Resistant Materials Courses in Dubai

Course Details

• We are offering a free class for Arabic and French language for Non-Arabic people
• Our location is in Al Barsha 1 

Course Details

• Instructor led live training 
• Daily 2 hours classes ( Sunday to Thursday )
• One hour theory followed by the one hour practical demonstration in the system

Course Details

PMP 6th Edition Exam preparation
Totally exam oriented mentoring by an exam expert
50-60 hours of comprehensive coverage of all the areas
Based upon the most important questions from the recent exams
35 hours PDUs will be awarded 
Morning & Evening batches
Weekend batches also available

Course Details

This SAP MM program will equip you with substantial knowledge and skills to hold a key position in any organization like SAP Consultant, SAP End-User posts in the Oil & Gas, Logistics, Manufacturing etc companies. This is a complete hands-on job oriented SAP Material Management (Functional Consultant + End User Role) training.

- The trainer is a having a 15 years real-time experience across major MNCs in Dubai.

- Live classroom sessions with flexible timings

- Exhaustive course material & FAQs will be provided

- Free demo also available

Course Details

1. Training & Exam/Slot Booking:

MEDICAL CODING CERTIFICATION

• CPC (CERTIFIED PROFESSIONAL CODER)

MEDICAL BILLING CERTIFICATION

• CPB (CERTIFIED PROFESSIONAL BILLER)

MEDICAL AUDITING CERTIFICATION

• CPMA (CERTIFIED PROFESSIONAL MEDICAL AUDITOR)

Course Details

What are the following ways to improve and handle Group Trainings. How to handle any types of people in doing group trainings.

Course Details

If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass™ is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you’ll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We’re approaching a new era where only apps and games that are considered “smart” will survive. (Remember how Blockbuster went bankrupt when Netflix became a giant?) Jump the curve and adopt this innovative approach; the Complete iOS Machine Learning Masterclass™ will introduce Machine Learning in a way that’s both fun and engaging.

In this course, you will:

• Master the 3 fundamental branches of applied Machine Learning: Image & Video Processing, Text Analysis, and Speech & Language Recognition
• Develop an intuitive sense for using Machine Learning in your iOS apps
• Create 7 projects from scratch in practical code-along tutorials
• Find pre-trained ML models and make them ready to use in your iOS apps
• Create your own custom models
• Add Image Recognition capability to your apps
• Integrate Live Video Camera Stream Object Recognition to your apps
• Add Siri Voice speaking feature to your apps
• Dive deep into key frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit.
• Use Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder–even if you have zero experience
• Get FREE unlimited hosting for one year
• And more!

This course is also full of practical use cases and real-world challenges that allow you to practice what you’re learning. Are you tired of courses based on boring, over-used examples? Yes? Well then, you’re in a treat. We’ll tackle 5 real-world projects in this course so you can master topics such as image recognition, object recognition, and modifying existing trained ML models. You’ll also create an app that classifies flowers and another fun project inspired by Silicon Valley™ Jian Yang’s masterpiece: a Not-Hot Dog classifier app! 

Why Machine Learning on iOS

One of the hottest growing fields in technology today, Machine Learning is an excellent skill to boost your your career prospects and expand your professional tool kit. Many of Silicon Valley’s hottest companies are working to make Machine Learning an essential part of our daily lives. Self-driving cars are just around the corner with millions of miles of successful training. IBM’s Watson can diagnose patients more effectively than highly-trained physicians. AlphaGo, Google DeepMind’s computer, can beat the world master of the game Go, a game where it was thought only human intuition could excel.

In 2017, Apple has made Machine Learning available in iOS 11 so that anyone can build smart apps and games for iPhones, iPads, Apple Watches and Apple TVs. Nowadays, apps and games that do not have an ML layer will not be appealing to users. Whether you wish to change careers or create a second stream of income, Machine Learning is a highly lucrative skill that can give you an amazing sense of gratification when you can apply it to your mobile apps and games.

Why This Course Is Different

Machine Learning is very broad and complex; to navigate this maze, you need a clear and global vision of the field. Too many tutorials just bombard you with the theory, math, and coding. In this course, each section focuses on distinct use cases and real projects so that your learning experience is best structured for mastery.

