Machine Learning: Instructional Materials


Foreseeing the future of EdTech

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In this article, I want to talk about artificial intelligence (AI) and how it is transforming the training and education sector. Before we get to that part, let's quickly take a look at how formal and informal education has evolved side by side throughout history. Formalized education has existed for thousands of years. Greek philosophers used to deliver lectures and teach their students long before the time of the Romans. It goes back hundreds of years before the Julian calendar was even introduced.


A Beginner's Guide to EDA with Linear Regression -- Part 3

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Mother Race -- but what we are seeing at X-Axis here is a bunch of variables. When you look closer you would notice that each variable seems to be representing each unique value of Mother Race variable. Linear Regression function'lm' in R automatically transforms a categorical variable into something called'dummy' variables. It will create a column for each categorical value (e.g. Japanese) and have a value of 0 or 1 based on whether a given row matches a given column (e.g.


A Beginner's Guide to EDA with Linear Regression -- Part 2

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So far, we have investigated if Father Age and Mother Age were impacting Gestation Week, and we know that both Father Age and Mother Age influence the changes in Gestation Week. But since we have done the investigation separately, one for Father Age's influence on Gestation Week and another for Mother's Age's influence on Gestation Week, we still don't know which of Father Age and Mother Age is the direct cause of the influence. In this post, I'm going to investigate further to find this out. So far, we know that the increases in Father Age would make Gestation Week shorter. And, the increases in Mother Age would also make Gestation Week shorter.


Art and Science of Machine Learning Coursera

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About this course: Welcome to the art and science of machine learning. In this course you will learn the essential skills of ML intuition, good judgment and experimentation to finely tune and optimize your ML models for the best performance. In this course you will learn the many knobs and levers involved in training a model. You will first manually adjust them to see their effects on model performance. Once familiar with the knobs and levers, otherwise known as hyperparameters, you will learn how to tune them in an automatic way using Cloud Machine Learning Engine on Google Cloud Platform.


A Beginner's Guide to Exploratory Data Analysis with Linear Regression -- Part 1

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Linear Regression is an algorithm that helps us predict unknown numeric outcome in future. It is usually the first Machine Learning (or Statistical) algorithms to learn when you are stepping into the world of Data Science or Machine Learning. Though it is one of the'old school' Statistical algorithms, it is still the most often used algorithm among many data scientists even today thanks to its simplicity and explainability. We at Exploratory always focus on, as the name suggests, making Exploratory Data Analysis (EDA) easier. EDA is a practice of iteratively asking a series of questions about data and trying to gain useful insights out of the data to answer the questions and essentially to influence our decision making.


Deep Learning: Convolutional Neural Networks in Python

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This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU. This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.


Google and Coursera launch a new machine learning specialization

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Over the last few years, Google and Coursera have regularly teamed up to launch a number of online courses for developers and IT pros. Among those was the Machine Learning Crash course, which provides developers with an introduction to machine learning. Now, building on that, the two companies are launching a machine learning specialization on Coursera. This new specialization, which consists of five courses, has an even more practical focus. The new specialization, called "Machine Learning with TensorFlow on Google Cloud Platform," has students build real-world machine learning models.


Deep Learning Prerequisites: Logistic Regression in Python

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This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.


New Product Forecasting Using Machine Learning Udemy

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All businesses introduce new products for various reasons. The new products poses challenge for the planners and marketing executives to estimate the demand for them for merchandise and supply planning purposes. The primary reason being the lack of historical data that can be used for forecasting. These techniques are'By Analogy' and'Bass Diffusion' including a live demonstration using a planning software. While Analogy is the more popular technique, the issue most planners face in this technique is in choosing the right analogue product.


Machine Learning Crash Course From Google

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We've been talking a lot about machine learning lately. People are using it for speech generation and recognition, computer vision, and even classifying radio signals. If you've yet to climb the learning curve, you might be interested in a new free class from Google using TensorFlow. Of course, we've covered tutorials for TensorFlow before, but this is structured as a 15 hour class with 25 lessons and 40 exercises. Of course, it is also from the horse's mouth, so to speak.