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What's Included
Google Assistant Automation IoT Development
$200 Value
The Ultimate Hands-On Hadoop: Tame your Big Data!
$200 Value
Data Science, Deep Learning, Machine Learning with Python: Hands-On
$200 Value
Taming Big Data with Spark Streaming Scala: Hands-On
$200 Value
C++ for Absolute Beginners!
$200 Value
The Complete Python Course: Learn Python by Doing in 2022
$200 Value
Rust Programming Master Class: From Beginner to Expert
$200 Value
Search Operative
$200 Value
Google Assistant Automation IoT Development
Learn Internet of Things Automation of Google Assistant Apple Home
By Mammoth Interactive | in Online Courses
- Description
- Instructor
- Specs
You'll learn about Google Assistant development from scratch!
Assistants represent one of the major trends in 2020. Some have even suggested they'll eventually supplant our app-based ecosystem. With apps for Assistants, you're building an app that works on smart speakers, like Google Home, and devices like Android phones, and Android watches.
App leaders like Apple, Amazon, and Google, who want to guarantee that people spend most of their time in their apps, are placing big bets on Assistants. And devices like Google Home are penetrating into our homes.
- Access 163 lectures 15 hours of content 24/7
- Learn how Google Assistant and Smart Home work
- Learn JavaScript to build a web app to control your appliances
- Manage events with Google's Firebase storage deployment
- Learn to code in Swift and Xcode for the Apple App Store
- Connect to HomeKit in your iOS app
- Build homes and accessories in your app
Mammoth Interactive | Top-Rated Instructor
4.3/5 Instructor Rating: starf; starf; starf; starf; starf; starf;
Mammoth Interactive produces XBOX 360, iPhone, iPad, Android, HTML 5, ad games, and more. It's owned by top-rated instructor John Bura. Mammoth Interactive recently sold a game to Nickelodeon! John has been contracted by many different companies to provide game design, audio, programming, level design, and project management. To this day John has 40 commercial games that he has contributed to. Several of the games he has produced have risen to number 1 in Apple's app store. In his spare time, John likes to play ultimate Frisbee, cycle, and work out.Important Details
- Length of time users can access this course: lifetime
- Access options: desktop mobile
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
- Have questions on how digital purchases work? Learn more here
Requirements
- Any device with basic specifications
The Ultimate Hands-On Hadoop: Tame your Big Data!
Hadoop Tutorial with MapReduce, HDFS, Spark, Flink, Hive, HBase, MongoDB, Cassandra, Kafka + More!
By Frank Kane | in Online Courses
- Description
- Instructor
- Specs
The world of Hadoop and "Big Data" can be intimidating - hundreds of different technologies with cryptic names form the Hadoop ecosystem. With this Hadoop tutorial, you'll not only understand what those systems are and how they fit together - but you'll go hands-on and learn how to use them to solve real business problems! Learn and master the most popular big data technologies in this comprehensive course, taught by a former engineer and senior manager from Amazon and IMDb. We'll go way beyond Hadoop itself, and dive into all sorts of distributed systems you may need to integrate with.
4.6/5 average rating: starf; starf; starf; starf; starf; starf;
- Access 101 lectures 14 hours of content 24/7
- Design distributed systems that manage "big data" using Hadoop a related technologies
- Use Pig Spark to create scripts to process data on a Hadoop cluster in more complex ways
- Analyze non-relational data using HBase, Cassandra, MongoDB
- Use HDFS MapReduce for storing and analyzing data at scale
Frank Kane | Founder, Sundog Education
4.5/5 Instructor Rating: starf; starf; starf; starf; starf; starf;
Frank Kane spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology and teaching others about big data analysis.Important Details
- Length of time users can access this course: lifetime
- Access options: desktop mobile
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
- Have questions on how digital purchases work? Learn more here
Requirements
- Any device with basic specifications
Course Outline
- Learn all the buzzwords! And install Hadoop.
- Tips for Using This Course - 1:09
- If you have trouble downloading Hortonworks...