This course brings my teaching experience and technical know-how to you. I’ve taught programming for over 10 years, and I’m also a veteran iOS developer with hands-on experience making top-ranked apps. For each project, we will write up the code line by line to create it from scratch. This way you can follow along and understand exactly what each line means and how to code comes together. Once you go through the hands-on coding exercises, you will see for yourself how much of a game-changing experience this course is.

As an educator, I also want you to succeed. I’ve put together a team of professionals to help you master the material. Whenever you ask a question, you will get a response from my team within 48 hours. No matter how complex your question, we will be there–because we feel a personal responsibility in being fully committed to our students.

By the end of the course, you will confidently understand the tools and techniques of Machine Learning for iOS on an instinctive level.

Don’t be the one to get left behind. Get started today and join millions of people taking part in the Machine Learning revolution.

topics: ios 11 swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios11 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection ios 11 swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios11 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection ios 11 swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios11 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection ios 11 swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios11 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection ios 11 swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios11 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection 

Who is the target audience?

• People with a basic foundation in iOS programming who would like to discover Machine Learning, a branch of Artificial Intelligence
• People who want to pursue a career combining app development and Machine Learning to become a hybrid iOS developer and ML expert
• Developers who would like to apply their Machine Learning skills by creating practical mobile apps
• Entrepreneurs who want to leverage the exponential technology of Machine Learning to create added value to their business could also take this course. However, this course does assume that you are familiar with basic programming concepts such as object oriented programming, variables, methods, classes, and conditional statements

BASIC KNOWLEDGE

• Basic understanding of programming
• Have access to a MAC computer or MACinCloud website

WHAT YOU WILL LEARN

• Build smart iOS 11 & Swift 4 apps using Machine Learning
• Use trained ML models in your apps
• Convert ML models to iOS ready models
• Create your own ML models
• Apply Object Prediction on pictures, videos, speech and text
• Discover when and how to apply a smart sense to your apps

Course Details

Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. 

This course is a gentle yet thorough introduction to Data Science, Statistics and R using real-life examples. 

Let’s parse that.

Gentle, yet thorough: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualise your findings. 

Data Science, Statistics and R: This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R. 

Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context. 

What's Covered:

Data Analysis with R: Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames

Linear Regression: Regression, Simple Linear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plots

Data Visualization in R: Line plot, Scatterplot, Bar plot, Histogram, Scatterplot matrix, Heatmap, Packages for Data Visualisation: Rcolorbrewer, ggplot2

Descriptive Statistics: Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots

Inferential Statistics: Random Variables, Probability Distributions, Uniform Distribution, Normal Distribution, Sampling, Sampling Distribution, Hypothesis testing, Test statistic, Test of significance

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

• Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role
• Yep! Engineers who want to understand basic statistics and lay a foundation for a career in Data Science
• Yep! Analytics professionals who have mostly worked in Descriptive analytics and want to make the shift to being modellers or data scientists
• Yep! Folks who've worked mostly with tools like Excel and want to learn how to use R for statistical analysis

BASIC KNOWLEDGE

• No prerequisites: We start from basics and cover everything you need to know. We will be installing R and RStudio as part of the course and using it for most of the examples. Excel is used for one of the examples and basic knowledge of Excel is assumed.

WHAT YOU WILL LEARN

• Harness R and R packages to read, process and visualize data
• Understand linear regression and use it confidently to build models
• Understand the intricacies of all the different data structures in R
• Use Linear regression in R to overcome the difficulties of LINEST() in Excel
• Draw inferences from data and support them using tests of significance
• Use descriptive statistics to perform a quick study of some data and present results

Course Details

This course is a really comprehensive guide to the Google Cloud Platform - it has ~25 hours of content and ~60 demos.

The Google Cloud Platform is not currently the most popular cloud offering out there - that's AWS of course - but it is possibly the best cloud offering for high-end machine learning applications. That's because TensorFlow, the super-popular deep learning technology is also from Google.

What's Included:

• Compute and Storage - AppEngine, Container Engineer (aka Kubernetes) and Compute Engine
• Big Data and Managed Hadoop - Dataproc, Dataflow, BigTable, BigQuery, Pub/Sub 
• TensorFlow on the Cloud - what neural networks and deep learning really are, how neurons work and how neural networks are trained.
• DevOps stuff - StackDriver logging, monitoring, cloud deployment manager
• Security - Identity and Access Management, Identity-Aware proxying, OAuth, API Keys, service accounts
• Networking - Virtual Private Clouds, shared VPCs, Load balancing at the network, transport and HTTP layer; VPN, Cloud Interconnect and CDN Interconnect
• Hadoop Foundations: A quick look at the open-source cousins (Hadoop, Spark, Pig, Hive and HBase)

Who is the target audience?

