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1. Statistics and Data Science in R
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 visualising 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, Scatter plot, Bar plot, Histogram, Scatterplot matrix,
Heat map, 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 *NOToffer 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 modelers 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
Are you ready to join us to Keep Growing Up
2. Complete iOS 11 Machine Learning Masterclass
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
Are you ready to join us to Keep Growing Up
3. Introduction to Data Science with Python
This course introduces Python programming as a way to have hands-on experience with Data
Science. It starts with a few basic examples in Python before moving onto doing statistical
processing. The course then introduces Machine Learning with techniques such as regression,
classification, clustering, and density estimation, in order to solve various data problems.
Basic knowledge
 This course is for beginners, but it helps to have some basic understanding of statistics
(mean, median, scatter plot) and preliminary knowledge of any programming. The course
also assumes that you know how to download and install various programs/apps, and you
are able to edit and debug simple programs
What you will learn
 Writing simple Python scripts to do basic mathematical and logical operations
 Loading structured data in a Python environment for processing
 Creating descriptive statistics and visualizations
 Finding correlations among numerical variables
 Using regression analysis to predict the value of a continuous variable
 Building classification models to organize data into pre-determined classes
 Organizing given data into meaningful clusters
 Applying basic machine learning techniques for solving various data problems
Are you ready to join us to Keep Growing Up
4. Introduction to Data Science with R
This course introduces R programming environment as a way to have hands-on experience with
Data Science. It starts with a few basic examples in R before moving onto doing statistical
processing. The course then introduces Machine Learning with techniques such as regression,
classification, clustering, and density estimation, in order to solve various data problems.
Basic knowledge
 This course is for beginners, but it helps to have some basic understanding of statistics
(mean, median, scatter plot) and preliminary knowledge of any programming. The course
also assumes that you know how to download and install various programs/apps, and you
are able to edit and debug simple programs
What you will learn
 Writing simple R programs to do basic mathematical and logical operations
 Loading structured data in a R environment for processing
 Creating descriptive statistics and visualizations
 Finding correlations among numerical variables
 Using regression analysis to predict the value of a continuous variable
 Building classification models to organize data into pre-determined classes
 Organizing given data into meaningful clusters
 Applying basic machine learning techniques for solving various data problems
Are you ready to join us to Keep Growing Up
5. Machine Learning In The Cloud With Azure Machine
Learning
The history of data science, machine learning, and artificial Intelligence is long, but it’s only
recently that technology companies - both start-ups and tech giants across the globe have begun
to get excited about it… Why? Because now it works. With the arrival of cloud computing and
multi-core machines - we have enough compute capacity at our disposal to churn large volumes
of data and dig out the hidden patterns contained in these mountains of data.
This technology comes in handy, especially when handling Big Data. Today, companies collect
and accumulate data at massive, unmanageable rates for website clicks, credit card transactions,
GPS trails, social media interactions, and so on. And it is becoming a challenge to process all the
valuable information and use it in a meaningful way. This is where machine learning algorithms
come into the picture. These algorithms use all the collected “past” data to learn patterns and
predict results or insights that help us make better decisions backed by actual analysis.
You may have experienced various examples of Machine Learning in your daily life (in some
cases without even realizing it). Take for example
Credit scoring, which helps the banks to decide whether to grant the loans to a particular
customer or not - based on their credit history, historical loan applications, customers’ data and
so on
Or the latest technological revolution from right from science fiction movies – the self-driving
cars, which use Computer vision, image processing, and machine learning algorithms to learn
from actual drivers’ behavior.
Or Amazon's recommendation engine which recommends products based on buying patterns of
millions of consumers.
In all these examples, machine learning is used to build models from historical data, to forecast
the future events with an acceptable level of reliability. This concept is known as Predictive
analytics. To get more accuracy in the analysis, we can also combine machine learning with
other techniques such as data mining or statistical modeling.
This progress in the field of machine learning is great news for the tech industry and humanity in
general.
But the downside is that there aren’t enough data scientists or machine learning engineers who
understand these complex topics.
Well, what if there was an easy to use a web service in the cloud - which could do most of the
heavy lifting for us? What if scaled dynamically based on our data volume and velocity?
The answer - is new cloud service from Microsoft called Azure Machine Learning. Azure
Machine Learning is a cloud-based data science and machine learning service which is easy to
use and is robust and scalable like other Azure cloud services. It provides visual and
collaborative tools to create a predictive model which will be ready-to-consume on web services
without worrying about the hardware or the VMs which perform the calculations.
The advantage of Azure ML is that it provides a UI-based interface and pre-defined algorithms
that can be used to create a training model. And it also supports various programming and
scripting languages like R and Python.
In this course, we will discuss Azure Machine Learning in detail. You will learn what features it
provides and how it is used. We will explore how to process some real-world datasets and find
some patterns in that dataset.
Do you know what it takes to build sophisticated machine learning models in the cloud?
How to expose these models in the form of web services?
