Coding for #AI and #machinelearning – online course/workshop

Coding for #AI and #machinelearning – online course/workshop

We are exploring a new way to learning how to code for AI and Machine Learning by applying ideas of  deliberate practise

Starting Oct 2018 – the workshop has limited places

Please contact [email protected] if you want to sign up for our online workshop (300 USD)

 

Deliberate practice is a technique which probably originated in the former Soviet Union to train world class athletes.

Deliberate practise is also used in learning complex skills like playing the violin – which require mastering many small steps and then putting the steps together in a complex whole (like a violin concert)

Deliberate practice always follows the same pattern: break the overall process down into parts, identify your weaknesses, test new strategies for each section, and then integrate your learning into the overall process.

We apply deliberate practise to learning coding for AI

To try this, we work in small sections of code which you should master bit by bit

We divide the code into four sections
1) Pandas, NumPy and MathPlotLib

2) Data manipulation and feature engineering

3) Machine Learning models and validation·

4) Deep learning

Each of these topics is divided into detail (subtopics) as below

(note the code itself is taken from the public domain and from existing books under Apache v2 license)

Pandas, NumPy and MathPlotLib
· initializing NumPy array
· Creating NumPy array
· NumPy datatypes
· Field access
· Basic slicing
· Advanced indexing
· Array math: Sum function, Transpose function
· Broadcasting
· Creating a pandas series
· Creating a pandas dataframe
· Reading / writing data from csv, text, Excel
· Basic statistics on dataframe
· Creating covariance on dataframe
· Creating correlation matrix on dataframe
· Concat or append operation, Merge, join dataframes
· Grouping operation, Pivot tables on Dataframe
· Plots, Bar charts, Pie charts etc for DataFrames

Data manipulation and feature engineering
· Converting categorical variable to numerical
· Normalization and scaling
· Univariate analysis
· Pandas dataframe visualization
· Multivariate analysis
· Correlation matrix
· Pair plot
· Scatter plots
· Find outliers
· Load data
· Normalize data
· Split data into train and test

Machine Learning models and validation·
Linear regression
· Linear regression model accuracy matrices
· Polynomial regression
· Regularization
· Nonlinear regression
· Logistic regression
· Confusion matrix
· Area Under the Curve
· Under-fitting, right-fitting, and over-fitting
· Logistic regression model training and evaluation
· Generalized Linear Model
· Decision tree model
· Support vector machine (SVM) model
· Plotting SVM decision boundaries
· k Nearest Neighbors model
· k-means clustering

Deep Learning·
sklearn perceptron code
· loading MNIST data for training MLP classifier
· sklearn MLP classifier
· Bernoulli RBM with classifier
· grid search with RBM + logistic regression
· Keras MLP
· Compile model
· Train model and evaluate
· dimension reduction using autoencoder
· de-noising using autoencoder
· Keras LSTM
·

Please contact [email protected] if you want to sign up for our online workshop (300 USD)

Image source: Vanessa Mae – Violin prodigy/ player

Enterprise AI workshop

Jumpstart your knowledge of Enterprise AI – Workshop two full days –

Saturdays/Remote(Online) starting Oct 2018

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Introduction

Launched for the first time, jointly delivered by Ajit Jaokar and Cheuk Ting Ho – Enterprise AI workshop is a two-day workshop targeting developers and strategists. Conducted in London on Saturdays in Sep and Oct in a conference room setting, the workshop has currently limited places. You can also participate remotely

Here is some more information

  • Our philosophy and approach to Enterprise AI is outlined below
  • Remote participation is possible for the live sessions
  • The sessions will be recorded
  • We may include more dates in future
  • Each session is two hours long – hence the workshop is a total of 16 hours for 8 sessions spanning 2 Saturdays
  • The workshop is ideal for strategists/ senior executives
  • We work with code through ‘code walkthroughs’ hence some knowledge of Python is good, but it is not mandatory. We can provide an overview session for Python/TensorFlow and Keras as an online video before the workshop
  • Our bios Ajit Jaokar (https://www.linkedin.com/in/ajitjaokar/) and Cheuk Ting Ho (https://www.linkedin.com/in/cheukting-ho/)
  • Workshop outline presented below. (Note It is subject to change)
  • Please contact ajit.jaokar at futuretext.com for pricing

 

 

Philosophy

The workshop is based on the following guidelines

Emphasis on the full AI pipeline

In this workshop, we explore the real world / large scale deployment of AI. The professional deployment of AI in Enterprises differs from the content in a typical training course. In larger organisations, the Data Science function typically spans three distinct roles: The Data Engineer, the Data Scientist and the DevOps Engineer. The Data Scientist is primarily responsible for developing the Machine Learning and Deep Learning algorithms. The Data Engineer and The DevOps Engineer roles work in conjunction with the Data Scientist to manage the product/service lifecycle.