• Yep! Anyone looking to use the Google Cloud Platform in their organizations
• Yep! Any one who is interesting in architecting compute, networking, loading balancing and other solutions using the GCP
• Yep! Any one who wants to deploy serverless analytics and big data solutions on the Google Cloud
• Yep! Anyone looking to build TensorFlow models and deploy them on the cloud

BASIC KNOWLEDGE

• Basic understanding of technology - superficial exposure to Hadoop is enough.

WHAT YOU WILL LEARN

• Deploy Managed Hadoop apps on the Google Cloud
• Build deep learning models on the cloud using TensorFlow
• Make informed decisions about Containers, VMs and AppEngine
• Use big data technologies such as BigTable, Dataflow, Apache Beam and Pub/Sub

Course Details

Taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data. 

This course is a zoom-in, zoom-out, hands-on workout involving Hadoop, MapReduce and the art of thinking parallel. 

Let’s parse that.

Zoom-in, Zoom-Out:  This course is both broad and deep. It covers the individual components of Hadoop in great detail, and also gives you a higher level picture of how they interact with each other. 

Hands-on workout involving Hadoop, MapReduce: This course will get you hands-on with Hadoop very early on.  You'll learn how to set up your own cluster using both VMs and the Cloud. All the major features of MapReduce are covered - including advanced topics like Total Sort and Secondary Sort. 

The art of thinking parallel: MapReduce completely changed the way people thought about processing Big Data. Breaking down any problem into parallelizable units is an art. The examples in this course will train you to "think parallel". 

What's Covered: Lot's of cool stuff ..

Using MapReduce to:

• Recommend friends in a Social Networking site: Generate Top 10 friend recommendations using a Collaborative filtering algorithm. 
• Build an Inverted Index for Search Engines: Use MapReduce to parallelize the humongous task of building an inverted index for a search engine. 
• Generate Bigrams from text: Generate bigrams and compute their frequency distribution in a corpus of text. 

Build your Hadoop cluster: 

• Install Hadoop in Standalone, Pseudo-Distributed and Fully Distributed modes 
• Set up a Hadoop cluster using Linux VMs.
• Set up a cloud Hadoop cluster on AWS with Cloudera Manager.
• Understand HDFS, MapReduce and YARN and their interaction 

Customize your MapReduce Jobs: 

• Chain multiple MR jobs together
• Write your own Customized Partitioner
• Total Sort: Globally sort a large amount of data by sampling input files
• Secondary sorting 
• Unit tests with MR Unit
• Integrate with Python using the Hadoop Streaming API

.. and of course all the basics: 

• MapReduce: Mapper, Reducer, Sort/Merge, Partitioning, Shuffle and Sort
• HDFS & YARN: Namenode, Datanode, Resource manager, Node manager, the anatomy of a MapReduce application, YARN Scheduling, Configuring HDFS and YARN to performance tune your cluster. 

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

• Yep! Analysts who want to leverage the power of HDFS where traditional databases don't cut it anymore
• Yep! Engineers who want to develop complex distributed computing applications to process lot's of data
• Yep! Data Scientists who want to add MapReduce to their bag of tricks for processing data

BASIC KNOWLEDGE

• You'll need an IDE where you can write Java code or open the source code that's shared. IntelliJ and Eclipse are both great options.
• You'll need some background in Object-Oriented Programming, preferably in Java. All the source code is in Java and we dive right in without going into Objects, Classes etc
• A bit of exposure to Linux/Unix shells would be helpful, but it won't be a blocker

WHAT YOU WILL LEARN

• Develop advanced MapReduce applications to process BigData
• Master the art of "thinking parallel" - how to break up a task into Map/Reduce transformations
• Self-sufficiently set up their own mini-Hadoop cluster whether it's a single node, a physical cluster or in the cloud.
• Use Hadoop + MapReduce to solve a wide variety of problems: from NLP to Inverted Indices to Recommendations
• Understand HDFS, MapReduce and YARN and how they interact with each other
• Understand the basics of performance tuning and managing your own cluster

Course Details

Prerequisites: Working with HBase requires knowledge of Java

Record and run settings a team which includes 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with large-scale data processing jobs. 