Do you know how you can share your machine learning models with non-technical knowledge
workers and hand them the power of data analysis?
These are some of the fundamental problems data scientists and engineers struggle with on a
daily basis.
This course teaches you how to design, deploy, configure and manage your machine learning
models with Azure Machine Learning. The course will start with an introduction to the Azure
ML toolset and features provided by it and then dive deeper into building some machine learning
models based on some real-world problems.
If you’re serious about building scalable, flexible and powerful machine learning models in the
cloud, then this course is for you.
These data science skills are in great demand, but there’s no easy way to acquire this knowledge.
Rather than rely on hit and trial method, this course will provide you with all the information you
need to get started with your machine learning projects.
Startups and technology companies pay big bucks for experience and skills in these technologies
They demand data science and cloud engineers make sense of their dormant data collected on
their servers - and in turn, you can demand top dollar for your abilities.
You may be a data science veteran or an enthusiast - if you invest your time and bring an
eagerness to learn, we guarantee you real, actionable education at a fraction of the cost you can
demand as a data science engineer or a consultant. We are confident your investment will come
back to you many-fold in no time.
So, if you're ready to make a change and learn how to build some cool machine learning models
in the cloud, click the "Add to Cart" button below.
Look, if you're serious about becoming an expert data engineer and generating a greater income
for you and your family, it’s time to take action.
Imagine getting that promotion which you’ve been promised for the last two presidential terms.
Imagine getting chased by recruiters looking for skilled and experienced engineers by companies
that are desperately seeking help. We call those good problems to have.
Imagine getting a massive bump in your income because of your newly-acquired, in-demand
skills.
That’s what we want for you. If that’s what you want for yourself, click the “Add to Cart” button
below and get started today with our “Machine Learning In The Cloud With Azure Machine
Learning”.
Let’s do this together!
Who is the target audience?
 Data science enthusiasts
 Software and IT engineers
 Statisticians
 Cloud engineers
 Software architects
 Technical and non-technical tech founders
Basic knowledge
 Access to a free or paid account for Azure
 Basic knowledge about cloud computing and data science
 Basic knowledge about IT infrastructure setup
 Desire to learn something new and continuous improvement
What you will learn
 Learn about Azure Machine Learning
 Learn about various machine learning algorithms supported by Azure Machine Learning
 Learn how to build and run a machine learning experiment with real world datasets
 Learn how to use classification machine learning algorithms
 Learn how to use regression machine learning algorithms
 Learn how to expose the Azure ML machine learning experiment as a web service or API
 Learn how to integrate the Azure ML machine learning experiment API with a web
application
Are you ready to join us to Keep Growing Up
Click to Continue Reading:
Simpliv Youtube Course & Tutorial :

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iOS ML Masterclass

  • 1. 1. Statistics and Data Science in R 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 visualising your findings.
  • 2.  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, Scatter plot, Bar plot, Histogram, Scatterplot matrix, Heat map, 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
  • 3. 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 *NOToffer 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 modelers 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
  • 4. Are you ready to join us to Keep Growing Up 2. Complete iOS 11 Machine Learning Masterclass 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
  • 5.  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.
  • 6. 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?
  • 7.  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 Are you ready to join us to Keep Growing Up 3. Introduction to Data Science with Python
  • 8.
  • 9. This course introduces Python programming as a way to have hands-on experience with Data Science. It starts with a few basic examples in Python before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems. Basic knowledge  This course is for beginners, but it helps to have some basic understanding of statistics (mean, median, scatter plot) and preliminary knowledge of any programming. The course also assumes that you know how to download and install various programs/apps, and you are able to edit and debug simple programs What you will learn  Writing simple Python scripts to do basic mathematical and logical operations  Loading structured data in a Python environment for processing  Creating descriptive statistics and visualizations  Finding correlations among numerical variables  Using regression analysis to predict the value of a continuous variable  Building classification models to organize data into pre-determined classes  Organizing given data into meaningful clusters  Applying basic machine learning techniques for solving various data problems Are you ready to join us to Keep Growing Up 4. Introduction to Data Science with R
  • 10. This course introduces R programming environment as a way to have hands-on experience with Data Science. It starts with a few basic examples in R before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems. Basic knowledge  This course is for beginners, but it helps to have some basic understanding of statistics (mean, median, scatter plot) and preliminary knowledge of any programming. The course also assumes that you know how to download and install various programs/apps, and you are able to edit and debug simple programs What you will learn  Writing simple R programs to do basic mathematical and logical operations  Loading structured data in a R environment for processing  Creating descriptive statistics and visualizations  Finding correlations among numerical variables  Using regression analysis to predict the value of a continuous variable  Building classification models to organize data into pre-determined classes  Organizing given data into meaningful clusters  Applying basic machine learning techniques for solving various data problems Are you ready to join us to Keep Growing Up 5. Machine Learning In The Cloud With Azure Machine Learning