To understand how these roles work together, it is important to understand the philosophy of DevOps. DevOps is concerned with managing rapid, frequent delivery which lends to the idea of Continuous Innovation and Continuous Delivery(CICD). DevOps creates a culture of increased collaboration, ownership and accountability enabling teams to create large-scale applications.

 

In this workshop, we take the approach of managing the AI pipeline using the philosophy of CICD. The approach suits a professional deployment of AI.

 

CICD (Continuous Improvement/Continuous Delivery). CI/CD can be seen as an evolution of Waterfall and Agile methodologies.

 

The business case for AI

Broadly, we can consider an AI process as one which improves with experience

The business case for Enterprise AI is a moving goalpost. It is driven by some of the considerations below which we discuss in the workshop

a)       Improving performance of existing business processes (ex: fraud detection, loan approval etc)

b)      Create a new business model / process

c)       Create barriers to entry by building superior models

d)      Develop IPR

e)      Bring non-transactional interaction in the Enterprise

f)        Re-engineering existing processes in the Enterprise

g)       Enhancing job functions where humans and AI can work together

 

Taking a pragmatic approach

The workshop takes a pragmatic approach to balance against the media hype

Competitive advantage of AI

It is no secret that AI creates competitive advantages and creates a ‘winner takes all’ situation.

Use case driven approach

We take a use case driven approach discussing deployment scenarios

How to build your own Enterprise AI portfolio/profile?

The overall focus is based on how you can build your own Enterprise AI focus/profile

 

Approach

Enterprise AI can be a vast topic. Considering the philosophy listed above, our approach involves taking a two-sided view.  We first cover the end-to-end implementation of two examples with code walkthroughs. We then consider these examples from an enterprise pipeline standpoint.

Session One: Introduction and Overview

Cover topics like

  • Why use AI in enterprises
  • Industry landscape of enterprise AI
  • Understanding different issues and strategies in Enterprise AI
  • Architectural design considerations for AI in enterprises
  • Understand what problems Enterprise AI solves
  • Understand the issues behind deploying Enterprise AI applications in scale for Enterprises
  • Study the strategies of large scale Enterprise AI vendors (Azure, Amazon and Google)
  • Use cases

 

Session Two: Unique considerations for Enterprise AI

Here, we cover specific drivers for Enterprise AI

  • Explainable AI
  • Productivity (AutoMl)
  • New regulatory structures responsible for AI ex GDPR, Payment regulation

 

Session Three: Understanding AI and the Enterprise

Enterprise AI does not exist in isolation. The word ‘Enterprise’ can be seen in terms of Enterprise workflows. We also consider the core Enterprise (a non-manufacturing company ex Insurance) and the Wider enterprise (including supply chain). This section is concerned with understanding how these workflows could change when AI is deployed in the Enterprise.  We first define Enterprise workflows using a typical ERP process ex from SAP as: Procure to Pay – Plan to Product – Order to Cash – Support customer-focused processes – Request to Service – Core Human Resources – Core Finance. We then explore how AI could potentially impact each of these functions both with the core and the wider Enterprise.

 

Session four: AI models used in the Enterprise

In this section, we study AI / Deep learning algorithms. These include:

  • Overview of Machine learning and Deep Learning
  • Deep-learning techniques. MLP CNN RNN Autoencoders

Session Five: End to End – Enterprise AI(Transactional)

Code walkthrough with a end to end example for Enterprise AI (Credit approval)

 

Session Six: End to End – Enterprise AI (Natural Language Processing)

Code walkthrough with a end to end example for Enterprise AI (Natural Language Processing)

Session seven: Deployment considerations

For the end to end examples discussed above, this session covers the deployment perspective considering CICD, DevOps etc (refer the Approach and Philosophy sections above)

Here we also cover the strategies for three vendors

ERP – SAP
Cloud – Microsoft
Data Integration – Talend

Session Eight: The Enterprise AI business case

This interactive session will explore the Business case for AI building on the above ideas. We also explore other areas such as Advances in Hardware, Expectations, Social Controversy and backlash, Need for Data, Limitations of Deep Learning, the war for Talent etc

 

Questions and contact details

Conducted in London on Saturdays in Sep and Oct in a conference room setting, the workshop has currently only limited places. You can also participate remotely

Please contact ajit.jaokar at futuretext.com for pricing and any other questions

Learn AI and Data Science rapidly based only on high school math

 

What if you could learn AI and Data Science based on knowledge you already know?

