learn
In this project-based course, you will learn the fundamentals of sentiment analysis, and build a logistic regression model to classify movie reviews as either positive or negative. We will use the popular IMDB data set. Our goal is to use a simple logistic regression estimator from scikit-learn for document classification.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Build a Machine Learning Web App with Streamlit and Python
Welcome to this hands-on project on building your first machine learning web app with the Streamlit library in Python. By the end of this project, you are going to be comfortable with using Python and Streamlit to build beautiful and interactive ML web apps with zero web development experience! We are going to load, explore, visualize and interact with data, and generate dashboards in less than 100 lines of Python code!
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Predict Ad Clicks Using Logistic Regression and XG-Boost
In this project, we will predict Ads clicks using logistic regression and XG-boost algorithms. In this project, we will assume that you have been hired as a consultant to a start-up that is running a targeted marketing ad campaign on Facebook. The company wants to analyze customer behavior by predicting which customer clicks on the advertisement.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
RStudio for Six Sigma - Hypothesis Testing
Welcome to RStudio for Six Sigma - Hypothesis Testing. This is a project-based course which should take approximately 2 hours to finish.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Logistic Regression 101: US Household Income Classification
In this hands-on project, we will train Logistic Regression and XG-Boost models to predict whether a particular person earns less than 50,000 US Dollars or more than 50,000 US Dollars annually. This data was obtained from U.S. Census database and consists of features like occupation, age, native country, capital gain, education, and work class.
By the end of this project, you will be able to:
- Understand the theory and intuition behind Logistic Regression and XG-Boost models
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Logistic Regression with Python and Numpy
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.
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Course by
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Self Paced
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4 ساعات
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الإنجليزية
Breast Cancer Prediction Using Machine Learning
In this 2 hours long project-based course, you will learn to build a Logistic regression model using Scikit-learn to classify breast cancer as either Malignant or Benign. We will use the Breast Cancer Wisconsin (Diagnostic) Data Set from Kaggle. Our goal is to use a simple logistic regression classifier for cancer classification. We will be carrying out the entire project on the Google Colab environment. You will need a free Gmail account to complete this project. Please be aware of the fact that the dataset and the model in this project, can not be used in real-life.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Titanic Survival Prediction Using Machine Learning
In this 1-hour long project-based course, we will predict titanic survivors’ using logistic regression and naïve bayes classifiers. The sinking of the Titanic is one of the key sad tragedies in history and it took place on April 15th, 1912. The numbers of survivors were low due to lack of lifeboats for all passengers. This practical guided project, we will analyze what sorts of people were likely to survive this tragedy with the power of machine learning. Note: This course works best for learners who are based in the North America region.
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Course by
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3 ساعات
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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.
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Course by
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3 ساعات
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الإنجليزية
Logistic Regression for Classification using Julia
This guided project is about book genre classification using logistic regression in Julia. It is ideal for beginners who do not know what logistic regression is because this project explains these concepts in simple terms.
While you are watching me code, you will get a cloud desktop with all the required software pre-installed. This will allow you to code along with me. After all, we learn best with active, hands-on learning.
Special features:
1) Simple explanations of important concepts.
2) Use of images to aid in explanation.
3) Use a real world dataset.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
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.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Diabetes Prediction With Pyspark MLLIB
In this 1 hour long project-based course, you will learn to build a logistic regression model using Pyspark MLLIB to classify patients as either diabetic or non-diabetic. We will use the popular Pima Indian Diabetes data set. Our goal is to use a simple logistic regression classifier from the pyspark Machine learning library for diabetes classification. 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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3 ساعات
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الإنجليزية
Evaluate Machine Learning Models with Yellowbrick
Welcome to this project-based course on Evaluating Machine Learning Models with Yellowbrick. In this course, we are going to use visualizations to steer our machine learning workflow. The problem we will tackle is to predict whether rooms in apartments are occupied or unoccupied based on passive sensor data such as temperature, humidity, light and CO2 levels. We will build a logistic regression model for binary classification. This is a continuation of the course on Room Occupancy Detection.
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Course by
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3 ساعات
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الإنجليزية
Machine Learning for Telecom Customers Churn Prediction
In this hands-on project, we will train several classification algorithms such as Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Random Forest Classifier to predict the churn rate of Telecommunication Customers. Machine learning help companies analyze customer churn rate based on several factors such as services subscribed by customers, tenure rate, and payment method. Predicting churn rate is crucial for these companies because the cost of retaining an existing customer is far less than acquiring a new one.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Logistic Regression&application as Classification Algorithm
In this project, you will learn about Logistic Regression and its application as Classification Algorithm. The project demonstrates the theoretical background of Logistic Regression using the Sigmoidal function. It also explains the suitability of linear vs logistic regression to answer the specific types of research questions. Finally, it covers an implementation of classification algorithm using logit model. The project utilizes the 'Candy' dataset for illustrative purpose.
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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 Spark on Google Cloud Dataproc
This is a self-paced lab that takes place in the Google Cloud console. In this lab you will learn how to implement logistic regression using a machine learning library for Apache Spark running on a Google Cloud Dataproc cluster to develop a model for data from a multivariable dataset.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Combining and Analyzing Complex Data
In this course you will learn how to use survey weights to estimate descriptive statistics, like means and totals, and more complicated quantities like model parameters for linear and logistic regressions. Software capabilities will be covered with R® receiving particular emphasis. The course will also cover the basics of record linkage and statistical matching—both of which are becoming more important as ways of combining data from different sources. Combining of datasets raises ethical issues which the course reviews.
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Course by
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Self Paced
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10 ساعات
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الإنجليزية
Introduction to Statistical Analysis: Hypothesis Testing
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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10 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
Logistic Regression in R for Public Health
Welcome to Logistic Regression in R for Public Health! Why logistic regression for public health rather than just logistic regression? Well, there are some particular considerations for every data set, and public health data sets have particular features that need special attention. In a word, they're messy. Like the others in the series, this is a hands-on course, giving you plenty of practice with R on real-life, messy data, with predicting who has diabetes from a set of patient characteristics as the worked example for this course.
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Course by
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Self Paced
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12 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
Introduction to Machine Learning in Sports Analytics
In this course students will explore supervised machine learning techniques using the python scikit learn (sklearn) toolkit and real-world athletic data to understand both machine learning algorithms and how to predict athletic outcomes.
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Course by
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Self Paced
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13 ساعات
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الإنجليزية