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$449
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Courses
8
Lessons
298
Enrolled
549
Courses
8
Lessons
298
Enrolled
549
What's Included
Tensorflow For Beginners
$50 Value
Data Visualization With Python: The Complete Guide
$50 Value
Mathematical Foundations For Machine Learning & AI
$50 Value
R Programming For Beginners
$50 Value
Introduction To Data Science Using R Programming
$50 Value
Machine Learning With R
$50 Value
Unreal Game Development For Beginners
$50 Value
Machine Learning For Absolute Beginners
$99 Value
Product Details
Access
Lifetime
Content
4 hours
Lessons
20
Tensorflow For Beginners
Start Your AI Deep Dive with a Detailed Look at this Machine Learning Staple
By Eduonix Technologies | in Online Courses
Artificial intelligence is the future, and it's being brought to us by TensorFlow. Used by the likes of Google, Snapchat, and Twitter, TensorFlow is an open-source software library that empowers users to add AI elements, like speech recognition and computer vision, into their applications. This combines TensorFlow theory with real-life applications. Jump in, and you'll come to grips with the TensorFlow basics. Then you'll explore machine learning, creating neural networks, and even building your own project from scratch.
- Access 20 lectures & 4 hours of content 24/7
- Broaden your understanding of the TensorFlow essentials
- Dive into the machine learning lifecycle, TensorBoard, logical regression & neural networks
- Explore building your own neural networks
- Validate your training by completing a deep learning project from scratch
Instructor
Eduonix creates and distributes high-quality technology training content. Their team of industry professionals has been training manpower for more than a decade. They aim to teach technology the way it is used in the industry and professional world. They have a professional team of trainers for technologies ranging from Mobility, Web and Enterprise, and Database and Server Administration.Important Details
- Length of time users can access this course: lifetime
- Access options: web and mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: intermediate
Requirements
- Internet required
Course Outline
- Introduction
- Introduction - 0:35
- Tensorflow Foundations
- Tensor flow Foundations Part 1 - 19:13
- Tensor flow Foundations Part 2 - 15:18
- ML lifecycle & TensorBoard
- The Machine Learning Lifecycle Part 1 - 15:13
- The Machine Learning Lifecycle Part 2 - 16:58
- The Machine Learning Lifecycle & Using TensorBoard
- Tensor Board - Part 1 - 13:40
- Tensor Board - Part 2 - 16:04
- Logistic Regression & NN Basics
- Part 1 - 16:28
- Part 2 - 16:50
- Single & Multiple Hidden Layer NNs
- Part 1 - 10:45
- Part 2 - 16:51
- Part 3 - 4:35
- Convolutional NNs
- CNN - Part 1 - 8:51
- CNN - Part 2 - 13:09
- CNN - Part 3 - 9:16
- Deep Learning
- Deep Learning - Part 1 - 16:49
- Deep Learning - Part 2 - 16:51
- Final Project
- Problem statement - 5:38
- Code Solution - 15:01
Access
Lifetime
Content
7 hours
Lessons
59
Data Visualization With Python: The Complete Guide
Help Companies Make Sense of Their Data with this Data Science Primer
By Eduonix Technologies | in Online Courses
Data science is all about collecting, sorting, and analyzing vast amounts of information to understand current and future trends. From tech to medicine, virtually every industry relies on data science to move forward, which makes now the best time to get into this lucrative field. With an emphasis on data visualization, this course acts are your data science primer. You'll start with a detailed look at the basics, along with Matplotlib, Python’s very own visualization library. From there, you'll move on to more advanced concepts and work toward creating real visualizations with Python.