You have an opportunity to accelerate your learning of AI in a unique way through this limited, early bird offer

Here is a simple observation:

The mathematical foundations of Data Science rest on four elements i.e. Linear Algebra, Probability Theory, Multivariate Calculus, and Optimization theory.

 Most of these are taught (at least partially) in high schools. 

In this program, we use these maths foundations (which you learnt in High school) to teach the foundations of Data Science and Artificial Intelligence

The program is interactive and personalized for a duration of three months. It includes a certificate of completion. Coding examples are in Python (Pandas and NumPy)

The program delivery is by video. The program also includes a copy of a pdf book.

Starting on July 1, 2018. Limited places. Price 300 USD Available for a limited time only

Contact [email protected]

Created by Ajit Jaokar: Based in London, Ajit Jaokar works in Data Science and Artificial Intelligence. He teaches at Oxford University (Data Science for Internet of Things).

Image source: Shutter stock

AI labs – Ai course with hands on experience

We are launching an AI lab.

The idea is to work on an open source project in sprints and to get real life experience in AI so that we manage your learning

Note

a) This is normally in London but we can make some exceptions(online). Ideally we prefer if you can commute to London

b) This lab has a fee. This is not a basic course. It is 100 percent handson. It needs some knowledge of Python and Keras

c) This includes robotics but also more mainstream AI applications.

d) Code will always be in open source i.e. your contribution will be open sourced under Apache v2 license. .

e)  It also includes the course – outline is HERE  

If you are interested in this, please let me know (ajit.jaokar at futuretext.com)

 

 

futuretext.ai newsletter

AI / Deep Learning applications course – with hands-on experience

 

 

New workshop in London / remote

Link - Enterprise AI workshop – Sep 2018 – in London or remote

 

 

AI / Deep Learning applications course/ mentoring program – hands-on experience with limited spaces

I am pleased to announce a new course on AI Applications

The course combines elements of teaching, coaching and community. For this reason, the batch sizes are small and selective. I will be working with a small/selective group of people to actively transfer their career to AI through education and my network towards specific outcomes/goals. This course involves hands-on experience (if you are interested in this)

Early bird discounts now for a limited time ..

In a nutshell

  • The course (spanning three months) covers concepts, theory and coding for AI (in Python ie. Tensorflow and Keras) and also deployment.
  • Career guidance and mentoring
  • As a part of the mentoring process, you outline your goals and we work toward them. The mentoring may extend beyond the course.
  • You thus get access to a community and lifetime access for the content.
  • Starting June 1 2018
  • Audience: For professionals who want to learn AI concepts, applications and coding for AI (Deep Learning) from an application perspective. The course targets developers and Architects who want to transition their career to Enterprise AI. The course correlates the new AI ideas with familiar concepts like ERP, Data warehousing etc and helps to make the transition easier.
  • Pre-requisites Some basic knowledge of Python needed (or any programming language)
  • Coding using Tensorflow and Keras
  • Goals: To rapidly transition your career to AI in a supporting/mentoring/self-paced community
  • Delivery format is via video and online sessions (once every two weeks)
  • You can choose a strategic (non-coding) option also
  • Please contact [email protected] for more details and Pricing. See testimonials below
  • covers AI for IoT and Enterprise
  • course is personalized and the quiz is by milestone
  • The course involves hands-on experience. This means, you participate in an existing project in sprints. Note that this is different from the project you will do as part of the course. The hands-on project involves and open source project where you will work in sprints. The hands-on project is optional and helps you with gaining real-life experience

The course is unique in 3 ways

a)  Personalized and small numbers(I actually talk to everyone!). There is no cost for personalization
b)  Projects are by interest and vertical domain. we use a common codebase and then create small groups by interest(ex insurance, bioinformatics etc)
c)  you have lifetime access to the content

Course outline

Through the course, every participant should be able to develop a reusable codebase / library for solving problems using Tensorflow and Keras. This library then enables you to reuse the code in your applications.
The course comprises three milestones

Concepts milestone

In the Concepts milestone, we take a use case approach. We cover AI, IoT, Machine Learning, Platforms, Applications. We also discuss an overall methodology of applying AI techniques to  Enterprise and IoT problems. ,;’

Development milestone
In the development phase, we first cover the basics of development in Python (Tensorflow and Keras) for machine learning applications
We then cover three models (MLP, CNN and LSTM)
We also cover the theory of MLP, CNN and LSTM
Finally, we cover Python in more detail through development of a set of techniques for Deep learning applications
Deployment milestone
In this section, we discuss the deployment of Deep learning applications through Flask, Docker, Kubernetes and other real-world techniques

Testimonials for our courses

Jean Jacques Bernand – Paris – France

“Great course with many interactions, either group or one to one that helps in the learning. In addition, tailored curriculum to the need of each student and interaction with companies involved in this field makes it even more impactful. As for myself, it allowed me to go into topics of interests that help me in reshaping my career.”

