Machine Learning Training Classes in Hartford, Connecticut

Learn Machine Learning in Hartford, Connecticut and surrounding areas via our hands-on, expert led courses. All of our classes are offered on an onsite, online and public instructor led basis. Here is a list of our current Machine Learning related training offerings in Hartford, Connecticut: Machine Learning Training

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Hartford  Upcoming Instructor Led Online and Public Machine Learning Training Classes
Python for Data Scientist and Machine Learning Practitioners Training/Class 28 October, 2019 - 1 November, 2019 $2090
HSG Training Center
Hartford, Connecticut
Hartmann Software Group Training Registration

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The Zen of Python, by Tim Peters has been adopted by many as a model summary manual of python's philosophy.  Though these statements should be considered more as guideline and not mandatory rules, developers worldwide find the poem to be on a solid guiding ground.


Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!

What are the three most important things non-programmers should know about programming?
 
Written by Brian Knapp, credit and reprint CodeCareerGenius
 
 
Since you asked for the three most important things that non-programmers should know about, and I’ve spent most of my career working with more non-programmers than programmers, I have a few interesting things that would help.
 
Number One - It Is Impossible To Accurately Estimate Software Projects
 
No matter what is tried. No matter what tool, agile approach, or magic fairy dust people try to apply to creating software… accurately predicting software project timelines is basically impossible.
 
There are many good reasons for this. Usually, requirements and feature ideas change on a daily/weekly basis. Often it is impossible to know what needs to be done without actually digging into the code itself. Debugging and QA can take an extraordinary amount of time.
 
And worst of all…
 
Project Managers are always pushing for shorter timelines. They largely have no respect for reality. So, at some point they are given estimates just to make them feel better about planning.
 
No matter how much planning and estimation you do, it will be wrong. At best it will be directionally correct +/- 300% of what you estimated. So, a one year project could actually take anywhere between 0 and 5 years, maybe even 10 years.
 
If you think I’m joking, look at how many major ERP projects that go over time and over budget by many years and many hundreds of millions of dollars. Look at the F-35 fighter jet software issues.
 
Or in the small, you can find many cases where a “simple bug fix” can take days when you thought it was hours.
 
All estimates are lies made up to make everyone feel better. I’ve never met a developer or manager who could accurately estimate software projects even as well as the local weatherman(or woman) predicts the weather.
 
Number Two - Productivity Is Unevenly Distributed
 
What if I told you that in the average eight hour work day the majority of the work will get done in a 30 minute timeframe? Sound crazy?
 
Well, for most programmers there is a 30–90 minute window where you are extraordinarily productive. We call this the flow state.
 
Being in the flow state is wonderful and amazing. It often is where the “magic” of building software happens.
 
Getting into flow can be difficult. It’s akin to meditation in that you have to have a period of uninterrupted focus of say 30 minutes to “get in” the flow, but a tiny interruption can pull you right out.
 
Now consider the modern workplace environment. Programmers work in open office environments where they are invited to distract each other constantly.
 
Most people need a 1–2 hour uninterrupted block to get 30–90 minutes of flow.
 
Take the 8 hour day and break it in half with a lunch break, and then pile in a few meetings and all of a sudden you are lucky to get one decent flow state session in place.
 
That is why I say that most of the work that gets done happens in a 30 minute timeframe. The other 7–8 hours are spent being distracted, answering email, going to meetings, hanging around the water cooler, going to the bathroom, and trying to remember what you were working on before all these distractions.
 
Ironically, writers, musicians, and other creative professionals have their own version of this problem and largely work alone and away from other people when they are creating new things.
 
Someday the programming world might catch on, but I doubt it.
 
Even if this became obvious, it doesn’t sit well with most companies to think that programmers would be paid for an 8 hour day and only be cranking out code for a few hours on a good day. Some corporate middle manager would probably get the bright idea to have mandatory flow state training where a guru came in and then there would be a corporate policy from a pointy haired boss mandating that programmers are now required to spend 8 hours a day in flow state and they must fill out forms to track their time and notify their superiors of their flow state activities, otherwise there would be more meetings about the current flow state reports not being filed correctly and that programmers were spending too much time “zoning out” instead of being in flow.
 