- Access 59 lectures & 7 hours of content 24/7
- Understand the importance of data science
- Familiarize yourself w/ Matplotlib & visualizing data via Python
- Develop a greater understanding of linear general statistics & data analysis
- Explore data clustering, hypothesis gradient descent & advanced data visualizations
- Learn how to build real data visualizations w/ Python
Instructor
Eduonix creates and distributes high-quality technology training content. Their team of industry professionals has been training manpower for more than a decade. They aim to teach technology the way it is used in the industry and professional world. They have a professional team of trainers for technologies ranging from Mobility, Web and Enterprise, and Database and Server Administration.Important Details
- Length of time users can access this course: lifetime
- Access options: web and mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: intermediate
Requirements
- Internet required
Course Outline
- Introduction to Course
- Introduction - 1:01
- Overview of Course - 4:05
- Understanding Concepts of Data Science - 6:58
- Python as a Tool - 3:23
- Crash Course of Python - 10:09
- Sample Scripts with Loops in Python - 7:47
- Object Oriented Programming - 6:31
- Functional Tools - 4:15
- Data Visualization
- Understanding Data Visualization - 4:13
- Matplotlib library - 8:04
- Bar Chart - 10:00
- Line Charts - 6:42
- Scatter Plots - 6:00
- A1. Activity for Data Visualization - 7:52
- Linear Algebra
- What are Vectors. Various operations of vectors - 4:11
- Vectors - 7:57
- Understanding Matrices - 5:31
- Matrices - 9:38
- A2. Activity for Vectors Implementation - 9:33
- A3. Activity for Matrix Implementation - 7:18
- Statistics
- A. Single Set of Data - 2:32
- Single set of data - 7:08
- Concepts of Central Tendencies - 4:54
- Central Tendencies - 7:46
- Dispersion - 9:04
- A4. Activity for implementation of statistics - 7:34
- Probability
- Probability Concepts - 3:07
- The Normal Distribution - 9:09
- Central Limit Theorem - 7:23
- A5.Activity for understanding - 6:02
- Data Analysis
- Understanding Data Analysis - 3:43
- Exploring One dimensional Data - 8:12
- Exploring Two dimensional data - 12:49
- Exploring many dimensions - 8:49
- Bubble charts representation - 4:17
- Data Munging - 8:13
- A6. Activity for understanding data analysis - 7:15
- Advanced Data Visualization
- Visualizing the contecnt of a 2D array - 7:27
- Adding a colormap legend to th figure - 3:38
- Visualizing nonuniform 2D data - 7:48
- Visualizing a 2D scalar Field - 4:46
- Visualizing contour lines - 7:32
- Polar charts - 5:56
- Plotting log charts for research - 8:37
- Export Feature - Data Visualization
- Generating a PNG picture - 9:13
- Generating PDF documents - 5:53
- Multiple graph plotting and export - 7:56
- Inserting sub figures - 4:35
- Hypothesis and Gradient Descent
- Understanding Hypothesis - 3:46
- Implementation of hypothesis in Python - 13:22
- Gradient Descent - 4:08
- Implementation of Gradient Descent - 12:44
- A7. Activity for illustration of Gradient Descent - 14:54
- A7. Output for Gradient Descent Activity - 5:12
- Data Clustering
- Data Clustering concepts - 11:36
- Developing a data cluster model - 10:22
- Illustration of data clustering - 14:41
- A8 Activity for understanding data clusters - 7:10
Access
Lifetime
Content
4.5 hours
Lessons
19
Mathematical Foundations For Machine Learning & AI
Demystify the Math That Powers Today's AI Innovations
By Eduonix Technologies | in Online Courses
With self-driving cars on the road and virtual assistants inside our phones, it's clear we're moving toward an AI-powered future. As such, demand is high for those who understand the science that powers these innovations, and this course can help you join their ranks. Designed with the beginner in mind, this course will give you the mathematical foundation required for writing programs and algorithms for AI and machine learning. You'll explore linear algebra, multivariate calculus, and probability theory, and emerge ready to put these algorithms to use in your own AI projects.