- Warning for Apple M1 Users
- Introduction, and install Hadoop on your desktop - 19:01
- The Hortonworks and Cloudera Merger, and how it affects this course. - 3:01
- Hadoop Overview and History - 7:44
- Overview of the Hadoop Ecosystem - 16:48
- Using Hadoop's Core: HDFS and MapReduce
- HDFS: What it is, and how it works - 13:56
- Alternate MovieLens download location
- Install the MovieLens dataset into HDFS using the Ambari UI - 6:22
- Install the MovieLens dataset into HDFS using the command line - 7:52
- MapReduce: What it is, and how it works - 10:42
- How MapReduce distributes processing - 12:59
- MapReduce example: Break down movie ratings by rating score - 11:37
- Notes on MRJob installation
- Installing Python, MRJob, and nano - 13:19
- Code up the ratings histogram MapReduce job and run it - 7:36
- Rank movies by their popularity - 7:06
- Check your results against mine! - 8:25
- Programming Hadoop with Pig
- Introducing Ambari - 9:49
- Introducing Pig - 6:27
- Example: Find the oldest movie with a 5-star rating using Pig - 15:09
- Find old 5-star movies with Pig - 9:42
- More Pig Latin - 7:36
- Find the most-rated one-star movie - 1:56
- Pig Challenge: Compare Your Results to Mine! - 5:39
- Programming Hadoop with Spark
- Why Spark? - 10:08
- The Resilient Distributed Dataset (RDD) - 10:14
- Find the movie with the lowest average rating - with RDD's - 15:33
- Datasets and Spark 2.0 - 6:30
- Find the movie with the lowest average rating - with DataFrames - 10:02
- Movie recommendations with MLLib - 12:43
- Filter the lowest-rated movies by number of ratings - 2:52
- Check your results against mine! - 6:42
- Using Relational Data Stores with Hadoop
- What is Hive? - 6:33
- Use Hive to find the most popular movie - 10:45
- How Hive works - 9:12
- Use Hive to find the movie with the highest average rating - 1:56
- Compare your solution to mine. - 4:12
- Integrating MySQL with Hadoop - 8:02
- Cheat Sheet for the following lecture
- Install MySQL and import our movie data - 7:45
- Use Sqoop to import data from MySQL to HFDS/Hive - 7:01
- Use Sqoop to export data from Hadoop to MySQL - 7:16
- Using non-relational data stores with Hadoop
- Why NoSQL? - 13:57
- What is HBase - 12:57
- Import movie ratings into HBase - 13:30
- Use HBase with Pig to import data at scale. - 11:21
- Cassandra overview - 14:53
- If you have trouble installing Cassandra...
- Installing Cassandra - 10:53
- Write Spark output into Cassandra - 11:00
- MongoDB overview - 17:19
- Install MongoDB, and integrate Spark with MongoDB - 12:44
- Using the MongoDB shell - 7:48
- Choosing a database technology - 16:01
- Choose a database for a given problem - 5:02
- Querying your Data Interactively
- Overview of Drill - 7:57
- Setting up Drill - 10:58
- Querying across multiple databases with Drill - 7:09
- Overview of Phoenix - 8:57
- Install Phoenix and query HBase with it - 7:02
- Integrate Phoenix with Pig - 11:47
- Overview of Presto - 6:41
- Install Presto, and query Hive with it. - 12:29
- Query both Cassandra and Hive using Presto. - 9:03
- Managing your Cluster
- YARN explained - 10:03
- Tez explained - 4:58
- Use Hive on Tez and measure the performance benefit - 8:37
- Mesos explained - 7:15
- ZooKeeper explained - 13:12
- Simulating a failing master with ZooKeeper - 6:49
- Oozie explained - 11:58
- Set up a simple Oozie workflow - 16:54
- Zeppelin overview - 5:04
- Use Zeppelin to analyze movie ratings, part 1 - 12:28
- Use Zeppelin to analyze movie ratings, part 2 - 9:48
- Hue overview - 8:08
- Other technologies worth mentioning - 4:37
- Feeding Data to your Cluster
- Kafka explained - 9:50
- Setting up Kafka, and publishing some data. - 7:24
- Publishing web logs with Kafka - 10:21
- Flume explained - 10:18
- Set up Flume and publish logs with it. - 7:46
- Set up Flume to monitor a directory and store its data in HDFS - 9:14
- Analyzing Streams of Data
- Spark Streaming: Introduction - 14:29
- Analyze web logs published with Flume using Spark Streaming - 14:20
- Monitor Flume-published logs for errors in real time - 2:02
- Exercise solution: Aggregating HTTP access codes with Spark Streaming - 4:26
- Apache Storm: Introduction - 9:29
- Count words with Storm - 15:49
- Flink: An Overview - 6:55
- Counting words with Flink - 10:20
- Designing Real-World Systems
- Best Of The Rest - 9:26
- Review: How the pieces fit together - 6:31
- Understanding your requirements - 8:04
- Sample application: consume webserver logs and keep track of top-sellers - 10:08
- Sample application: serving movie recommendations to a website - 11:20
- Design a system to report web sessions per day - 2:53
- Exercise solution: Design a system to count daily sessions - 4:26
- Learning More
- Books and online resources - 5:32
View Full Curriculum
Data Science, Deep Learning, Machine Learning with Python: Hands-On
Complete Hands-On Machine Learning Tutorial with Data Science, Tensorflow, AI, Neural Networks
By Frank Kane | in Online Courses
- Description
- Instructor
- Specs
Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path.