Johnny Johnson, AT&T – USA

“This DSIOT course is a great way to get up-to-speed.  The tools and methodologies for managing devices, wrangling and fusing data, and being able to explain it are taking form fast; Ajit Jaokar is a good fit.  For me, his patience and vision keep this busy corporate family man coming back.”

Yongkang Gao, General Electric, UK.

“I especially thank Ajit for his help on my personal project of the course — recommending proper tools and introducing mentors to me, which significantly reduced my pain in the beginning stage.”

karthik padmanabhan Manager – Global Data Insight and Analytics (GDIA) – Ford Motor Pvt Ltd.

“I am delighted to provide this testimonial to Ajit Jaokar who has extended outstanding support and guidance as my mentor during the entire program on Data science for IoT. Ajit is a world renowned professional in the niche area of applying the Data science principles in creating IoT apps. Talking about the program, it has a lot of breadth and depth covering some of the cutting edge topics in the industry such as Sensor Fusion, Deep Learning oriented towards the Internet of things domain. The topics such as Statistics, Machine Learning, IoT Platforms, Big Data and more speak about the complexity of the program. This is the first of its kind program in the world to provide Data Science training especially on the IoT domain and I feel fortunate to be part of the batch comprising of participants from different countries and skill sets. Overall this journey has transformed me into a mature and confident professional in this new space and I am grateful to Ajit and his team. My wish is to see this program accepted as a gold standard in the industry in the coming years”.

 

Peter Marriott – UK – www.catalystcomputing.co.uk

Attending the Data Science for IoT course has really helped me in demystifying the tools and practices behind machine learning and has allowed me to move from an awareness of machine learning to practical application.

 

Yair Meidan Israel – https://il.linkedin.com/in/yairmeidandatamining

“As a PhD student with an academic and practical experience in analytics, the DSIOT course is the perfect means by which I extend my expertise to the domain of IoT. It gradually elaborates on IoT concepts in general, and IoT analytics in particular. I recommend it to any person interested in entering that field. Thanks Ajit!”

 

Parinya Hiranpanthaporn, Data Architect and Advanced Analytics professional Bangkok

“Good content, Good instructor and Good networking. This course totally answers what I should know about Data Science for Internet of Things.”

 

Sibanjan Das – Bangalore

Ajit helped me to focus and set goals for my career that is extremely valuable. He stands by my side for every initiative I take and helps me to navigate me through every difficult situation I face. A true leader, a technology specialist, good friend and a great mentor. Cheers!!!

 

Manuel Betancurt – Mobile developer / Electronic Engineer. – Australia

I have had the opportunity to partake in the Data Science for the IoT course taught by Ajit Jaokar. He have crafted a collection of instructional videos, code samples, projects and social interaction with him and other students of this deep knowledge.

Ajit gives an awesome introduction and description of all the tools of the trade for a data scientist getting into the IoT. Even when I really come from a software engineering background, I have found the course totally accessible and useful. The support given by Ajit to make my IoT product a data science driven reality has been invaluable. Providing direction on how to achieve my data analysis goals and even helping me to publish the results of my investigation.

The knowledge demonstrated on this course in a mathematical and computer science level has been truly exciting and encouraging. This course was the key for me to connect the little data to the big data.

 

Barend Botha – London and South Africa – http://www.sevensymbols.co.uk

This is a great course for anyone wanting to move from a development background into Data Science with specific focus on IoT. The course is unique in that it allows you to learn the theory, skills and technologies required while working on solving a specific problem of your choice, one that plays to your past strengths and interests. From my experience care is taken to give participants one to one guidance in their projects, and there is also within the course the opportunity to network and share interesting content and ideas in this growing field. Highly recommended!

- Barend Botha

 

Jamie Weisbrod – San Diego – https://www.linkedin.com/in/jamie-weisbrod-3630053

Currently there is a plethora of online courses and degrees available in data science/big data. What attracted me to joining the futuretext class “Data Science for ioT” is Ajit Jaokar. My main concern in choosing a course was how to leverage skills that I already possessed as a computer engineer. Ajit took the time to discuss how I could personalize the course for my interests.

I am currently in the midst of the basic coursework but already I have been able to network with students all over the world who are working on interesting projects. Ajit inspires a lot of people at all ages as he is also teaching young people Data science using space exploration.

 

 Robert Westwood – UK – Catalyst computing

“Ajit brings to the course years of experience in the industry and a great breadth of knowledge of the companies, people and research in the Data Science/IoT arena.”