  • 11. The history of data science, machine learning, and artificial Intelligence is long, but it’s only recently that technology companies - both start-ups and tech giants across the globe have begun to get excited about it… Why? Because now it works. With the arrival of cloud computing and multi-core machines - we have enough compute capacity at our disposal to churn large volumes of data and dig out the hidden patterns contained in these mountains of data. This technology comes in handy, especially when handling Big Data. Today, companies collect and accumulate data at massive, unmanageable rates for website clicks, credit card transactions, GPS trails, social media interactions, and so on. And it is becoming a challenge to process all the valuable information and use it in a meaningful way. This is where machine learning algorithms come into the picture. These algorithms use all the collected “past” data to learn patterns and predict results or insights that help us make better decisions backed by actual analysis. You may have experienced various examples of Machine Learning in your daily life (in some cases without even realizing it). Take for example Credit scoring, which helps the banks to decide whether to grant the loans to a particular customer or not - based on their credit history, historical loan applications, customers’ data and so on Or the latest technological revolution from right from science fiction movies – the self-driving cars, which use Computer vision, image processing, and machine learning algorithms to learn from actual drivers’ behavior. Or Amazon's recommendation engine which recommends products based on buying patterns of millions of consumers. In all these examples, machine learning is used to build models from historical data, to forecast the future events with an acceptable level of reliability. This concept is known as Predictive
  • 12. analytics. To get more accuracy in the analysis, we can also combine machine learning with other techniques such as data mining or statistical modeling. This progress in the field of machine learning is great news for the tech industry and humanity in general. But the downside is that there aren’t enough data scientists or machine learning engineers who understand these complex topics. Well, what if there was an easy to use a web service in the cloud - which could do most of the heavy lifting for us? What if scaled dynamically based on our data volume and velocity? The answer - is new cloud service from Microsoft called Azure Machine Learning. Azure Machine Learning is a cloud-based data science and machine learning service which is easy to use and is robust and scalable like other Azure cloud services. It provides visual and collaborative tools to create a predictive model which will be ready-to-consume on web services without worrying about the hardware or the VMs which perform the calculations. The advantage of Azure ML is that it provides a UI-based interface and pre-defined algorithms that can be used to create a training model. And it also supports various programming and scripting languages like R and Python. In this course, we will discuss Azure Machine Learning in detail. You will learn what features it provides and how it is used. We will explore how to process some real-world datasets and find some patterns in that dataset. Do you know what it takes to build sophisticated machine learning models in the cloud? How to expose these models in the form of web services?
  • 13. Do you know how you can share your machine learning models with non-technical knowledge workers and hand them the power of data analysis? These are some of the fundamental problems data scientists and engineers struggle with on a daily basis. This course teaches you how to design, deploy, configure and manage your machine learning models with Azure Machine Learning. The course will start with an introduction to the Azure ML toolset and features provided by it and then dive deeper into building some machine learning models based on some real-world problems. If you’re serious about building scalable, flexible and powerful machine learning models in the cloud, then this course is for you. These data science skills are in great demand, but there’s no easy way to acquire this knowledge. Rather than rely on hit and trial method, this course will provide you with all the information you need to get started with your machine learning projects. Startups and technology companies pay big bucks for experience and skills in these technologies They demand data science and cloud engineers make sense of their dormant data collected on their servers - and in turn, you can demand top dollar for your abilities. You may be a data science veteran or an enthusiast - if you invest your time and bring an eagerness to learn, we guarantee you real, actionable education at a fraction of the cost you can demand as a data science engineer or a consultant. We are confident your investment will come back to you many-fold in no time.
  • 14. So, if you're ready to make a change and learn how to build some cool machine learning models in the cloud, click the "Add to Cart" button below. Look, if you're serious about becoming an expert data engineer and generating a greater income for you and your family, it’s time to take action. Imagine getting that promotion which you’ve been promised for the last two presidential terms. Imagine getting chased by recruiters looking for skilled and experienced engineers by companies that are desperately seeking help. We call those good problems to have. Imagine getting a massive bump in your income because of your newly-acquired, in-demand skills. That’s what we want for you. If that’s what you want for yourself, click the “Add to Cart” button below and get started today with our “Machine Learning In The Cloud With Azure Machine Learning”. Let’s do this together! Who is the target audience?  Data science enthusiasts  Software and IT engineers  Statisticians  Cloud engineers  Software architects  Technical and non-technical tech founders Basic knowledge  Access to a free or paid account for Azure  Basic knowledge about cloud computing and data science  Basic knowledge about IT infrastructure setup  Desire to learn something new and continuous improvement What you will learn  Learn about Azure Machine Learning  Learn about various machine learning algorithms supported by Azure Machine Learning  Learn how to build and run a machine learning experiment with real world datasets  Learn how to use classification machine learning algorithms  Learn how to use regression machine learning algorithms  Learn how to expose the Azure ML machine learning experiment as a web service or API  Learn how to integrate the Azure ML machine learning experiment API with a web application
  • 15. Are you ready to join us to Keep Growing Up Click to Continue Reading: Simpliv Youtube Course & Tutorial :