Advanced Machine Learning & Data Analysis Projects Bootcamp – John Bura

Description

Unlock exclusive learning opportunities with the Advanced Machine Learning & Data Analysis Projects Bootcamp – John Bura course at esys[GB]. Explore expert insights, advanced techniques, and practical applications from world-renowned instructors in your chosen field. Empower your growth and career with our curated collection of over 70,000 courses from top authors such as John Overdurf, Conor Harris, Tony Robbins, Dr. Joe Dispenza, and more.

What you'll learn

• Code in 3 programming languages: Java, Python and Swift
• Build nodes and data models for linear regression
• Use summarizing mechanisms to handle text data
• Test projects on mobile devices
• Examine computational graphs
• Analyze scalars and histograms
• Build neuron functions
• Load, convert, and display image and digit data
• Describe data with statistics
• And much more…

Requirements

• PyCharm

Description

"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.

Enroll Now to Learn with Mammoth Interactive

Who this course is for:

• Topics involve intermediate math, so familiarity with university-level math is very helpful

Course content
30 sections • 132 lectures • 20h 34m total length

• Introduction to Machine Learning + Software
• Android Studio
• Java
• App Development
• Machine Learning Concepts
• Pycharm v Introduction
• Python Language Basics
• Tensorflow
• Building Apps With Machine Learning
• Text Summarizer
• Object Localization
• Object Recognition
• Introduction to Tensorboard
• Advanced Machine Learning Concepts
• Advanced MNIST
• Advanced Cl FAR 100
• Introduction to iOS
• Introduction to Xcode
• Coding Crash Course – Swift Programming Language
• iOS App Development
• Introduction to CoreML
• iOS Image Recognition
• iOS Tensorflow Model Importing
• Starting Projects in Pandas
• Working With Data Structures
• Manipulating DataFrames
• Reading and Writing Data
• Analytical Approaches to Data
• Test Your Knowledge: Pandas (NEW)

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