4.6/5 average rating: starf; starf; starf; starf; starf; starf;
- Access 110 lectures 15 hours of content 24/7
- Build artificial neural networks with Tensorflow Keras
- Make predictions using linear regression, polynomial regression, multivariate regression
- Implement machine learning at massive scale with Apache Spark's MLLib
- Design evaluate A/B tests using T-Tests and P-Values
Frank Kane | Founder, Sundog Education
4.5/5 Instructor Rating: starf; starf; starf; starf; starf; starf;
Frank Kane spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology and teaching others about big data analysis.Important Details
- Length of time users can access this course: lifetime
- Access options: desktop mobile
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: beginner
- Have questions on how digital purchases work? Learn more here
Requirements
- Any device with basic specifications
Course Outline
- Getting Started
- Introduction - 2:41
- Installation: Getting Started
- [Activity] WINDOWS: Installing and Using Anaconda Course Materials - 12:37
- [Activity] MAC: Installing and Using Anaconda Course Materials - 10:02
- [Activity] LINUX: Installing and Using Anaconda Course Materials - 10:57
- Python Basics, Part 1 - 4:59
- Python Basics, Part 2 - 5:17
- Python Basics, Part 3 - 2:46
- Python Basics, Part 4 - 4:02
- Introducing the Pandas Library - 10:08
- Statistics and Probability Refresher, and Python Practice
- Types Of Data - 6:58
- Mean, Median, Mode - 5:26
- Using mean, median, and mode in Python - 8:20
- Variation and Standard Deviation - 11:12
- Probability Density Function; Probability Mass Function - 3:27
- Common Data Distributions - 7:45
- Percentiles and Moments - 12:33
- A Crash Course in matplotlib - 13:46
- Advanced Visualization with Seaborn - 17:30
- Covariance and Correlation - 11:31
- Conditional Probability - 16:04
- Exercise Solution: Conditional Probability of Purchase by Age - 2:20
- Bayes' Theorem - 5:23
- Predictive Models
- Linear Regression - 11:01
- Polynomial Regression - 8:04
- Multiple Regression, and Predicting Car Prices - 16:26
- Multi-Level Models - 4:36
- Machine Learning with Python
- Supervised vs. Unsupervised Learning, and Train/Test - 8:57
- Using Train/Test to Prevent Overfitting a Polynomial Regression - 5:47
- Bayesian Methods: Concepts - 3:59
- Implementing a Spam Classifier with Naive Bayes - 8:05
- K-Means Clustering - 7:23
- Clustering people based on income and age - 5:14
- Measuring Entropy - 3:09
- WINDOWS: Installing GraphViz - 0:22
- MAC: Installing GraphViz - 1:16
- LINUX: Installing GraphViz - 0:54
- Decision Trees: Concepts - 8:43
- Decision Trees: Predicting Hiring Decisions - 9:47
- Ensemble Learning - 5:59
- XGBoost - 15:29
- Support Vector Machines (SVM) Overview - 4:27
- Using SVM to cluster people using scikit-learn - 8:38
- Recommender System
- User-Based Collaborative Filtering - 7:57
- Item-Based Collaborative Filtering - 8:15
- Finding Movie Similarities - 9:08
- Improving the Results of Movie Similarities - 7:59
- Making Movie Recommendations to People - 10:22
- Improve the recommender's results - 5:29
- More Data Mining and Machine Learning Techniques
- K-Nearest-Neighbors: Concepts - 3:44
- Using KNN to predict a rating for a movie - 12:29
- Dimensionality Reduction; Principal Component Analysis - 5:44
- PCA Example with the Iris data set - 9:05
- Data Warehousing Overview: ETL and ELT - 9:05
- Reinforcement Learning - 12:44