Relational Databases are so stuffy and old! Welcome to HBase - a database solution for a new age. 

HBase: Do you feel like your relational database is not giving you the flexibility you need anymore? Column-oriented storage, no fixed schema and low latency make HBase a great choice for the dynamically changing needs of your applications. 

What's Covered: 

25 solved examples covering all aspects of working with data in HBase

CRUD operations in the shell and with the Java API, Filters, Counters, MapReduce 

Implement your own notification service for a social network using HBase

HBase and it’s role in the Hadoop ecosystem, HBase architecture and what makes HBase different from RDBMS and other Hadoop technologies like Hive. 

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

• Yep! Anyone who is interested in understanding HBase, NoSQL and distributed database management
• Yep! Engineers who want to move away from RDBMS and use HBase for their database solution
• Yep! Folks who are using Hadoop ecosystem tools for analytical processing and want a single system for both OLAP and OLTP

BASIC KNOWLEDGE

• You'll need to have an IDE where you can run Java code, Intellij IDEA and Eclipse are both good options
• You'll need to have some experience with Java programming

WHAT YOU WILL LEARN

• Set up a database for your application using HBase
• Integrate HBase with MapReduce for data processing tasks
• Create tables, insert, read and delete data from HBase
• Get an all-round understanding of HBase and it's role in the Hadoop ecosystem

Course Details

See why over 350,000 Simpliv members learn coding from Mark Lassoff and LearnToProgram.tv!

Few programming languages provide you with the flexibility and pure power of Python.

If you're becoming a professional developer, or are early in your development career, adding the Python skill set isn't just a resume embellishment-- It's an empowering language that will allow you to write procedural code in many types of environments and for many uses.

Python is commonly used for server-side programming for complex web applications or as a middle tier language providing web services or a communication layer with larger e-commerce systems. That being said, it's also a great language for beginners. The clear syntax makes it very easy to learn, and the powerful libraries make all types of programming possible. There are libraries for everything from games and graphics to complex mathematics to network and embedded programming.

Watch, Learn and Do

Programming is not a spectator sport and if you want to learn Python well, this course contains everything you need.

Skill oriented instructor-led lectures will demonstrate each important Python skill. You'll be able to study and modify the code on your own to cement each topic. Python code coding lab exercises will familiarize you not just with Python syntax, but how real problem-solving in Python is done. You'll complete a more comprehensive project to help you integrate the different skills that are part of core Python.

Who is the target audience?

• First time Python programmers
• Students and Teachers
• IT pros who want to learn to code
• Aspiring data scientists who want to add Python to their tool arsenal

BASIC KNOWLEDGE

• Students should be comfortable working in the PC or Mac operating system

WHAT YOU WILL LEARN

• How to use the Python Shell
• How to use interactive mode in Python
• How to develop and run programs in the IDLE editor
• How to run Python scripts directly from the command line
• How to use the print() function
• How to use separators and new line characters to modify command line output
• Assign variables of different types
• Understand integer, floating point, complex and string variables
• Be able to extract substrings
• Easily concatenate strings
• Create Lists and Tuples
• Create key value pairs and store them in dictionaries
• Understand mathematical operators used in Python
• Apply the order of operations to mathematical operations
• Utilize comparison operators to determine logical outcomes
• Use logical operators to join comparisons
• Write conditional statements to correctly branch code
• Use If, else and else if statements to apply branching
• Understand how and when to used nested if statements
• Use the shortcut Ternary operator
• Be able to use looping structures effectively
• Create While loops
• Be able to use For loops to loop through an objects properties
• Be able to construct nested loops and understand their utility.
• Cast variables from one type to another using built-in Python functions
• Use the Mathematical functions within Python to evaluate expressions
• Randomize numbers and selections with the Randomization tools
• Use Python String functions such as find(), join() and split()
• Create immutable tuples
• Access values within tuples
• Use tuple functions to manipulate tuple data
• Declare a dictionary and populate it with key/value pairs
• Access and edit values within dictionaries
• Extract date and time information from the time tuple
• Use the calendar object to work with calendar related information
• Create custom functions
• Send arguments to functions using order or keyword
• Create default function arguments
• Read and obtain keyboard input
• Read from a text file and process the data in Python
• Write to a text file from Python
• Append to a text file
• Handle exceptions with try/except/else in Python
• Use Python within the cgi-bin or a web server
• Process form data from Python

Course Details

Image Processing Applications on Raspberry Pi is a beginner course on the newly launched Raspberry Pi 3 and is fully compatible with Raspberry Pi 2 and Raspberry Pi Zero.