Companies / Participants who have been part of our courses

  • Technology: GE, HPE, Oracle, TCS, Wipro, HCL, HPE, Dell, Honeywell
  • Banking and Fintech : Goldman Sachs, ABN Amro, Nordea, Santander, BNP Paribas
  • Telecoms : Nokia, AT&T, Ericsson
  • Consulting : McKinsey, PA consulting
  • Automotive : Ford, Daimler, Jaguar
  • Retail : Coca Cola, Target
  • Airlines and Aircrafts : Boeing, Airbus

(Note : Above list includes participants from companies and also companies who have sponsored their personnel)

Participant Countries

We are pleased to have participants from all over the world – leading to a vibrant and a diverse learning ecosystem. A majority of our participants are from UK USA and India. But we also have participants from the following

  • North America: USA, Canada
  • Europe:  UK and Eastern Europe:  UK, Germany, France, Belgium, Poland, Russia, Norway, Italy, Finland, Ukraine, Austria, Ireland, Spain, Estonia, Sweden, Switzerland, Russia, Holland
  • Asia:  India, Japan, Thailand, Vietnam, Singapore
  • Middle East: UAE, Egypt, Iran
  • South America: Mexico, Brazil, Colombia, Nicaragua
  • Africa: South Africa, Zimbabwe
  • Australia and NZ: Australia

 

Please contact [email protected] for more details

 

IOTA – The potential to drive Data Science for IoT

I have a close circle of clued-on/tech savvy friends whose views I take seriously. For the last few weeks, one of these friends has been sending me emails extolling the merits of something called IOTA – which calls itself as the next generation Blockchain.  At first, I thought of IOTA as yet another cryptocurrency. A whole flock of people are rebranding themselves as Bitcoin/ Blockchain / ICO experts and spamming me! So, I was initially sceptical of something that can be the ‘next generation blockchain’. But some more investigation over the holiday season has convinced me that IOTA could be a game changer. In this post, I explore the significance of IOTA and its implications for IoT analytics.

I explore such concepts in my course  Implementing Enterprise AI course using TensorFlow and Keras

I plan to also explore IOTA in my course @Oxford University Data Science for Internet of Things @Oxford University

 

What is (and what is not) IOTA

Before we proceed, this discussion is not about Bitcoin. I am not an expert on Bitcoin. For a discussion on Bitcoin, see what is bitcoin and why it matters from MIT tech review. IOTA is a cryptocurrency. But I am not an expert on cryptocurrencies either (ex the factors driving the price of the currency).  I am more interested in the problem IOTA solves and it’s disruptive potential – especially for IoT. To understand the disruptive potential of IOTA, we first need to understand what IOTA is (and what it is not).

Like blockchain, IOTA is a distributed ledger technology – but it aims to go beyond  blockchain. Bitcoin uses blockchain technology and a distributed ledger system to conduct transactions. But IOTA does not use Blockchain. Instead it uses something called the Tangle distributed Ledger which comprises of directed acyclic graphs, or DAGs. The activity of users of the system propagates the Tangle distributed ledger. This does not need fees, miners or creation of new tokens. In contrast, to propagate the Bitcoin ledger, miners need to perform computational work (or pay for previously mined Bitcoins).

Also, Bitcoin has a scalability problem because as more people start to use the system, it gets slower and it costs higher to process a transaction.  In contrast, the cost of using the IOTA ledger(Tangle) is the cost of the user’s computational effort to verify two randomly selected existing prior tangle ledger sites (transactions).

Source: IOTA-Whitepaper The main point is: “Every new transaction must approve two other transactions.” In this sense, IOTA is an attempt to potentially create a superior cryptocurrency platform by overcoming the limitations of Blockchain.

What problem does IOTA solve and why does that matter

If all IOTA created was a ‘better blockchain’ It would be interesting but not disruptive. But IOTA can be really disruptive with IoT. For example, an IoT sensor in a car could retrieve data from the factory automatically. IoT devices can connect and transact with each other in a peer to peer mode.  Potentially, it could help 50 billion devices to connect.  IOTA is getting traction(despite some recent hiccups). The IOTA Foundation announced that Robert Bosch Venture Capital GmbH (RBVC) — the corporate venture capital company of the Bosch Group — has purchased a significant number of IOTA tokens. Dr. Hongquan Jiang, partner at RBVC, will also join the IOTA Foundation’s advisory board. The core feature of IOTA is the ability ford devices  to transfer data through the Tangle. With recent extensions to the Core, IOTA can even operate in ‘one to many’ mode i.e. the ability to broadcast messages to devices.