- Reinforcement Learning Q-Learning with Gym - 12:56
- Understanding a Confusion Matrix - 5:17
- Measuring Classifiers (Precision, Recall, etc.) - 6:35
- Dealing with Real-World Data
- Bias/Variance Tradeoff - 6:15
- K-Fold Cross-Validation to avoid overfitting - 10:26
- Data Cleaning and Normalization - 7:10
- Cleaning web log data - 10:56
- Normalizing numerical data - 3:22
- Detecting outliers - 6:21
- Feature Engineering and the Curse of Dimensionality - 6:03
- Imputation Techniques for Missing Data - 7:48
- Handling Unbalanced Data - 5:35
- Binning, Transforming, Encoding, Scaling, and Shuffling - 7:51
- Apache Spark: Machine Learning on Big Data
- Installing Spark - Part 1 - 6:59
- Installing Spark - Part 2 - 7:20
- Spark Introduction - 9:10
- Spark and the Resilient Distributed Dataset (RDD) - 11:42
- Introducing MLLib - 5:09
- Decision Trees in Spark - 16:15
- K-Means Clustering in Spark - 11:23
- TF / IDF - 6:43
- Searching Wikipedia with Spark - 8:21
- Using the Spark DataFrame API for MLLib - 8:07
- Experimental Design
- Deploying Models to Real-Time Systems - 8:42
- A/B Testing Concepts - 8:23
- T-Tests and P-Values - 5:59
- Hands-on With T-Tests - 6:04
- Determining How Long to Run an Experiment - 3:24
- A/B Test Gotchas - 9:26
- Deep Learning and Neural Networks
- Deep Learning Pre-Requisites - 11:43
- The History of Artificial Neural Networks - 11:14
- Deep Learning in the Tensorflow Playground - 12:00
- Deep Learning Details - 9:29
- Introducing Tensorflow - 11:29
- Using Tensorflow, Part 1 - 13:10
- Using Tensorflow, Part 2 - 12:03
- Introducing Keras - 13:33
- Using Keras to Predict Political Parties - 12:05
- Convolutional Neural Networks (CNN's) - 11:27
- Using CNN's for handwriting recognition - 8:02
- Recurrent Neural Networks (RNN's) - 11:02
- Using a RNN for sentiment analysis - 9:37
- Transfer Learning - 12:14
- Tuning Neural Networks - 4:39
- Deep Learning Regularization Techniques - 6:21
- The Ethics of Deep Learning - 11:02
- Generative Models
- Variational Auto-Encoders (VAE's) - how they work - 10:23
- Variational Auto-Encoders (VAE) - Hands-on with Fashion MNIST - 26:31
- Generative Adversarial Networks (GAN's) - How they work - 7:39
- Generative Adversarial Networks (GAN's) - Playing with some demos - 11:22
- Generative Adversarial Networks (GAN's) - Hands-on with Fashion MNIST - 15:20
- Learning More about Deep Learning - 1:44
- Final Project
- Your final project assignment - 6:19
- Final project review - 10:26
- You made it!
- More to Explore - 2:59
View Full Curriculum
Taming Big Data with Spark Streaming Scala: Hands-On
Process Massive Streams of Data in Real Time Start Working Towards a Career in Big Dat
By Frank Kane | in Online Courses
- Description
- Instructor
- Specs
Big Data analysis is an essential component of any company organization that works with mass amounts of data, and it's a constantly adapting and innovating field. Spark Streaming is a new and quickly developing technology for processing mass data sets in real-time. Whether it's clickstream data from a major website, sensor data from an Internet of Things deployment, financial data, or any other large stream of data, Spark Streaming has the capability to transform and analyze that data as it is created.
The professional applications of this technology are obvious, and this course will get you up to speed not just in Spark Streaming, but in Big Data generally, so you can confidently start looking for high-paying Big Data jobs.
