Goto

Collaborating Authors

Results


Theory of Gaussian Process Regression for Machine Learning

#artificialintelligence

Probabilistic modelling, which falls under the Bayesian paradigm, is gaining popularity world-wide. Its powerful capabilities, such as giving a reliable estimation of its own uncertainty, makes Gaussian process regression a must-have skill for any data scientist. Gaussian process regression is especially powerful when applied in the fields of data science, financial analysis, engineering and geostatistics. This course covers the fundamental mathematical concepts needed by the modern data scientist to confidently apply Gaussian process regression. The course also covers the implementation of Gaussian process regression in Python.


Applied Machine Learning in Python

#artificialintelligence

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models.


Predictive Modeling and Machine Learning with MATLAB

#artificialintelligence

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. To be successful in this course, you should have some background in basic statistics (histograms, averages, standard deviation, curve fitting, interpolation) and have completed courses 1 through 2 of this specialization. By the end of this course, you will use MATLAB to identify the best machine learning model for obtaining answers from your data. You will prepare your data, train a predictive model, evaluate and improve your model, and understand how to get the most out of your models.


Applied Data Science with Python Coursera

#artificialintelligence

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models.


Spark Machine Learning Project (House Sale Price Prediction)

#artificialintelligence

Get your team access to 3,500 top Udemy courses anytime, anywhere. In this Data science Machine Learning project, we will predict the sales prices in the Housing data set using LinearRegression one of the predictive models. Databricks lets you start writing Spark ML code instantly so you can focus on your data problems.


How Widely Can Prediction Models be Generalized? An Analysis of Performance Prediction in Blended Courses

arXiv.org Machine Learning

Blended courses that mix in-person instruction with online platforms are increasingly popular in secondary education. These tools record a rich amount of data on students' study habits and social interactions. Prior research has shown that these metrics are correlated with students' performance in face to face classes. However, predictive models for blended courses are still limited and have not yet succeeded at early prediction or cross-class predictions even for repeated offerings of the same course. In this work, we use data from two offerings of two different undergraduate courses to train and evaluate predictive models on student performance based upon persistent student characteristics including study habits and social interactions. We analyze the performance of these models on the same offering, on different offerings of the same course, and across courses to see how well they generalize. We also evaluate the models on different segments of the courses to determine how early reliable predictions can be made. This work tells us in part how much data is required to make robust predictions and how cross-class data may be used, or not, to boost model performance. The results of this study will help us better understand how similar the study habits, social activities, and the teamwork styles are across semesters for students in each performance category. These trained models also provide an avenue to improve our existing support platforms to better support struggling students early in the semester with the goal of providing timely intervention.