What is the implication of IOTA for Data Science for IoT

The ability to manage and share data securely has profound implications for next generation IoT applications such as self driving cars, Drones etc. Such devices would need to collaborate within peers. A leasing model for devices could arise instead of an ownership model. That leasing/ collaboration model could also extend to data arising from IoT devices. Furthermore, interaction between devices can happen autonomously. IOTA could thus be the backbone for IoT applications.

If “Data is the next Oil” makes you cringe .. this is for you

The term ‘Data is the next Oil’ often makes me cringe .. because it’s your data and their Oil! But for a long time, there was not much you could do about it. At least for sensor data, IOTA offers a potentally disruptive way out and yet help to foster an ecosystem.  If you want to work with me on ideas such as this, I explore such concepts in my course  Implementing Enterprise AI course using TensorFlow and Keras

Comment and Disclosure

a)  The post is narrowly confined to the potential of IOTA for IoT and DataScience for IoT

b)  I do not claim any expertise or knowledge of IOTA as a cryptocurrency

c)  The cryptographic security discussions re IOTA are also not in scope (and I am not an expert on this)

d)  I do not hold any IOTA currency at the time of writing


agilePHM – a new open source product for rapid prototyping of PHM analytics

agilePHM

We are launching a new product called agilePHM

In Industrial IoT, I have been working with PHM  (Prognostics and Health Management) for a while and it is a well known discipline

Prognostics and Health management(PHM)  is an engineering discipline focused on predicting the time at which a system or a component will no longer perform its intended function. This lack of performance is most often a failure beyond which the system can no longer be used to meet desired performance. The predicted time then becomes the remaining useful life (RUL), which is an important concept in decision making for contingency mitigation. Prognostics predicts the future performance of a component by assessing the extent of deviation or degradation of a system from its expected normal operating conditions.The science of prognostics is based on the analysis of failure modes, detection of early signs of wear and aging, and fault conditions. An effective prognostics solution is implemented when there is sound knowledge of the failure mechanisms that are likely to cause the degradations leading to eventual failures in the system. (source wikiedia)

 

PHM applies to a range of domains like defence, shipping, industrial applications etc

We are developing a product for rapid prototyping of the analytics component of PHM

agiilePHM is designed for for rapid prototyping of PHM applications (implemented on standard hardware)

The need for the product

The idea of agilePHM arose due to a few observations

a)  There is a need for rapid prototyping of new ideas(from an analytics standpoint) as an exclusive function: Industrial IoT is a very new space and evolving. Ideas from different domains cross-pollinate and there is a need to quickly test out concepts(either products or processes)

b)   Data Science skills shortage: Data science skills are expensive and are often focused on industries like Banking(in contrast to Industrial IoT). So, think of agilePHM like ‘Data Scientist in a  box’ for the Industrial IoT space

c)  larger products have a much heavier footprint: Our customer is someone who wants to rapidly prototype the model (without knowing the algorithms in detail). Larger products have a much heavier footprint. Many seem like installing ERP in the old days! They perform the function of rapid prototyping as a small component (as opposed to exclusive emphasis on it)

d)  Flexibility: The approach complements existing approaches like Physics based modelling

e)  Why open source ..  Our main strength lies in IoT analytics (Ajit Jaokar teaches a course on Data Science for Internet of Things at the University of Oxford). However, the problem we address is complex because there are many processes (and many machines!) to abstract algorithmically. This needs some form of open source.

Components

agilePHM has three components

1) Digital Twin

2) Rapid prototyping

3) Workflow – Process engineering

agilePHM will have the following deployment models
On premise with support

Open source

Kaggle like contest community engagement

It also allows students in our course to gain real life / practical experience

So,

a)   If you are a company interested in working with us, please email me on ajit.jaokar at futuretext.com

b) If you are interested in gaining real experience in AI .. you can work on the product with companies as part of our course. Please contact info at futuretext.com to know more

Pleased to be in the list of top 30 influencers for #IoT for 2017 along with Amazon Bosch Cisco Forrester and Gartner ..

Pleased to be in the list of top 30 influencers for IoT for 2017 along with Amazon Bosch Cisco  Forrester and Gartner ..

About 4 years ago, when I suggested to Oxford University that we should create a course on only the Algorithmic (#Datascience and #AI) aspects of Internet of Things .. I am grateful that they accepted the obscure(and complex!) idea creating the now industry recognised Data Science for Internet of Things course

Here, we work on complex and pioneering aspects of AI, Data Science and IoT (for instance systems engineering for AI/IoT).
Special thanks to Peter Holland and Adrian Stokes at Oxford University.
The list is created by Munich Re .. one of the largest reinsurance companies and Industrial IoT companies  in the world
https://en.m.wikipedia.org/wiki/Munich_Re twitter feed @relayr_iot
Great to see IoT friends  Alexandra , Ronald Van Loon, Boris Adryan, Rob Van Kranenberg also on the list

Implementing Enterprise AI course using TensorFlow and Keras

Switch your career to Artificial Intelligence(AI) in 2018 through this unique and limited-edition course focused on AI for the Enterprise.

