Advanced Machine Learning & Data Analysis Projects Bootcamp
4.7 (217 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
24,007 students enrolled

Advanced Machine Learning & Data Analysis Projects Bootcamp

Build projects like a text summarizer! Learn object localization, image recognition and structuring data with pandas
Highest Rated
4.7 (217 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
24,007 students enrolled
Last updated 2/2020
English
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Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 20.5 hours on-demand video
  • 17 articles
  • 14 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Expand all 132 lectures 20:34:03
+ Java
13 lectures 02:02:42
Intro to Language Basics
02:46
Variable Types
14:00
Operations on Variables
10:49
Array and Lists
09:26
Array and List Operations
08:00
If and Switch Statements
11:34
While Loops
10:09
For Loops
08:51
Functions Intro
08:39
Parameters and Return Values
07:05
Classes and Objects Intro
12:14
Superclass and Subclasses
11:42
Static Variables and Axis Modifiers
07:27
+ App Development
4 lectures 29:33
Intro To Android App Development
01:57
Building Basic UI
12:15
Connecting UI to Backend
06:12
Implementing Backend and Tidying UI
09:09
+ Machine Learning Concepts
2 lectures 02:01
Introduction to Machine Learning
01:59
Pycharm Files
00:02
+ Pycharm
2 lectures 02:42
Project Overview
02:40
Pycharm Source Files - Mammoth Interactive
00:02
+ Introduction
2 lectures 14:43
Downloading and Installing Pycharm and Python
06:55
Exploring Pycharm
07:48
+ Python Language Basics
8 lectures 01:37:58
Introduction to Variables
13:17
Variables Operations and Conversions
12:35
Collection Types
12:47
Collections Operations
08:42
Control Flow If Statements
12:50
While and For Loops
10:44
Functions
11:23
Classes and Objects
15:40
+ Tensorflow
10 lectures 01:26:04
Project Demo
02:53
Topics List
06:09
Importing Tensorflow to Pycharm
04:25
Constant Nodes and Sessions
09:01
Variable Nodes
10:46
Placeholder Nodes
07:35
Operation nodes
12:47
Loss, Optimizers, and Training
11:56
Building a Linear Regression Model
20:30
Tensorflow Project Files - Mammoth Interactive
00:02
+ Building Apps With Machine Learning
3 lectures 16:08
Introduction - Improving Model Efficiency
05:47
Project Code - Mammoth Interactive
00:02
Introduction to Tensorflow Lite
10:19