The course is ideal for those who are new to the Raspberry Pi and want to explore more about it.

You will learn the components of Raspberry Pi, connecting components to Raspberry Pi, installation of NOOBS operating system, basic Linux commands, Python programming and building Image Processing applications on Raspberry Pi.

This course will take beginners without any coding skills to a level where they can write their own programs.

Basics of Python programming language are well covered in the course.

Building Image Processing applications are taught in the simplest manner which is easy to understand.

Users can quickly learn hardware assembly and coding in Python programming for building Image Processing applications. By the end of this course, users will have enough knowledge about Raspberry Pi, its components, basic Python programming, and execution of Image Processing applications in the real time scenario.

The course is taught by an expert team of Electronics and Computer Science engineers, having PhD and Postdoctoral research experience in Image Processing. 

Anyone can take this course. No engineering knowledge is expected. Tutor has explained all required engineering concepts in the simplest manner.

The course will enable you to independently build Image Processing applications using Raspberry Pi.

This course is the easiest way to learn and become familiar with the Raspberry Pi platform.

By the end of this course, users will build Image Processing applications which includes scaling and flipping images, varying brightness of images, perform bit-wise operations on images, blurring and sharpening images, thresholding, erosion and dilation, edge detection, image segmentation. User will also be able to build real-world Image Processing applications which includes real-time human face eyes nose detection, detecting cars in video, real-time object detection, human face recognition and many more. 

The course provides complete code for all Image Processing applications which are compatible on Raspberry Pi 3/2/Zero.

Who is the target audience?

• Anyone who wants to explore Raspberry Pi and interested in building Image Processing applications

BASIC KNOWLEDGE

• Only High School Maths
• No prior programming knowledge is expected
• All the code files and images used in this course will be provided
• Hardware needed: Raspberry Pi 3/2/Zero, Monitor, Mouse, Keyboard, HDMI-VGA connector, USB flash drive (minimum storage capacity 2 GB), Micro SD card (minimum storage capacity 8 GB), Micro SD card reader, Power adapter (2 Amp, Micro-USB charger is preferred), USB Webcam (minimum 5 Megapixel resolution)

WHAT YOU WILL LEARN

• What is Raspberry Pi? and what are its components?
• Understand peripherals that need to be connected to Raspberry Pi
• Wire up your Raspberry Pi to create a fully functional computer
• Easily learn preparing SD Card to load Operating System for Raspberry Pi
• Install packages needed to build Image Processing applications
• Learn basic programming aspects of Python
• Create simple Image Processing applications using Python and OpenCV
• Build real-world Image Processing applications on Raspberry Pi

Course Details

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.

Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. 

This course is a down-to-earth, shy but confident take on machine learning techniques that you can put to work today

Let’s parse that.

The course is down-to-earth: it makes everything as simple as possible - but not simpler

The course is shy but confident: It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.

You can put ML to work today: If Machine Learning is a car, this car will have you driving today. It won't tell you what the carburetor is.

The course is very visual: most of the techniques are explained with the help of animations to help you understand better.

This course is practical as well: There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python.

The course is also quirky. The examples are irreverent. Lots of little touches: repetition, zooming out so we remember the big picture, active learning with plenty of quizzes. There’s also a peppy soundtrack, and art - all shown by studies to improve cognition and recall.

What's Covered:

Machine Learning: 

Supervised/Unsupervised learning, Classification, Clustering, Association Detection, Anomaly Detection, Dimensionality Reduction, Regression.