Thus, programmers would spent 7–8 hours a day pretending to be in flow state, reporting on their progress, and getting all their work done in 30 minutes of accidental flow state somewhere in the middle of all that flow state reporting.
 
If you think I’m joking about this, I’m not. I promise you this is what would happen to any company of more than 2 employees. (Even the ones run by programmers.)
 
Number Three - It Will Cost 10x What You Think
 
Being a programmer, I get a lot of non-programmers telling me about their brilliant app ideas. Usually they want me to build something for free and are so generous as to pay me up to 5% of the profits for doing 100% of the work.
 
Their ideas are just that good.
 
Now, I gently tell them that I’m not interested in building anything for free.
 
At that point they get angry, but a few ask how much it will cost. I give them a reasonable (and very incorrect) estimate of what it would cost to create the incredibly simple version of their app idea.
 
Let’s say it’s some number like $25,000.
 
They look at me like I’m a lunatic, and so I explain how much it costs to hire a contract programmer and how long it will actually take. For example’s sake let’s say it is $100/hr for 250 hours.
 
To be clear, these are made up numbers and bad estimates (See Number One for details…)
 
In actuality, to build the actual thing they want might cost $250,000, or even $2,500,000 when it’s all said and done.
 
Building software can be incredibly complex and expensive. What most people can’t wrap their head around is the fact that a company like Google, Apple, or Microsoft has spent BILLIONS of dollars to create something that looks so simple to the end user.
 
Somehow, the assumption is that something that looks simple is cheap and fast to build.
 
Building something simple and easy for the end user is time consuming and expensive. Most people just can’t do it.
 
So, the average person with a brilliant app idea thinks it will cost a few hundred or maybe a few thousand dollars to make and it will be done in a weekend is so off the mark it’s not worth considering their ideas.
 
And programmers are too eager to play along with these bad ideas (by making bad estimates and under charging for their time) that this notion is perpetuated to the average non-programmer.
 
So, a good rule of thumb is that software will cost 10 times as much as you think and take 10 times as long to finish.
 
And that leads to a bonus point…
 
BONUS - Software Is Never Done
 
Programmers never complete a software project, they only stop working on it. Software is never done.
 
I’ve worked at many software companies and I’ve never seen a software project “completed”.
 
Sure, software gets released and used. But, it is always changing, being updated, bugs get fixed, and there are always new customer requests for features.
 
Look at your favorite software and you’ll quickly realize how true this is. Facebook, Instagram, Google Search, Google Maps, GMail, iOS, Android, Windows, and now even most video games are never done.
 
There are small armies of developers just trying to keep all the software you use every day stable and bug free. Add on the fact that there are always feature requests, small changes, and new platforms to deal with, it’s a treadmill.
 
So, the only way out of the game is to stop working on software. At that point, the software begins to decay until it is no longer secure or supported.
 
Think about old Windows 3.1 software or maybe old Nintendo Cartridge video games. The current computers and video game consoles don’t even attempt to run that software anymore.
 
You can’t put an old video game in your new Nintendo Switch and have it “just work”. That is what happens when you think software is done.
 
When programmers stop working on software the software starts to die. The code itself is probably fine, but all the other software keeps moving forward until your software is no longer compatible with the current technology.
 
So, those are the four most important things that non-programmers should know about programming. I know you asked for only three, so I hope the bonus was valuable to you as well.

If you’re interested in building modern websites or applications which use ASP, XML, or mobile technology, you’ve heard of Visual Studio .NET.  It is one of the more popular suites of development tools available to aspiring programmers, as it consolidates several different tools and languages into the same development environment, which helps in turn to integrate this code across development languages.  Here are three important benefits to using the visual studio suite:

·         Use of Visual J# - This development tool is specifically oriented towards people who already are familiar with basic Java syntax, and is designed for use by those people to build apps or services which will then run on the Microsoft .NET Framework.  This is useful because it fully supports Microsoft Extensions, among other reasons.  Visual J# was developed completely independently by Microsoft.

·         Utility for Smart Devices – Another huge benefit of using visual studio .NET is the ability to immediately integrate your programming efforts with deployment across a variety of smart devices.  PDAs, smartphones, Pocket PCs, and any device which has a limited amount of resources all require a compact framework for the programming of applications it is designed to run.