- Access 19 lectures & 4.5 hours of content 24/7
- Explore the core mathematical concepts for machine learning
- Learn how to implement machine learning concepts w/ R & Python
- Understand how neural networks are put together & how they operate
Instructor
Eduonix creates and distributes high-quality technology training content. Their team of industry professionals has been training manpower for more than a decade. They aim to teach technology the way it is used in the industry and professional world. They have a professional team of trainers for technologies ranging from Mobility, Web and Enterprise, and Database and Server Administration.Important Details
- Length of time users can access this course: lifetime
- Access options: web and mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: intermediate
Requirements
- Internet required
Course Outline
- Introduction
- Introduction - 3:52
- Linear Algebra
- Scalars, Vectors, Matrices, and Tensors - 21:14
- Vector and Matrix Norms - 9:35
- Vectors, Matrices, and Tensors in Python - 21:27
- Special Matrices and Vectors - 13:35
- Eigenvalues and Eigenvectors - 11:41
- Norms and Eigendecomposition - 28:21
- Multivariate Calculus
- Introduction to Derivatives - 19:24
- Basics of Integration - 11:08
- Gradients - 12:05
- Gradient Visualization - 18:49
- Optimization - 18:51
- Probability Theory
- Intro to Probability Theory - 11:00
- Probability Distributions - 10:13
- Expectation, Variance, and Covariance - 11:23
- Graphing Probability Distributions in R - 12:31
- Covariance Matrices in R - 9:49
- Probaility Theory
- Special Random Variables - 10:52
Access
Lifetime
Content
4 hours
Lessons
28
R Programming For Beginners
Analyze & Visualize Data with the Language of Choice for Data Professionals
By Eduonix Technologies | in Online Courses
Created by statisticians and designed for statistical computing, R is the language of choice for working with data. As such, you'll want to come to grips with it if you're pursuing a career in machine learning, data visualization, or data analysis. This course breaks down the R programming language into beginner-friendly terms. Blending theory and practice together, this course will take you through R's syntax, rules, and benefits and help you build your own experimental programs; so you can kickstart your own data-driven career.
- Access 28 lectures & 4 hours of content 24/7
- Dive into data analysis & data visualization w/ R
- Familiarize yourself w/ R's syntax, rules & benefits
- Explore working w/ data sets, creating charts & statistics, and more
- Use R to analyze data sets & create graphical representations of data
Instructor
Eduonix creates and distributes high-quality technology training content. Their team of industry professionals has been training manpower for more than a decade. They aim to teach technology the way it is used in the industry and professional world. They have a professional team of trainers for technologies ranging from Mobility, Web and Enterprise, and Database and Server Administration.Important Details
- Length of time users can access this course: lifetime
- Access options: web and mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: intermediate
Requirements
- Internet required
Course Outline
- Introduction
- Intro - 2:52
- Introduction to R
- Introduction - 2:52
- Installation of R programming - 3:41
- Basic Fundamentals - 13:32
- Data sets and packages in R tool - 8:40
- Charts
- Bar charts for one variable - 6:00
- Pie charts for one variable - 6:44
- Histograms - 12:06
- Boxplots - 6:37
- Statistics
- Descriptive Statistics - 11:43
- Modifying Data - 15:25
- Data Structures - 7:18
- Data Frames - 7:43
- Activity Line Chart Plot - 8:51
- Activity Scattered Plots (2D and 3D) - 6:13
- Data Simulation
- Working with the Data file - 13:35
- Grouped charts in R - 14:39
- Scattered Plots for associations - 10:03
- Loop functions - 13:19
- Simulation - 13:23
- Clustered Bar chart - 9:18
- Grouping of Data
- Scatter Plots by groups - 9:33
- Profiling R code - 9:46
- Correlations - 6:43
- Bivariate regression - 5:13
- New Lecture
- Two-sample t-test and Paired t-test - 9:02
- Activity Cluster Analysis - 8:20
Access
Lifetime
Content
7 hours
Lessons
55
Introduction To Data Science Using R Programming
Master 2 Data Science Staples as You Dive into Data Analysis & Visualization
By Eduonix Technologies | in Online Courses
If you hope to cash in on the demand for data scientists, you'll need to master two tools: the R programming language and R statistical environment. Data experts leverage this dynamic duo to analyze and visualize mounds of information, and this course will get you up to speed. Jump in, and you'll cover basic and advanced data visualization, implementing statistics, data manipulation, and more concepts essential for wrangling data like a pro.