 

The Implementing Enterprise AI course covers

  • Design of Enterprise AI services
  • Technology foundations of Enterprise AI systems
  • Specific AI use cases
  • Development of AI services using TensorFlow and Keras
  • Deployment and Business models

We use TensorFlow and Keras. We also cover deployment models using Microservices, Docker and Kubernetes.

 

More details

  • The course targets developers and Architects who want to transition their career to Enterprise AI. The course correlates the new AI ideas with familiar concepts like ERP, Data warehousing etc and helps to make the transition easier. The course is based on a logical concept called an  ‘Enterprise AI layer’. This AI layer is focused on solving domain specific problems for an Enterprise.  We could see such a layer as an extension to the Data Warehouse or the ERP system (an Intelligent Data Warehouse/ Cognitive ERP system). Thus, the approach provides tangible and practical benefits for the Enterprise with a clear business model.
  • See link below for references from previous courses
  • Duration: Starting Jan 2018 Approximately six months (3 months for the content and up to three months for the Project)
  • Course includes a certificate of completion for projects (Projects will be created in a team and will use the code base provided in Keras)
  • The course covers the Enterprise AI Use Cases like insurance, Fraud detection, Anomaly detection, Churn, classification, Customer analytics etc
  • We also have a strategic(non-coding) based version of the course also
  • Delivery format is via video and online sessions (once every two weeks)
  • For pricing please contact us below

Please contact us to sign up or to know more [email protected]

Testimonials for our courses

 

 Jean Jacques Bernand – Paris – France

“Great course with many interactions, either group or one to one that helps in the learning. In addition, tailored curriculum to the need of each student and interaction with companies involved in this field makes it even more impactful.

As for myself, it allowed me to go into topics of interests that help me in reshaping my career.”

 

Johnny Johnson, AT&T – USA

“This DSIOT course is a great way to get up-to-speed.  The tools and methodologies for managing devices, wrangling and fusing data, and being able to explain it are taking form fast; Ajit Jaokar is a good fit.  For me, his patience and vision keep this busy corporate family man coming back.”

 

Yongkang Gao, General Electric, UK.

“I especially thank Ajit for his help on my personal project of the course — recommending proper tools and introducing mentors to me, which significantly reduced my pain in the beginning stage.”

 

karthik padmanabhan Manager – Global Data Insight and Analytics (GDIA) – Ford Motor Pvt Ltd.

“I am delighted to provide this testimonial to Ajit Jaokar who has extended outstanding support and guidance as my mentor during the entire program on Data science for IoT. Ajit is a world renowned professional in the niche area of applying the Data science principles in creating IoT apps. Talking about the program, it has a lot of breadth and depth covering some of the cutting edge topics in the industry such as Sensor Fusion, Deep Learning oriented towards the Internet of things domain. The topics such as Statistics, Machine Learning, IoT Platforms, Big Data and more speak about the complexity of the program. This is the first of its kind program in the world to provide Data Science training especially on the IoT domain and I feel fortunate to be part of the batch comprising of participants from different countries and skill sets. Overall this journey has transformed me into a mature and confident professional in this new space and I am grateful to Ajit and his team. My wish is to see this program accepted as a gold standard in the industry in the coming years”.

 

Peter Marriott – UK – www.catalystcomputing.co.uk

Attending the Data Science for IoT course has really helped me in demystifying the tools and practices behind machine learning and has allowed me to move from an awareness of machine learning to practical application.

 

Yair Meidan Israel – https://il.linkedin.com/in/yairmeidandatamining

“As a PhD student with an academic and practical experience in analytics, the DSIOT course is the perfect means by which I extend my expertise to the domain of IoT. It gradually elaborates on IoT concepts in general, and IoT analytics in particular. I recommend it to any person interested in entering that field. Thanks Ajit!”

 

Parinya Hiranpanthaporn, Data Architect and Advanced Analytics professional Bangkok

“Good content, Good instructor and Good networking. This course totally answers what I should know about Data Science for Internet of Things.”

 

Sibanjan Das – Bangalore

Ajit helped me to focus and set goals for my career that is extremely valuable. He stands by my side for every initiative I take and helps me to navigate me through every difficult situation I face. A true leader, a technology specialist, good friend and a great mentor. Cheers!!!