4.3/5 average rating: starf; starf; starf; starf; starf; starf;
- Access 35 lectures 6 hours of content 24/7
- Get a crash course in the Scala programming language
- Learn how Apache Spark operates on a cluster
- Set up discretized streams with Spark Streaming transform them as data is received
- Analyze streaming data over sliding windows of time
- Maintain stateful information across streams of data
- Connect Spark Streaming with highly scalable source of data, including Kafka, Flume, Kinesis
- Dump streams of data in real-time to NoSQL databases such as Cassandra
Frank Kane | Founder, Sundog Education
4.5/5 Instructor Rating: starf; starf; starf; starf; starf; starf;
Frank Kane spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology and teaching others about big data analysis.Important Details
- Length of time users can access this course: lifetime
- Access options: desktop mobile
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
- Have questions on how digital purchases work? Learn more here
Requirements
- Any device with basic specifications
Course Outline
- Getting Started
- Tip: Apply for a Twitter Developer Account now
- Introduction, and Getting Set Up - 13:20
- Stream Live Tweets with Spark Streaming! - 14:27
- A Crash Course in Scala
- Scala Basics: Part 1 - 24:27
- Flow Control in Scala - 9:28
- Functions in Scala - 9:08
- Data Structures in Scala - 22:28
- Spark Streaming Concepts
- Introduction to Spark - 7:06
- The Resilient Distributed Dataset (RDD) - 10:40
- RDD's in Action: Simple Word Count Application - 8:02
- Introduction to Spark Streaming - 6:32
- Revisiting the PrintTweets Application - 7:31
- Windowing: Aggregating Data over Longer Time Spans - 5:00
- Fault Tolerance in Spark Streaming - 6:06
- Spark Streaming Examples with Twitter
- Saving Tweets to Disk - 13:24
- Tracking the Average Tweet Length - 10:17
- Tracking the Most Popular Hashtags - 15:52
- Spark Streaming Examples with Clickstream / Apache Access Log Data
- Tracking the Top URL's Requested - 14:19
- Alarming on Log Errors - 13:09
- Integrating Spark Streaming with SparkSQL - 15:37
- Intro to Structured Streaming in Spark 2 - 8:27
- [Activity] Analyzing Apache Log files with Structured Streaming - 11:32
- Integrating with Other Systems
- Integrating with Apache Kafka - 12:20
- Integrating with Apache Flume - 8:51
- Integrating with Amazon Kinesis - 4:46
- Writing Custom Data Receivers - 5:53
- Integrating with Cassandra - 7:35
- Advanced Spark Streaming Examples
- Stateful Information in Spark Streams - 14:22
- Streaming K-Means Clustering - 15:40
- Streaming Linear Regression - 12:19
- Spark Streaming in Production
- [Activity] Packaging and running Spark code in production - 9:39
- Packaging your Code with SBT - 11:45
- Running on a Real Hadoop Cluster with EMR - 15:48
- Troubleshooting and Tuning Spark Jobs - 12:35
- You Made It!
- Learning More - 3:44
View Full Curriculum
C++ for Absolute Beginners!
Start Programming Today Using C++
By Joseph Delgadillo | in Online Courses
- Description
- Instructor
- Specs
If you are looking for a crash course in C++, you are going to love C++ for Absolute Beginners! We know that your time is valuable, so we included everything you need to know about C++ in a concise video course.
- Access 21 lectures 4 hours of content 24/7
- Get an introduction to C++
- Learn about integers, operators, strings
- Explore loops, if-statements, functions, pointers, switches
- Debug Vsual Studio memory
- Understand object-oriented programming
Joseph Delgadillo
4.2/5 Instructor Rating: starf; starf; starf; starf; starf; starf;
Joseph Delgadillo teaches skills ranging from computers and technology to entrepreneurship and digital marketing. Take a course with him to get an over-the-shoulder view of how experts are successful in their respective fields. He is a proud alumnus of Central Washington University.Important Details
- Length of time users can access this course: lifetime
- Access options: desktop mobile
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: beginner
- Have questions on how digital purchases work? Learn more here
Requirements
- Any device with basic specifications
Course Outline
- Welcome to the Course!
