Advanced Machine Learning & Data Analysis Projects Bootcamp

Advanced Machine Learning & Data Analysis Projects Bootcamp, available at $64.99, has an average rating of 4.45, with 132 lectures, 1 quizzes, based on 245 reviews, and has 24391 subscribers.

You will learn about 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… This course is ideal for individuals who are Topics involve intermediate math, so familiarity with university-level math is very helpful It is particularly useful for Topics involve intermediate math, so familiarity with university-level math is very helpful.

Enroll now: Advanced Machine Learning & Data Analysis Projects Bootcamp

Summary

Title: Advanced Machine Learning & Data Analysis Projects Bootcamp

Price: $64.99

Average Rating: 4.45

Number of Lectures: 132

Number of Quizzes: 1

Number of Published Lectures: 132

Number of Published Quizzes: 1

Number of Curriculum Items: 133

Number of Published Curriculum Objects: 133

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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…

Who Should Attend

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

Target Audiences

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

“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

Course Curriculum

Chapter 1: Introduction to Machine Learning + Software

Lecture 1: Projects Overview

Lecture 2: Project Resources – Mammoth Interactive

Chapter 2: Android Studio

Lecture 1: Downloading and Installing Android Studio

Lecture 2: Exploring Interface

Lecture 3: Setting up an Emulator and Running Project

Lecture 4: Code

Chapter 3: Java

Lecture 1: Intro to Language Basics

Lecture 2: Variable Types

Lecture 3: Operations on Variables

Lecture 4: Array and Lists

Lecture 5: Array and List Operations

Lecture 6: If and Switch Statements

Lecture 7: While Loops

Lecture 8: For Loops

Lecture 9: Functions Intro

Lecture 10: Parameters and Return Values

Lecture 11: Classes and Objects Intro

Lecture 12: Superclass and Subclasses

Lecture 13: Static Variables and Axis Modifiers

Chapter 4: App Development

Lecture 1: Intro To Android App Development

Lecture 2: Building Basic UI

Lecture 3: Connecting UI to Backend

Lecture 4: Implementing Backend and Tidying UI

Chapter 5: Machine Learning Concepts

Lecture 1: Introduction to Machine Learning

Lecture 2: Pycharm Files

Chapter 6: Pycharm

Lecture 1: Project Overview

Lecture 2: Pycharm Source Files – Mammoth Interactive

Chapter 7: Introduction

Lecture 1: Downloading and Installing Pycharm and Python

Lecture 2: Exploring Pycharm

Chapter 8: Python Language Basics

Lecture 1: Introduction to Variables

Lecture 2: Variables Operations and Conversions

Lecture 3: Collection Types

Lecture 4: Collections Operations

Lecture 5: Control Flow If Statements

Lecture 6: While and For Loops

Lecture 7: Functions

Lecture 8: Classes and Objects

Chapter 9: Tensorflow

Lecture 1: Project Demo

Lecture 2: Topics List

Lecture 3: Importing Tensorflow to Pycharm

Lecture 4: Constant Nodes and Sessions

Lecture 5: Variable Nodes

Lecture 6: Placeholder Nodes

Lecture 7: Operation nodes

Lecture 8: Loss, Optimizers, and Training

Lecture 9: Building a Linear Regression Model

Lecture 10: Tensorflow Project Files – Mammoth Interactive

Chapter 10: Building Apps With Machine Learning

Lecture 1: Introduction – Improving Model Efficiency

Lecture 2: Project Code – Mammoth Interactive

Lecture 3: Introduction to Tensorflow Lite

Chapter 11: Text Summarizer

Lecture 1: Introduction

Lecture 2: Exploring How Model Is Built

Lecture 3: Exploring Training and Summarizing Mechanisms

Lecture 4: Exploring Training and Summarizing Code

Lecture 5: Testing the Model

Lecture 6: Text Summarizer Pycharm

Chapter 12: Object Localization

Lecture 1: Introductions

Lecture 2: Examining Project Code

Lecture 3: Testing with a Mobile Device

Chapter 13: Object Recognition

Lecture 1: Introduction

Lecture 2: Examining Code

Lecture 3: Testing on Mobile Device

Chapter 14: Introduction to Tensorboard

Lecture 1: Introduction to Upcoming Projects

Lecture 2: Examining Computational Graph In Tensorboard

Lecture 3: Analyzing Scalars and Histograms

Lecture 4: Modifying Model Parameters Across Multiple Runs

Lecture 5: Project Code – Mammoth Interactive

Chapter 15: Advanced Machine Learning Concepts

Lecture 1: Introduction to Upcoming Projects

Lecture 2: Project Code – Mammoth Interactive

Chapter 16: Advanced MNIST

Lecture 1: Project Demonstration

Lecture 2: Project Overview

Lecture 3: Building Neuron Functions

Lecture 4: Building the Convolutional Layers

Lecture 5: Building Dense, Dropout, and Readout Layers

Lecture 6: Writing Loss and Optimizer Functions and Training and Testing

Lecture 7: Optimizing Saved Graph

Lecture 8: Setting up Android Project

Lecture 9: Setting Up UI

Lecture 10: Load and Display Digit Images

Lecture 11: Formatting Model Input

Lecture 12: Displaying Results and Summary

Lecture 13: Project Code – Mammoth Interactive

Chapter 17: Advanced CIFAR 100