 

Manuel Betancurt – Mobile developer / Electronic Engineer. – Australia

I have had the opportunity to partake in the Data Science for the IoT course taught by Ajit Jaokar. He have crafted a collection of instructional videos, code samples, projects and social interaction with him and other students of this deep knowledge.

Ajit gives an awesome introduction and description of all the tools of the trade for a data scientist getting into the IoT. Even when I really come from a software engineering background, I have found the course totally accessible and useful. The support given by Ajit to make my IoT product a data science driven reality has been invaluable. Providing direction on how to achieve my data analysis goals and even helping me to publish the results of my investigation.

The knowledge demonstrated on this course in a mathematical and computer science level has been truly exciting and encouraging. This course was the key for me to connect the little data to the big data.

 

Barend Botha – London and South Africa – http://www.sevensymbols.co.uk

This is a great course for anyone wanting to move from a development background into Data Science with specific focus on IoT. The course is unique in that it allows you to learn the theory, skills and technologies required while working on solving a specific problem of your choice, one that plays to your past strengths and interests. From my experience care is taken to give participants one to one guidance in their projects, and there is also within the course the opportunity to network and share interesting content and ideas in this growing field. Highly recommended!

- Barend Botha

 

Jamie Weisbrod – San Diego - https://www.linkedin.com/in/jamie-weisbrod-3630053

Currently there is a plethora of online courses and degrees available in data science/big data. What attracted me to joining the futuretext class “Data Science for ioT” is Ajit Jaokar. My main concern in choosing a course was how to leverage skills that I already possessed as a computer engineer. Ajit took the time to discuss how I could personalize the course for my interests.

I am currently in the midst of the basic coursework but already I have been able to network with students all over the world who are working on interesting projects. Ajit inspires a lot of people at all ages as he is also teaching young people Data science using space exploration.

 

 Robert Westwood – UK – Catalyst computing
“Ajit brings to the course years of experience in the industry and a great breadth of knowledge of the companies, people and research in the Data Science/IoT arena.”

Companies / Participants who have been part of the course

 

Tech: GE, HPE, Oracle, TCS, Wipro, HCL, HPE, Dell, Honeywell

Banking and Fintech : Goldman Sachs, ABN Amro, Nordea, Santander, BNP Paribas

Telecoms : Nokia, AT&T, Ericsson

Consulting : McKinsey, PA consulting

Automotive : Ford, Daimler, Jaguar

Retail : Coca Cola, Target

Airlines and Aircrafts : Boeing, Airbus

(Note : Above list includes participants from companies and also companies who have sponsored their personnel)

Participant Countries

We are pleased to have participants from all over the world – leading to a vibrant and a diverse learning ecosystem. A majority of our participants are from UK USA and India. But we also have participants from the following

North America: USA, Canada

Europe:  UK and Eastern Europe:  UK, Germany, France, Belgium, Poland, Russia, Norway, Italy, Finland, Ukraine, Austria, Ireland, Spain, Estonia, Sweden, Switzerland, Russia, Holland

Asia:  India, Japan, Thailand, Vietnam, Singapore

Middle East: UAE, Egypt, Iran

South America: Mexico, Brazil, Colombia, Nicaragua

Africa: South Africa, Zimbabwe

Australia and NZ: Australia

 

Contact

info at futuretext dot com

Timeline and Course outline

The course has three phases: Foundations, Development and Deployment. The projects are included in the deployment phase(in groups)

The Quiz is mostly in coding exercises (Unless you choose the strategic option)

Foundations (Jan – Feb)

  • The foundations of Data Science for the Enterprise with an emphasis on emerging fields like IoT and fintech.
  • A methodology for solving AI problems for the Enterprise
  • Foundations of Python
  • Tensorflow and Keras introduction
  • An end to end application (in code) for implementing Data Science for Enterprise and IoT
  • Understanding AI and Deep Learning

Development (March – April – May)

  • Machine learning implementation in detail
  • Understanding of Deep Learning concepts and implementation
  • Algorithms (ML and DL) : Multilayer Perceptron, Auto encoders, Deep Convolutional Networks, Recurrent Neural Networks, Reinforcement learning, Natural language processing
  • Implementations covering both Time series and Image
  • Unique considerations for Enterprise AI problems
  • Unique considerations for IoT (In this section, we consider the deployment models for IoT applications both for consumer and Industrial IoT – these include Edge, Complex event processing etc)
  • Considerations for specific industry verticals - insurance, Fraud detection, Anomaly detection, Churn, classification, Customer analytics etc

Deployment (June – July)

  •  A systems thinking approach for deploying complex applications
  • Understanding Docker, Microservices, Kubernetes and their role in the deployment cycle
  • Projects(in groups in a sprint / agile cycle)

Contact

info at futuretext dot com