- Access 55 lectures & 7 hours of content 24/7
- Familiarize yourself w/ the R programming language & R statistical environment
- Dive into both data analytics & visualization
- Understand the R language's basic syntax
- Learn how to import, organize, visualize & export data w/ R statistical environment
Instructor
Eduonix creates and distributes high-quality technology training content. Their team of industry professionals has been training manpower for more than a decade. They aim to teach technology the way it is used in the industry and professional world. They have a professional team of trainers for technologies ranging from Mobility, Web and Enterprise, and Database and Server Administration.Important Details
- Length of time users can access this course: lifetime
- Access options: web and mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: intermediate
Requirements
- Internet required
Course Outline
- Introduction
- Introduction - 1:53
- Basics of R tool
- Introduction to Course - 6:01
- R programming installation and concepts - 7:31
- R programming computations - 9:16
- Basic Data Visualization
- Data Visualization - Module - 5:07
- Pie charts - 8:15
- Bar charts - 12:36
- Boxplots - 10:19
- Histograms - 7:15
- Line charts - 7:29
- Scatterplots - 10:14
- Case Study Basic data visualization - 4:30
- Advanced Data Visualization
- Advanced Data Visualization - 2:22
- Basic Illustration of ggplot2 package - 8:34
- Facetting - 7:39
- Boxplots and Jittered Plots - 4:24
- Histograms and Frequency Polygons - 7:46
- Bar Charts and Time Series - 12:58
- Basic Plot Types - 8:10
- Case Study for ggplot2 package Scatterplot Encircling - 7:44
- Surface Plots - 7:33
- Revealing uncertainity - 7:31
- Weighted data - 9:12
- Drawing Maps- Vector Boundries - 6:23
- Drawing Maps - Point Metadata - 6:13
- Diamonds data for research - 10:32
- Dealing with overlapping - 8:41
- Statistical summaries - 7:28
- Scatterplot from excel file - 9:35
- Heatmap and area chart from excel file - 9:19
- Various bar charts from excel file - 10:27
- Leaflet Maps
- Implementing Leaflet with R tool - 5:48
- Adding Markers in map - 4:33
- Popups and Labels - 10:47
- Shiny Framework using Leaflet and R - 9:14
- Statistics
- Mean, median and mode - 10:57
- Linear Regression - 8:40
- Multiple Regression - 9:27
- Logistic Regression - 6:49
- Normal Distribution - 9:38
- Binomial Distribution - 6:31
- Poisson Regression - 5:49
- Analysis of Covariance - 8:15
- Time Series Analysis - 10:06
- Case study Time Series from dataset - 4:17
- Decision Tree - 7:06
- Implementation of decision tree in Dataset - 4:13
- Nonlinear Least Square - 7:50
- Case Study- Random Forest - 7:09
- Survival Analysis - 7:10
- Data Manipulation
- Case Study Exporting data in R - 9:36
- Data Munging and Visualization - 7:56
- Hierarchial Clustering - 6:26
- K means clustering - 7:49
Access
Lifetime
Content
5 hours
Lessons
36
Machine Learning With R
Expand Your AI Understanding with a Practical Approach to R Programming
By Eduonix Technologies | in Online Courses
From Netflix's recommendation system to Tesla's self-driving cars, machine learning is all around us, and more companies are getting on board with what this technology can offer. Serving as your machine learning primer, this course offers a comprehensive look at machine learning, the algorithms that power it, and how you can implement them with the R programming language. You'll dive into what makes today's AI innovations tick, explore key tools, like TensorFlow, and get hands-on training as you explore neural networks, decisions trees, and more.