·         XML Web Usage and Support – Because XML services aren’t married to any particular technology or programming language, they can be accessed by any system, and this broad-based utility has made the services increasingly popular.  Visual Studio .NET takes advantage of this by fully integrating with XML services, including the ability to create and edit them from scratch.

Machine learning systems are equipped with artificial intelligence engines that provide these systems with the capability of learning by themselves without having to write programs to do so. They adjust and change programs as a result of being exposed to big data sets. The process of doing so is similar to the data mining concept where the data set is searched for patterns. The difference is in how those patterns are used. Data mining's purpose is to enhance human comprehension and understanding. Machine learning's algorithms purpose is to adjust some program's action without human supervision, learning from past searches and also continuously forward as it's exposed to new data.

The News Feed service in Facebook is an example, automatically personalizing a user's feed from his interaction with his or her friend's posts. The "machine" uses statistical and predictive analysis that identify interaction patterns (skipped, like, read, comment) and uses the results to adjust the News Feed output continuously without human intervention. 

Impact on Existing and Emerging Markets

The NBA is using machine analytics created by a California-based startup to create predictive models that allow coaches to better discern a player's ability. Fed with many seasons of data, the machine can make predictions of a player's abilities. Players can have good days and bad days, get sick or lose motivation, but over time a good player will be good and a bad player can be spotted. By examining big data sets of individual performance over many seasons, the machine develops predictive models that feed into the coach’s decision-making process when faced with certain teams or particular situations. 

General Electric, who has been around for 119 years is spending millions of dollars in artificial intelligence learning systems. Its many years of data from oil exploration and jet engine research is being fed to an IBM-developed system to reduce maintenance costs, optimize performance and anticipate breakdowns.

Over a dozen banks in Europe replaced their human-based statistical modeling processes with machines. The new engines create recommendations for low-profit customers such as retail clients, small and medium-sized companies. The lower-cost, faster results approach allows the bank to create micro-target models for forecasting service cancellations and loan defaults and then how to act under those potential situations. As a result of these new models and inputs into decision making some banks have experienced new product sales increases of 10 percent, lower capital expenses and increased collections by 20 percent. 

Emerging markets and industries

By now we have seen how cell phones and emerging and developing economies go together. This relationship has generated big data sets that hold information about behaviors and mobility patterns. Machine learning examines and analyzes the data to extract information in usage patterns for these new and little understood emergent economies. Both private and public policymakers can use this information to assess technology-based programs proposed by public officials and technology companies can use it to focus on developing personalized services and investment decisions.

Machine learning service providers targeting emerging economies in this example focus on evaluating demographic and socio-economic indicators and its impact on the way people use mobile technologies. The socioeconomic status of an individual or a population can be used to understand its access and expectations on education, housing, health and vital utilities such as water and electricity. Predictive models can then be created around customer's purchasing power and marketing campaigns created to offer new products. Instead of relying exclusively on phone interviews, focus groups or other kinds of person-to-person interactions, auto-learning algorithms can also be applied to the huge amounts of data collected by other entities such as Google and Facebook.

A warning

Traditional industries trying to profit from emerging markets will see a slowdown unless they adapt to new competitive forces unleashed in part by new technologies such as artificial intelligence that offer unprecedented capabilities at a lower entry and support cost than before. But small high-tech based companies are introducing new flexible, adaptable business models more suitable to new high-risk markets. Digital platforms rely on algorithms to host at a low cost and with quality services thousands of small and mid-size enterprises in countries such as China, India, Central America and Asia. These collaborations based on new technologies and tools gives the emerging market enterprises the reach and resources needed to challenge traditional business model companies.