- Introduction to C++ - 16:52
- Basics - Integers, Operators, Namespaces and Strings
- Integers - 19:23
- Math Operations - 4:04
- Floating Point Numbers - 4:08
- Namespaces - 3:45
- Strings - 11:30
- More C++ Programming Fundamentals
- While Loops and Booleans - 9:11
- While Loops, Truthy Conditions and Ways to Manipulate Numbers - 10:27
- If-Statements - 7:08
- Arrays, Vectors, For Loops - 17:04
- Functions - 18:13
- Structs - 9:56
- Visual Studio Memory Debugging - 12:45
- Pointers - 15:05
- Switches - 21:02
- Object Oriented Programming - Classes
- Intro to Classes - 14:07
- Adding Class Functions - 8:15
- Static Variables - 12:59
- Private Members - 6:36
- Subclasses - 21:48
- Subclasses Continued - 25:57
View Full Curriculum
The Complete Python Course: Learn Python by Doing in 2022
Go from Beginner to Expert in Python by Building Projects
By Jose Salvatierra | in Online Courses
- Description
- Instructor
- Specs
Over the last few years, Python has become more and more popular. Demand for Python is booming in the job market and it is a skill that can help you enter some of the most exciting industries, including data science, web applications, home automation, and many more. Python is one of the "most loved" and "most wanted" programming languages according to recent industry surveys. If people are not using Python already, they want to start using Python. Learn Python from a software developer. If you want to master Python and write efficient, elegant, and simple code, this is the course you've been looking for!
4.6/5 average rating: starf; starf; starf; starf; starf; starf;
- Access 316 lectures 34 hours of content 24/7
- Get a broader deeper experience in Python
- Start at zero become an expert whilst learning all about the inner workings of Python
- Write professional Python code like a professional Python developer
- Embrace simplicity develop good programming habits
- Explore advanced Python, such as decorators, asynchronous development, managing project dependencies
- Improve your Python code with formatters linters
- Store data in a database so it's accessible searchable.
- Learn about web development using Flask, to create websites that you can share with users
- Extract information from existing websites using web scraping
- Control your browser using Selenium, to automate using almost any website!
- Interact with REST APIs to fetch data from other web applications
- Create desktop GUIs using Tkinter, turn them into executable applications you can share with non-technical users.
- Start working with unit testing in Python by learning about the unittest library
Jose Salvatierra | Founder of Teclado Software Engineer
4.6/5 Instructor Rating: starf; starf; starf; starf; starf; starf;
Jose Salvatierra has been teaching computer science and playing and teaching music (grades 1 to 8) for over four years, to students of all ages and all skill levels. He started programming at the age of 10, just a couple years after he started studying music, when his dad, excited that he had shown interest in similar things to himself, taught him the basics of Marin Saric’s METAL. Shortly thereafter they moved on to REALbasic, and from there Jose started learning Java and C.Important Details
- Length of time users can access this course: lifetime
- Access options: desktop mobile
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
- Have questions on how digital purchases work? Learn more here
Requirements
- Any device with basic specifications
Rust Programming Master Class: From Beginner to Expert
Study the Most Loved Programming Language of Programmers
By Nouman Azam | in Online Courses
- Description
- Instructor
- Specs
Rust is a system programming language which means they have speed and control but at the same time is much much better because of the safety features just like high-level languages. This makes rust so clearly stand out among all the other programming languages. Its popularity is increasing day by day and is therefore being adapted by bigger companies worldwide. This course will take you from beginner to expert level. This course is designed from a perspective of a student who has no prior knowledge of Rust and who is a Rusy beginner.
4.5/5 average rating: starf; starf; starf; starf; starf; starf;
- Access 41 lectures 10 hours of content 24/7
- Develop beginner to advance level skills of Rust Programming
- Learn the basic syntax of Rust its feature of memory safety
- Develop an understanding of advanced level concepts such as generics, traits, lifetimes, closures
- Gain hands-on experience of solving some intermediate to advance level problems using Rust
Nouman Azam | MATLAB Professor
4.4/5 Instructor Rating: starf; starf; starf; starf; starf; starf;
Nouman Azam received his Ph.D. Degree in Computer Science from the University of Regina in 2014. Prior to that, he completed his M.Sc. in Computer Software Engineering from the National University of Sciences and Technology, Pakistan, and Bachelor's in Computer Sciences from the National University of Computer and Emerging Sciences, Pakistan in 2007 and 2005, respectively.Nouman has over 10 years of teaching experience. He has taught almost all the major computer science subjects including introduction to computers, computer organization and architecture, operation systems, computer networks, image processing, digital logic design, discrete structures, and many others. He has extensive knowledge of tools such as MATLAB, QTSpim, C++, Java, and other academic tools used for teaching and instructing purposes.
