Ensuring fairness is essential for every education system. Machine learning is increasingly supporting the education system and educational data science (EDS) domain, from decision support to educational activities and learning analytics. However, the machine learning-based decisions can be biased because the algorithms may generate the results based on students' protected attributes such as race or gender. Clustering is an important machine learning technique to explore student data in order to support the decision-maker, as well as support educational activities, such as group assignments. Therefore, ensuring high-quality clustering models along with satisfying fairness constraints are important requirements. This chapter comprehensively surveys clustering models and their fairness in EDS. We especially focus on investigating the fair clustering models applied in educational activities. It is believed that these models are practical tools for analyzing students' data and ensuring fairness in EDS.

It was a great Experience with ONLEI Technologies Noida . I took admission in Machine Learning using Python Course and The Faculty taught very well and he has great knowledge . I also took their Online Classes my experience was good, they provide daily notes and Study Material after daily class . According to me ONLEI Technologies is the best Online Training Company and Best Institute for Python Training in Noida and Machine Learning using Python and Data Science Training in Delhi/NCR . I got my Summer Training Certificate and Internship Letter and Project Letter after the project work .

This Python Course is for beginners. In this course, you will learn the Python basics through video lectures, quizzes, review exercises, and programming challenges. You will also understand computer science concepts such as flow control and functions. And you will also use Pycharm to write their Python programs.

This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes. By the end of this course you should be able to: -Differentiate uses and applications of classification and classification ensembles -Describe and use logistic regression models -Describe and use decision tree and tree-ensemble models -Describe and use other ensemble methods for classification -Use a variety of error metrics to compare and select the classification model that best suits your data -Use oversampling and undersampling as techniques to handle unbalanced classes in a data set Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting.

Consider a learner that wants to predict the next day's temperature range at a given location based on inputs such as the current day's temperature range, humidity, atmospheric pressure, precipitation, wind speed, solar radiation, location, and time of year. In our model, this learner is tested daily. On a given day, the learner gets inputs for that day, which it uses to output a prediction for the next day's temperature range; when the next day arrives, it sees the correct temperature range, then uses this feedback to update future predictions. As this is repeated, the learner accumulates information to help it make better predictions. A natural question arises: can the learner guarantee that its predictions become better over time, and if so, how quickly? We investigate a model of online learning of real-valued functions previously studied in [9, 12, 13, 1, 10, 11] where an algorithm A learns a real-valued function f from some class F in trials. Past research on this model focused on functions of one input, for example, predicting the temperature range solely based on the time of year. The research showed that, as long as the function is sufficiently smooth, the learner can become a good predictor fairly rapidly. Suppose that F consists of functions f: S R for some set S, and fix some f F. In each trial t = 0,...,m, A receives an input s

This paper presents a machine learning approach to multidimensional item response theory (MIRT), a class of latent factor models that can be used to model and predict student performance from observed assessment data. Inspired by collaborative filtering, we define a general class of models that includes many MIRT models. We discuss the use of penalized joint maximum likelihood (JML) to estimate individual models and cross-validation to select the best performing model. This model evaluation process can be optimized using batching techniques, such that even sparse large-scale data can be analyzed efficiently. We illustrate our approach with simulated and real data, including an example from a massive open online course (MOOC). The high-dimensional model fit to this large and sparse dataset does not lend itself well to traditional methods of factor interpretation. By analogy to recommender-system applications, we propose an alternative "validation" of the factor model, using auxiliary information about the popularity of items consulted during an open-book exam in the course.

AI is transforming the practice of medicine. It's helping doctors diagnose patients more accurately, make predictions about patients' future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine. Medical treatment may impact patients differently based on their existing health conditions. In this third course, you'll recommend treatments more suited to individual patients using data from randomized control trials.

Are you looking for Best Free Coursera Courses in 2023? You can earn a Coursera Certificate with Coursera free courses by applying for a Coursera scholarship and by doing Coursera paid courses. You are going to get a 7-day free trial on Coursera when you join and start your very first subscription to do Coursera Specializations for free. If you do not cancel your free trial you will be automatically transferred to paid subscription on the 8th Day. You can continue your Coursera Classes either by using Coursera App on mobile or any other device. This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Learn and launch your career in Data Science with these best Coursera courses. A nine-course introduction to data science developed and taught by leading instructors. Develop programs to gather, clean, analyze, and visualize data. You will get new insights into your data. Learn to apply data science methods and techniques, and acquire analytical skills.

In these tough times one can learn new skills to get themselves better. How to apply for financial aid for any course? Now you will see 2 main questions and couple of small ones. This part will give you a rough idea of the answers, that I gave. Ans: I am currently an active student from India, so I am investing all my resources, from money (mostly sponsored by my parents) to all of my energy into my study.

Python is one of the most widely used programming languages in the Machine Learning field. Python has many packages and libraries that are specifically tailored for certain functions, including pandas, NumPy, scikit-learn, Matplotlib, and SciPy. So if you want to learn Machine Learning with Python, this article is for you. In this article, you will find the 12 Best Online Courses for Machine Learning with Python. Now, without wasting your time, let's start finding the Best Online Courses for Machine Learning with Python.