- Access 36 lectures & 5 hours of content 24/7
- Explore implementing machine learning algorithms w/ the R language
- Walk through creating neural networks & implementing them in R
- Familiarize yourself w/ TensorFlow & H2O
Instructor
Eduonix creates and distributes high-quality technology training content. Their team of industry professionals has been training manpower for more than a decade. They aim to teach technology the way it is used in the industry and professional world. They have a professional team of trainers for technologies ranging from Mobility, Web and Enterprise, and Database and Server Administration.Important Details
- Length of time users can access this course: lifetime
- Access options: web and mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: intermediate
Requirements
- Internet required
Course Outline
- Introduction
- Introduction - 1:42
- Starting up- Machine learning with R - 8:03
- What is Artificial Intelligence and machine learning - 4:32
- Flow of machine learning - 5:04
- Machine Learning vs Deep Learning - 5:14
- R programming tool
- R tool and installation - 5:07
- R data structures - 10:54
- H2O Package
- Basics of Machine learning - 4:33
- Supervised and unsupervised learning - 10:54
- Case study- K means clustering - 6:33
- Installation of H2O package - 5:57
- Performing Regression with H2O - 14:55
- Analysing the regression with H2O - 11:09
- TensorFlow Package
- Tensorflow package - 5:14
- Performing Regression with TensorFlow - 9:37
- Analysing the regression with TensorFlow - 14:16
- Performance of model using TensorFlow - 9:56
- First Machine Learning
- Caret Package for Machine Learning - 14:05
- Machine Learning with dataset - 11:54
- Iris dataset Implementation - 7:16
- Evaluation of Algorithms with models - 8:54
- Selecting Best Model in Machine Learning - 6:17
- Artificial Neural Networks
- Creating and Visualizing Neural networks - 5:32
- Demonstration of sample neural network - 12:45
- Prediction Analysis of neural network - 10:45
- Cross Validation Box plot - 10:16
- Activity- Dataset to Neural Network - 10:04
- Cluster Generation
- Cluster Generation - 6:30
- Cluster Generation Output Analysis - 8:32
- Decision Trees
- Decision Trees of Machine Learning - 5:18
- Car Evaluation Problem Statement - 12:52
- Plotting a Decision Tree - 10:45
- Prediction Analysis- Decision Tree - 6:35
- Text Mining
- Introduction to Text Mining - 9:38
- Text Mining with R - 9:55
Access
Lifetime
Content
5.5 hours
Lessons
19
Unreal Game Development For Beginners
Expand Your Programming Skills & Create a Complete 3D Game From Scratch
By Eduonix Technologies | in Online Courses
AI is all around us, including the video games we play. A great way to put your programming education into practice, this course takes you through creating a complete 3D game from scratch with the Unreal game development engine. From materials to physics and design, you'll get a comprehensive look at all things game development as you work through creating your own game project.
- Access 19 lectures & 5.5 hours of content 24/7
- Learn about game development as you create your own game project w/ Unreal
- Dive into game materials & creating a procedural racecourse
- Explore game physics & HUD animations
- Walk through lighting, landscape design & creating cinematics
Instructor
Eduonix creates and distributes high-quality technology training content. Their team of industry professionals has been training manpower for more than a decade. They aim to teach technology the way it is used in the industry and professional world. They have a professional team of trainers for technologies ranging from Mobility, Web and Enterprise, and Database and Server Administration.Important Details
- Length of time users can access this course: lifetime
- Access options: web and mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: intermediate
Requirements
- Internet required
Course Outline
- Introduction
- Introduction - 2:35
- Landscape Building - 18:17
- Materials
- Static Meshes and the Materials Editor - 28:51
- Creating Material Instances - 21:41
- Foliage Tool and Tesselated Materials - 23:41
- Creating a Procedural Racecourse - 20:35
- Blocking Volumes - 11:24
- Physics
- Custom Vehicle Blueprint and Physics - 26:12
- Dynamic Vehicle Effects - 16:49
- Creating a Race Timer HUD - 48:24
- Refining Race Timer Functionality - 10:13
- HUD Animations- Race Countdown - 18:15
- Vehicle Speedometer - 15:21
- Respawn Function - 4:47
- Design
- Landscape and Design - 11:06
- Lighting and Post Processing - 31:30
- Cinematics - 16:50
- Packaging Project - 5:35
Access
Lifetime
Content
15.5 hours
Lessons
62
Machine Learning For Absolute Beginners
Create Real Machine Learning Solutions with 9 Hands-on Projects
By Eduonix Technologies | in Online Courses
Machine learning has a reputation for being complex, but one of the best ways to master it is with real, hands-on training. Packed with different machine learning projects, this course takes a practical approach to teaching your the essentials of this booming field. From credit card fraud detection to natural language processing, you'll explore what machine learning can do as you walk through the essentials, dive into machine learning algorithms, and flex your programming muscles with Python.