Naive Bayes, K-nearest neighbours, Support Vector Machines, Artificial Neural Networks, K-means, Hierarchical clustering, Principal Components Analysis, Linear regression, Logistics regression, Random variables, Bayes theorem, Bias-variance tradeoff

Natural Language Processing with Python: 

Corpora, stopwords, sentence and word parsing, auto-summarization, sentiment analysis (as a special case of classification), TF-IDF, Document Distance, Text Summarization, Text classification with Naive Bayes and K-Nearest Neighbours and Clustering with K-Means

Sentiment Analysis: 

Why it's useful, Approaches to solving - Rule-Based, ML-Based, Training, Feature Extraction, Sentiment Lexicons, Regular Expressions, Twitter API, Sentiment Analysis of Tweets with Python

Mitigating Overfitting with Ensemble Learning:

Decision trees and decision tree learning, Overfitting in decision trees, Techniques to mitigate overfitting (cross-validation, regularization), Ensemble learning and Random forests

Recommendations: Content-based filtering, Collaborative filtering and Association Rules learning

Get started with Deep learning: Apply Multi-layer perceptrons to the MNIST Digit recognition problem

A Note on Python: The code-alongs in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for both Python 2 and Python 3 wherever possible.

Who is the target audience?

• Yep! Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning
• Yep! Engineers who want to understand or learn machine learning and apply it to problems they are solving
• Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
• Yep! Tech executives and investors who are interested in big data, machine learning or natural language processing
• Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role

BASIC KNOWLEDGE

• No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.

WHAT YOU WILL LEARN

• Identify situations that call for the use of Machine Learning
• Understand which type of Machine learning problem you are solving and choose the appropriate solution
• Use Machine Learning and Natural Language processing to solve problems like text classification, text summarization in Python

Course Details

Taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data. 

Get your data to fly using Spark for analytics, machine learning and data science 

Let’s parse that.

What's Spark? If you are an analyst or a data scientist, you're used to having multiple systems for working with data. SQL, Python, R, Java, etc. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code.

Analytics: Using Spark and Python you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease. 

Machine Learning and Data Science : Spark's core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We'll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets. 

What's Covered:

Lot's of cool stuff ..

• Music Recommendations using Alternating Least Squares and the Audioscrobbler dataset
• Dataframes and Spark SQL to work with Twitter data
• Using the PageRank algorithm with Google web graph dataset
• Using Spark Streaming for stream processing 
• Working with graph data using the  Marvel Social network dataset 

.. and of course all the Spark basic and advanced features: 

• Resilient Distributed Datasets, Transformations (map, filter, flatMap), Actions (reduce, aggregate) 
• Pair RDDs , reduceByKey, combineByKey 
• Broadcast and Accumulator variables 
• Spark for MapReduce 
• The Java API for Spark 
• Spark SQL, Spark Streaming, MLlib and GraphFrames (GraphX for Python) 

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

• Yep! Analysts who want to leverage Spark for analyzing interesting datasets
• Yep! Data Scientists who want a single engine for analyzing and modelling data as well as productionizing it.
• Yep! Engineers who want to use a distributed computing engine for batch or stream processing or both

BASIC KNOWLEDGE

• The course assumes knowledge of Python. You can write Python code directly in the PySpark shell. If you already have IPython Notebook installed, we'll show you how to configure it for Spark
• For the Java section, we assume basic knowledge of Java. An IDE which supports Maven, like IntelliJ IDEA/Eclipse would be helpful
• All examples work with or without Hadoop. If you would like to use Spark with Hadoop, you'll need to have Hadoop installed (either in pseudo-distributed or cluster mode).

WHAT YOU WILL LEARN

• Use Spark for a variety of analytics and Machine Learning tasks
• Implement complex algorithms like PageRank or Music Recommendations
• Work with a variety of datasets from Airline delays to Twitter, Web graphs, Social networks and Product Ratings
• Use all the different features and libraries of Spark : RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming and GraphX

Course Details

A Note on the Python versions 2 and 3: The code-along in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the code-along. The source code has been provided for both Python 2 and Python 3 wherever possible.