Important Details
- Length of time users can access this course: lifetime
- Access options: desktop mobile
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: beginner
- Have questions on how digital purchases work? Learn more here
Requirements
- Any device with basic specifications
Course Outline
- Introduction to Course and Instructor
- Course Intro - 2:35
- Running and Compiling Programs - 9:04
- Installing Rust and Web-based Playground Enviroment - 13:08
- Codes and Data
- Basic Programming
- Program Outputs and Comments - 19:35
- Variables and Scalar Data Types - 19:53
- More on Variables- Shadowing, Constants - 18:59
- Compund Data Types-Strings - 18:36
- Compund Data Types-Tuples, Arrays - 21:44
- Compund Data Types - Vectors - 13:16
- Functions and Inputs - 25:31
- Ownership: The Heart of Rust Programming
- Ownership, Premitive and non-Premitive - 15:09
- Application Memory (Heap and Stack) - 12:40
- Onwership and References in Functions - 17:50
- Mutable and Immutable References - 15:30
- String Concatenation and Ownership - 6:19
- Control Structures
- Conditional If and its Varaints - 19:36
- If let, Nested if and the Match - 13:52
- Match Statement - 12:52
- While and Simple loops - 14:57
- For Loops and its Variants - 12:41
- Break and Continue - 11:41
- Structures, Traits, Generics and Enums
- Structs basics - 21:50
- Traits and Default Implementations - 19:20
- Functions within a Trait - 8:47
- Enums - 20:23
- Generics - 17:52
- Option Enum - 15:54
- Result Enum - 10:45
- Hash Maps - 17:08
- Iterators, Lifetimes and Closures
- Lifetimes (Part 1)_Final - 15:15
- Lifetimes (Part 2)_Final - 18:21
- Closures (Part 1)_Final - 17:43
- 4 - Closures (Part 2)_Final - 13:52
- 5 - Function types_Final - 14:13
- 6 - Iterators (Part 1)_Final - 19:05
- 7 - Iterators (Part 2)_Final - 12:15
- Rust Modules and Crates
- 1 - Rust Modules (Part 1)_Final - 8:15
- 2- Rust Modules (Part 2)_Final - 25:47
- 3 - Using External crates_Final - 10:20
- 4 - Publishing your Crate_Final - 19:38
View Full Curriculum
Search Operative
Grow your Search Rankings with Professional SEO Methods
By Tom Owsiak | in Online Courses
- Description
- Instructor
- Specs
Are you a website owner who struggles to rank on the first page of Google? Are you a small business and would like to get more local clients from Google’s search? Google is massively competitive these days. There are millions of new websites being created daily. You can no longer randomly post articles and hope to go viral and get traffic. Writing articles is a part of SEO but there are other steps involved. And for that, you need an understanding of the SEO mechanics. You need to know the actions that are involved in ranking websites on search engines. And that’s where this course comes in. The goal here is to demystify the SEO process.
- Access 13 lectures 1 hour of content 24/7
- Learn the foundation of SEO
- Review your website figure out why it is underperforming while your competitors are getting all the traffic
- Discover the tools that you need for SEO research
- Learn how to track your SEO efforts see your traffic grow
Tom Wiztek | Recruitment and Marketing Specialist
4.2/5 Instructor Rating: starf; starf; starf; starf; starf; starf;
Tom Wiztek is a recruitment and marketing specialist. Working for over 2 years in the recruitment industry, he learned the ins and outs of hiring people. He then decided to publish courses related to finding a job because he realized that a lot of candidates are professionals in their field, but don't know to present themselves. Over the past couple of years, he has been involved in numerous projects related to traffic generation, online marketing, blogs, app creation, and web design.Important Details
- Length of time users can access this course: lifetime
- Access options: desktop mobile
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
- Have questions on how digital purchases work? Learn more here
Requirements
- Any device with basic specifications
Course Outline
- Module 1: Foundation of SEO
- Course Outline - 2:16
- Foundation of SEO - 5:31
- Essential On-Page Factors - 9:02
- Off-Page Factors that determine your Page Rank - 11:13
- The SEO Technicals that Crawlers Love - 11:06
- Module 2: Tools of SEO
- Webmaster Tool 1 - 8:35
- Webmaster Tool 2 - 8:39
- The No.1 Tool for Website Research - 10:35
- Chrome Extensions for SEO Analysis - 4:39
- Module 3: Monthly SEO Activities
- Backlink Building Strategies - 7:23
- Keyword Research Beginnings - 7:56
- How to Rank your Local Business - 3:39
- Summary and Where to Next - 1:07
View Full Curriculum
Terms
- Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.
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