Machine Learning: Instructional Materials


Discrete Probability Distributions for Machine Learning

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The probability for a discrete random variable can be summarized with a discrete probability distribution. Discrete probability distributions are used in machine learning, most notably in the modeling of binary and multi-class classification problems, but also in evaluating the performance for binary classification models, such as the calculation of confidence intervals, and in the modeling of the distribution of words in text for natural language processing. Knowledge of discrete probability distributions is also required in the choice of activation functions in the output layer of deep learning neural networks for classification tasks and selecting an appropriate loss function. Discrete probability distributions play an important role in applied machine learning and there are a few distributions that a practitioner must know about. In this tutorial, you will discover discrete probability distributions used in machine learning.


Discrete Probability Distributions for Machine Learning

#artificialintelligence

The probability for a discrete random variable can be summarized with a discrete probability distribution. Discrete probability distributions are used in machine learning, most notably in the modeling of binary and multi-class classification problems, but also in evaluating the performance for binary classification models, such as the calculation of confidence intervals, and in the modeling of the distribution of words in text for natural language processing. Knowledge of discrete probability distributions is also required in the choice of activation functions in the output layer of deep learning neural networks for classification tasks and selecting an appropriate loss function. Discrete probability distributions play an important role in applied machine learning and there are a few distributions that a practitioner must know about. In this tutorial, you will discover discrete probability distributions used in machine learning.


16 Best Deep Learning Tutorial for Beginners 2019 Digital Learning Land

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Do you want to add deep learning as your skill? We are with the best Deep Learning Tutorials for Beginners and Advanced, course, and certification. We are leaving in the era of machines. It is replacing the traditional ways of working. From a simple alarm clock to artificial intelligence, people are using machines in every sector of life. With the growth of using machines, the need to control and understand machines have grown. So, the skill of machine learning is in super demand. Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. The internet can offer you an uncountable amount of courses on deep learning. We have searched and found the few best Deep Learning tutorial for beginners and advanced level. Here, are the best Deep Learning certification and training for you. Coursera is offering this special course for those who want to master Deep Learning and start a career in machine learning. This 100% online course will take 3 months to complete.


rasbt/stat479-machine-learning-fs19

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Below is a list of the topics I am planning to cover. Note that while these topics are numerated by lectures, note that some lectures are longer or shorter than others. Also, we may skip over certain topics in favor of others if time is a concern. While this section provides an overview of potential topics to be covered, the actual topics will be listed in the course calendar.


Insights from the Field: Navigating the adaptive learning courseware products

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Adaptive learning is an emerging technology that has been shown to increase student engagement and student learning. Adaptive learning systems are automated systems that use machine learning to provide questions to assess student knowledge, give immediate feedback on responses, and provide scaffolding to support learning. The Online Learning Consortium (OLC) is reaching out to our global community of thought leaders, faculty, innovators, and practitioners to bring you insights from the field of online, blended, and digital learning. This week, Dr. Deborah Taylor, OLC Institute SME and faculty for the Adaptive Learning Fundamentals and Courseware Exploration workshop, joins us to answer our questions about this new workshop. OLC: There are many opportunities to teach online.


On Education Decision Trees, Random Forests, AdaBoost & XGBoost in Python - all courses

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Get a solid understanding of decision tree Understand the business scenarios where decision tree is applicable Tune a machine learning model's hyperparameters and evaluate its performance. Use Pandas DataFrames to manipulate data and make statistical computations. Use decision trees to make predictions Learn the advantage and disadvantages of the different algorithms Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same You're looking for a complete Decision tree course that teaches you everything you need to create a Decision tree/ Random Forest/ XGBoost model in Python, right? You've found the right Decision Trees and tree based advanced techniques course! After completing this course you will be able to: Identify the business problem which can be solved using Decision tree/ Random Forest/ XGBoost of Machine Learning.


A Gentle Introduction to PyTorch 1.2

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In our previous PyTorch notebook, we learned about how to get started quickly with PyTorch 1.2 using Google Colab. In this tutorial, we are going to take a step back and review some of the basic components of building a neural network model using PyTorch. As an example, we will build an image classifier using a few stacked layers and then evaluate the model. This will be a brief tutorial and will avoid using jargon and over-complicated code. That said, this is perhaps the most basic of neural network models you can build with PyTorch.


Data Science and Machine Learning Bootcamp with R

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Udemy Free Discount - Data Science and Machine Learning Bootcamp with R, Learn how to use the R programming language for data science and machine learning and data visualization! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science! This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost!


On Education Machine Learning: Support Vector Machines in R (SVM in R) - all courses

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You're looking for a complete Support Vector Machines course that teaches you everything you need to create a SVM model in R, right? You've found the right Support Vector Machines techniques course! How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course. If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Support Vector Machines.


On Education Tensorflow 2.0: Deep Learning and Artificial Intelligence - all courses

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It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version. Tensorflow is Google's library for deep learning and artificial intelligence. Deep Learning has been responsible for some amazing achievements recently, such as: Generating beautiful, photo-realistic images of people and things that never existed (GANs) Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning) Self-driving cars (Computer Vision) Speech recognition (e.g. Siri) and machine translation (Natural Language Processing) Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning) Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.