What's Covered:

• Introductory Python: Functional language constructs; Python syntax; Lists, dictionaries, functions and function objects; Lambda functions; iterators, exceptions and file-handling
• Database operations: Just as much database knowledge as you need to do data manipulation in Python
• Auto-generating spreadsheets: Kill the drudgery of reporting tasks with xlsxwriter; automated reports that combine database operations with spreadsheet auto-generation
• Text processing and NLP: Python’s powerful tools for text processing - nltk and others.
• Website scraping using Beautiful Soup: Scrapers for the New York Times and Washington Post
• Machine Learning : Use sk-learn to apply machine learning techniques like KMeans clustering
• Hundreds of lines of code with hundreds of lines of comments
• Drill #1: Download a zip file from the National Stock Exchange of India; unzip and process to find the 3 most actively traded securities for the day
• Drill #2: Store stock-exchange time-series data for 3 years in a database. On-demand, generate a report with a time-series for a given stock ticker
• Drill #3: Scrape a news article URL and auto-summarize into 3 sentences
• Drill #4: Scrape newspapers and a blog and apply several machine learning techniques - classification and clustering to these

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

• Yep! Folks with zero programming experience looking to learn a new skill
• Machine Learning and Language Processing folks looking to apply concepts in a full-fledged programming language
• Yep! Computer Science students or software engineers with no experience in Java, but experience in Python, C++ or even C#. You might need to skip over some bits, but in general the class will still have new learning to offer you :-)

BASIC KNOWLEDGE

• No prior programming experience is needed :-)
• The course will use a Python IDE (integrated development environment) called iPython from Anaconda. We will go through a step-by-step procedure on downloading and installing this IDE.

WHAT YOU WILL LEARN

• Pick up programming even if you have NO programming experience at all
• Write Python programs of moderate complexity
• Perform complicated text processing - splitting articles into sentences and words and doing things with them
• Work with files, including creating Excel spreadsheets and working with zip files
• Apply simple machine learning and natural language processing concepts such as classification, clustering and summarization
• Understand Object-Oriented Programming in a Python context

Course Details

The Digital Marketing course gives you step by step training on how to market your business from scratch, across major marketing channels.

Course Details

Description:

This online Diploma program covers both trade practice and trade finance in order to provide a comprehensive coverage of trade for trade finance professionals.  This program is ideal for anyone seeking to prepare themselves for either CITF or CDCS / CSDG exams from London Institute of Banking and Finance or any trade finance qualification.  It is an ideal onboarding tool for professionals entering the Trade Finance department of banks or corporates!

Course Details

After Effects is designed to allow those new to video animation techniques or who are self-taught, to get the most from immensely powerful software. Learn to create rich compositions with video, Photoshop, Illustrator and other files. Create advanced test effects, video blending, keying effects and track mattes. Learn basic through advanced animation techniques that make After Effects an unrivalled motion graphics toolset. This course introduces you to the newest techniques and tools used in Adobe After Effects CC 2017.

Course Details

This course covers the details of Adobe Photoshop. Find out how to perform  editing tasks, cropping and straightening, colour and tonal adjustments, various selection techniques, working with layers, image retouching and restoration methods. We will show you how to achieve more creative effects with filters, layer effects, type and creating photos.

Course Details

Beautiful Handwriting is a project to improve any poor/illegible handwriting in a very short time. It requires average 15 hours for a normal person to complete the course successfully. There is no age bar.

Course Details

IELTS, is an international standardized test of English language proficiency for non-native English Speakers. This language test measures ability to communicate in English across all four language skills - listening, reading, writing and speaking - for people who intend to study or work where English is the first language. This course prepares candidates for the IELTS. Some of the features of the program are: 

• Need Based Intensive Program
• Diagnostic Test   Standard Study Material
• Geared towards the desired scores
• Flexible Hours and Timings
• Evaluation and Feedback  

Course Details

This workshop has been designed for those in leadership positions where they must speak in front of a demanding audience. This course is also suitable for those who are relatively new to the subject and who want some encouragement to speak up in meetings or want some training before they could begin making presentations on behalf of their organization with confidence.

This course is aimed at improving the learner speaking skills and learning some new techniques which will give them a persuasive edge when they are making a presentation, fielding difficult questions, or presenting complex information during a meeting, seminar etc.

During the course, the learner will learn through many classroom activities and by practising what they learn by applying it to real context of their roles!

Course Details

Group classes for ISC grade 12 (Gems Modern Academy) syllabus starting in the first week of April 2018. Classes will be held twice a week (2 hours each class). The course will include covering the syllabus as per the school schedule and monthly test will be held to test the student. A thorough revision of syllabus and past papers will be done during the last 3 months before the exam.

Course Details

I have more than 12 years experience in IELTS, Test preparation (ACADEMIC AND GENERAL) in UAE.