Mammoth Interactive, John Bura – Advanced Machine Learning & Data Analysis Projects Bootcamp

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.File Size: 14.67 GB

Mammoth Interactive, John Bura – 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.

Explore the world of data science with a variety of examples, including the CIFAR 100 Image Dataset, Xcode Development for Apple, Swift coding and CoreML, image recognition and structuring data using pandas.

This Mammoth A #1 Kickstarter project funded an interactive course

Android Studio, Java and app development with Pycharm, Python Coding, Tensforflow, and other courses. Mammoth Interactive.

Machine learning is used to build complex projects, including those that use the MNIST database and neuron functions. Create a text summariser and learn object recognition, object location and Tensorboard.

Machine learning is a machine’s ability to make decisions or predictions based on previous exposure to data and extensive training. This means that a program, app, or machine can improve its prediction accuracy by training. If a machine (program, app, etc.) improves its prediction accuracy by training it then it has “learned”.

Learn how models work

Computational graphs are made up of many connected nodes (often called neurons). Each of these nodes has a weight or bias that determines, given an input, which path will be most likely.

Building a machine-learning program requires four components: data collection and formatting, modeling, training, testing, and evaluation

Data Formatting and gathering

It will be easy to collect lots of data that can be used to create a model.

All data should be formatted exactly the same (images the same size, same colour scheme, etc.). Labels should also be used. You can also divide the data into testing and training sets that are mutually exclusive.

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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.

Evaluating, Training, and Testing

The inputs to a run can be used by the model to determine the paths the model will take through the neural network.

We show the model the correct outputs for a set of inputs. The model then alters the weights or biases of the neurons to minimize any difference between the output and the correct answer.

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Who is this course for?
Topics are intermediate math. Therefore, familiarity with university is important.-Level math is extremely helpful