Machine Learning
The use of machine learning techniques has reached into industries as diverse as self-driving cars and financial forecasting. In fact, it is very likely that you already make use of machine learning on a daily basis. For example if you have used Google Maps for suggesting a traffic route, used Amazon to make an online purchase or communicated with your friends online via Facebook you have already interacted with machine learning. Below is few of the many projects I've completed during my Bootcamp with HyperionDev
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Recurrent Neural Networks
This Article is about a basic form of Natural Language Processing (NLP) called Sentiment Analysis . For this task we are going to use a recurrent neural network in Keras to try and classify a movie review as either positive or negative.
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Linear Regression
Regression analysis is a statistical process used to estimate the relationships between the dependent variable and one or more independent variables. Regression analysis is mostly used for prediction and forecasting which overlaps with machine learning. In this task we will experiment some linear regression use case.
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Neural Network
In this notebook we will use a neural network to predict diabetes using the Pima Diabetes Dataset. We will start by training a Random Forest to get a performance baseline. Then we will use the Keras package to quickly build and train a neural network and compare the performance. We will see how different network structures affect the performance, training time, and level of overfitting (or underfitting).
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CLASSIFICATION MODELING
In this article we're going to do some experiment with Classifiers like Bagged, Random Forest and Boosted tree on the titanic dataset... and we're going to add some bonus on other form of classification...
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Convolutional Neural Network
In this Notebook we will cover the basics of convolutional neural networks, or "ConvNets". ConvNets were invented in the late 1980s/early 1990s, and have had tremendous success especially with vision although they have also been used very successfully in speech processing pipelines, and more recently, for machine translation.
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