Advanced Machine Learning & Data Analysis Projects Bootcamp

"Excellent! Thank you for all your hard work." - Mammoth Interactive student Inderpal

"Great! Well explained and the instructor provides clear examples" - Mark T.

Dive into a world of data science and analysis with a wide range of examples including the CIFAR 100 image dataset, Xcode development for Apple, Swift coding, CoreML, image recognition, and structuring data with pandas.

This Mammoth Interactive course was funded by a #1 project on Kickstarter

Learn Android Studio, Java, app development, Pycharm, Python coding, Tensforflow and more with Mammoth Interactive.

Build advanced projects using machine learning including advanced the MNIST database with neuron functions. Build a text summarizer and learn object localization, object recognition and Tensorboard.

Machine learning is a machine’s ability to make decisions or predictions based on previous exposure to data and extensive training. In other words, if a machine (program, app, etc.) improves its prediction accuracy through training then it has “learned”.

Learn How Models Work

Computational graphs consist of a network of connected nodes (often called neurons). Each of these nodes typically has a weight and a bias that helps determine, given an input, which path is the most likely. 

There are 4 main components to building a machine learning program: data gathering and formatting, model building, training, and testing and evaluating

Data Gathering and Formatting

You will learn to gather plenty of data for the model to learn from.

All data should be formatted pretty much the same (images same size, same color scheme, etc.) and should be labelled. Also divide data into mutually exclusive training and testing sets.

Model Building

You will learn to figure out which kind of model scheme works best and what kinds of algorithms work best for the problem you’re trying to solve.

Training, Testing and Evaluating

The model can choose paths through the neural network or computational graph based upon the inputs for a particular run, as well as the weights and biases of neurons in the network. 

In supervised learning, we show the model what the correct outputs are for a given set of inputs and the model alters the weights and biases of neurons to minimize the difference between its output and the correct answer.

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