- Access 62 lectures & 15.5 hours of content 24/7
- Dive into supervised & unsupervised learning algorithms & implement them into projects
- Get hands-on training using algorithms for credit card fraud detection, object recognition & more
- Bolster your machine learning understanding w/ 9 real-world projects
Instructor
Eduonix creates and distributes high-quality technology training content. Their team of industry professionals has been training manpower for more than a decade. They aim to teach technology the way it is used in the industry and professional world. They have a professional team of trainers for technologies ranging from Mobility, Web and Enterprise, and Database and Server Administration.Important Details
- Length of time users can access this course: lifetime
- Access options: web and mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: intermediate
Requirements
- Internet required
- Some math and Python experience required
Course Outline
- An Introduction to Machine Learning
- Introduction - 0:58
- What is Machine Learning - 10:53
- Types and Applications of ML - 25:45
- AI vs ML - 9:43
- Essential Math for ML and AI - 17:04
- Supervised Learning - part 1
- Introduction to Supervised Learning - 13:38
- Linear Methods for Classification - 16:35
- Linear Methods for Regression - 11:51
- Support Vector Machines - 15:42
- Basis Expansions - 11:00
- Model Selection Procedures - 13:58
- Bonus! Supervised Learning Project in Python Part 1 - 15:24
- Bonus! Supervised Learning Project in Python Part 2 - 15:23
- Unsupervised Learning
- Introduction to Unsupervised Learning - 11:36
- Association Rules - 14:13
- Cluster Analysis - 13:19
- Reinforcement Learning - 16:33
- Bonus! KMeans Clustering Project - 14:14
- Neural Networks
- Introduction to Neural Networks - 12:26
- The Perceptron - 10:20
- The Backpropagation Algorithm - 12:19
- Training Procedures - 13:37
- Convolutional Neural Networks - 15:55
- Real World Machine Learning
- Introduction to Real World ML - 10:34
- Choosing an Algorithm - 8:44
- Design and Analysis of ML Experiments - 10:22
- Common Software for ML - 10:46
- Final Project
- Setting up OpenAI Gym - 12:43
- Building and Training the Network Part 1 - 16:14
- Building and Training the Network Part 2 - 21:54
- Project 1 Board Game Review Prediction
- Intro - 1:39
- Board Game Review Prediction - Building the Dataset Part 1 - 9:49
- Board Game Review Prediction - Building the Dataset Part 2 - 16:41
- Board Game Review Prediction - Training the Models - 15:17
- Project 2 Credit Card Fraud Detection
- Intro - 2:13
- Credit Card Fraud Detection - The Dataset - 22:23
- Credit Card Fraud Detection - The Algorithms - 20:41
- Project 3 Stock Market Clustering
- Intro - 1:55
- Stock Market Clustering - Building the Dataset Part 1 - 16:08
- Stock Market Clustering - Building the Dataset Part 2 - 12:36
- Stock Market Clustering - KMeans and PCA Part 1 - 19:10
- Stock Market Clustering - KMeans and PCA Part 2 - 20:47
- Project 4 Intro to Natural Language Processing
- Intro - 1:27
- Tokenizing, Stop Words, and Stemming - 22:49
- Tagging, Chunking, and Named Entity Recognition - 31:55
- Text Classification - 23:57
- Project 5 Object Recognition
- Intro - 1:20
- Loading and Preprocessing the CIFAR10 Dataset - 25:57
- Building and Deploying the All-CNN Network Part 1 - 25:24
- Building and Deploying the All-CNN Network Part 2 - 20:41
- Project 6 Image Super Resolution
- Intro - 1:09
- Quality Metrics and Preprocessing Images - 34:07
- Image Super Resolution using Deep Learning - 47:23
- Project 7 Text Classification
- Intro - 1:02
- Feature Engineering - 48:07
- Deploying Sklearn Classifiers - 26:58
- Project 8 - KMeans
- Intro - 1:07
- Preprocessing Images for Clustering - 38:56
- Evaluation and Visualization - 28:35
- Project 9 PCA
- Intro - 0:53
- The Elbow Method - 22:50
- PCA Compression and Visualization - 29:43
Terms
- Unredeemed licenses can be returned for store credit within 15 days of purchase. Once your license is redeemed, all sales are final.