Tech Life in Connecticut

Software developers in Hartford, Fairfield, New Haven, Greenwich and New Britain are rich in Fortune 1000 companies such as the Xerox Corporation, CIGNA, Aetna, and United Technologies Corporation just to name a few. A fun fact: Hartford has the oldest U.S. newspaper still being published?the Hartford Courant, established 1764. Connecticut is also the insurance capital of the nation.
Team development is like a birthday cake. Everybody gets a piece. Assaad Chalhoub
other Learning Options
Software developers near Hartford have ample opportunities to meet like minded techie individuals, collaborate and expend their career choices by participating in Meet-Up Groups. The following is a list of Technology Groups in the area.
Fortune 500 and 1000 companies in Connecticut that offer opportunities for Machine Learning developers
Company Name City Industry Secondary Industry
Stanley Black and Decker, Inc. New Britain Manufacturing Tools, Hardware and Light Machinery
EMCOR Group, Inc. Norwalk Energy and Utilities Energy and Utilities Other
The Hartford Financial Services Group Inc. Hartford Financial Services Insurance and Risk Management
Crane Co. Stamford Manufacturing Tools, Hardware and Light Machinery
Cenveo. Inc. Stamford Business Services Business Services Other
Amphenol Corporation Wallingford Computers and Electronics Semiconductor and Microchip Manufacturing
W. R. Berkley Corporation Greenwich Financial Services Insurance and Risk Management
Silgan Holdings Inc. Stamford Manufacturing Manufacturing Other
Hubbell Incorporated Shelton Manufacturing Concrete, Glass, and Building Materials
IMS Health Incorporated Danbury Business Services Management Consulting
CIGNA Corporation Hartford Financial Services Insurance and Risk Management
Chemtura Corp. Middlebury Manufacturing Chemicals and Petrochemicals
Harman International Industries, Inc Stamford Computers and Electronics Audio, Video and Photography
United Rentals, Inc. Greenwich Real Estate and Construction Construction Equipment and Supplies
The Phoenix Companies, Inc. Hartford Financial Services Investment Banking and Venture Capital
Magellan Health Services, Inc. Avon Healthcare, Pharmaceuticals and Biotech Healthcare, Pharmaceuticals, and Biotech Other
Terex Corporation Westport Manufacturing Heavy Machinery
Praxair, Inc. Danbury Manufacturing Chemicals and Petrochemicals
Knights of Columbus New Haven Non-Profit Social and Membership Organizations
Xerox Corporation Norwalk Computers and Electronics Office Machinery and Equipment
Starwood Hotels and Resorts Worldwide, Inc. Stamford Travel, Recreation and Leisure Hotels, Motels and Lodging
United Technologies Corporation Hartford Manufacturing Aerospace and Defense
General Electric Company Fairfield Computers and Electronics Consumer Electronics, Parts and Repair
Pitney Bowes, Inc. Stamford Manufacturing Tools, Hardware and Light Machinery
Charter Communications, Inc. Stamford Telecommunications Cable Television Providers
Aetna Inc. Hartford Financial Services Insurance and Risk Management
Priceline.com Norwalk Travel, Recreation and Leisure Travel, Recreation, and Leisure Other

training details locations, tags and why hsg

A successful career as a software developer or other IT professional requires a solid understanding of software development processes, design patterns, enterprise application architectures, web services, security, networking and much more. The progression from novice to expert can be a daunting endeavor; this is especially true when traversing the learning curve without expert guidance. A common experience is that too much time and money is wasted on a career plan or application due to misinformation.

The Hartmann Software Group understands these issues and addresses them and others during any training engagement. Although no IT educational institution can guarantee career or application development success, HSG can get you closer to your goals at a far faster rate than self paced learning and, arguably, than the competition. Here are the reasons why we are so successful at teaching:

  • Learn from the experts.
    1. We have provided software development and other IT related training to many major corporations in Connecticut since 2002.
    2. Our educators have years of consulting and training experience; moreover, we require each trainer to have cross-discipline expertise i.e. be Java and .NET experts so that you get a broad understanding of how industry wide experts work and think.
  • Discover tips and tricks about Machine Learning programming
  • Get your questions answered by easy to follow, organized Machine Learning experts
  • Get up to speed with vital Machine Learning programming tools
  • Save on travel expenses by learning right from your desk or home office. Enroll in an online instructor led class. Nearly all of our classes are offered in this way.
  • Prepare to hit the ground running for a new job or a new position
  • See the big picture and have the instructor fill in the gaps
  • We teach with sophisticated learning tools and provide excellent supporting course material
  • Books and course material are provided in advance
  • Get a book of your choice from the HSG Store as a gift from us when you register for a class
  • Gain a lot of practical skills in a short amount of time
  • We teach what we know…software
  • We care…
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Interesting Reads Take a class with us and receive a book of your choosing for 50% off MSRP.