Machine Learning Training Classes in Yonkers, New York

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

We offer private customized training for groups of 3 or more attendees.
Yonkers  Upcoming Instructor Led Online and Public Machine Learning Training Classes
Python for Data Scientist and Machine Learning Practitioners Training/Class 16 March, 2020 - 20 March, 2020 $2090
HSG Training Center
Yonkers, New York
Hartmann Software Group Training Registration

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Blog Entries publications that: entertain, make you think, offer insight

From Brennan's Blog which is no longer up and running:

I use Remote Desktop all the time to work inside of my development systems hosted by Microsoft Virtual Server. I use the host system to browse the web for documentation and searches as I work and when I need to copy some text from the web browser I find many times the link between the host clipboard and the remote clipboard is broken. In the past I have read that somehow the remote clipboard utility, rdpclip.exe, gets locked and no longer allows the clipboard to be relayed between the host and the client environment. My only way to deal with it was to use the internet clipboard, cl1p.net. I would create my own space and use it to send content between environments. But that is a cumbersome step if you are doing it frequently.

The only way I really knew to fix the clipboard transfer was to close my session and restart it. That meant closing the tools I was using like Visual Studio, Management Studio and the other ancillary processes I have running as I work and then restarting all of it just to restore the clipboard. But today I found a good link on the Terminal Services Blog explaining that what is really happening. The clipboard viewer chain is somehow becoming unresponsive on the local or remote system and events on the clipboards are not being relayed between systems. It is not necessarily a lock being put in place but some sort of failed data transmission. It then goes on to explain the 2 steps you can take to restore the clipboard without restarting your session.

  • Use Task Manager to kill the rdpclip.exe process
  • Run rdpclip.exe to restart it

The clipboard communications should be restored. My clipboard is currently working because I just restarted my session to fix it, but I wanted to test these steps. I killed rdpclip.exe and started it and was able to copy/paste from the remote to the host system. The next time my clipboard dies I will have to check to see if these steps truly do work.

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.

A business rule is the basic unit of rule processing in a Business Rule Management System (BRMS) and, as such, requires a fundamental understanding. Rules consist of a set of actions and a set of conditions whereby actions are the consequences of each condition statement being satisfied or true. With rare exception, conditions test the property values of objects taken from an object model which itself is gleaned from a Data Dictionary and UML diagrams. See my article on Data Dictionaries for a better understanding on this subject matter.

A simple rule takes the form:

if condition(s)

then actions.

An alternative form includes an else statement where alternate actions are executed in the event that the conditions in the if statement are not satisfied:

if condition(s)

then actions

else alternate_actions

It is not considered a best prectice to write rules via nested if-then-else statements as they tend to be difficult to understand, hard to maintain and even harder to extend as the depth of these statements increases; in other words, adding if statements within a then clause makes it especially hard to determine which if statement was executed when looking at a bucket of rules. Moreoever, how can we determine whether the if or the else statement was satisfied without having to read the rule itself. Rules such as these are often organized into simple rule statements and provided with a name so that when reviewing rule execution logs one can determine which rule fired and not worry about whether the if or else statement was satisfied. Another limitation of this type of rule processing is that it does not take full advantage of rule inferencing and may have a negative performance impact on the Rete engine execution. Take a class with HSG and find out why.

Rule Conditions

Although reports made in May 2010 indicate that Android had outsold Apple iPhones, more recent and current reports of the 2nd quarter of 2011 made by National Purchase Diary (NPD) on Mobile Phone Track service, which listed the top five selling smartphones in the United States for the months of April-June of 2011, indicate that Apple's iPhone 4 and iPhone 3GS outsold other Android phones on the market in the U. S. for the third calendar quarter of 2011. This was true for the previous quarter of the same year; The iPhone 4 held the top spot.  The fact that the iPhone 4 claimed top spot does not come as a surprise to the analysts; rather, it is a testament to them of how well the iPhone is revered among consumers. The iPhone 3GS, which came out in 2009 outsold newer Android phones with higher screen resolutions and more processing power. The list of the five top selling smartphones is depicted below:

  1. Apple iPhone 4
  2. Apple iPhone 3GS
  3. HTC EVO 4G
  4. Motorola Droid 3
  5. Samsung Intensity II[1]

Apple’s iPhone also outsold Android devices7.8:1 at AT&T’s corporate retail stores in December. A source inside the Apple company told The Mac Observer that those stores sold some 981,000 iPhones between December 1st and December 27th 2011, and that the Apple device accounted for some 66% of all device sales during that period (see the pie figure below) . Android devices, on the other hand, accounted for just 8.5% of sales during the same period.

According to the report, AT&T sold approximately 981,000 iPhones through AT&T corporate stores in the first 27 days of December, 2011 while 126,000 Android devices were sold during the same period. Even the basic flip and slider phones did better than Android, with 128,000 units sold.[2] However, it is important to understand that this is a report for one particular environment at a particular period in time. As the first iPhone carrier in the world, AT&T has been the dominant iPhone carrier in the U.S. since day one, and AT&T has consistently claimed that the iPhone is its best selling device.

Chart courtesy of Mac Observer: http://www.macobserver.com/tmo/article/iphone_crushes_android_at_att_corporate_stores_in_december/

A more recent report posted in ismashphone.com, dated January 25 2012, indicated that Apple sold 37 million iPhones in Q4 2011.  It appears that the iPhone 4S really helped take Apple’s handset past competing Android phones. According to research firm Kantar Worldpanel ComTech, Apple’s U.S. smartphone marketshare has doubled to 44.9 percent.[3] Meanwhile, Android marketshare in the U.S. dropped slightly to 44.8 percent. This report means that the iPhone has edged just a little bit past Android in U.S. marketshare. This is occurred after Apple’s Q1 2012 conference call, which saw themselling 37 million handsets. Meanwhile, it’s reported that marketers of Android devices, such as Motorola Mobility, HTC and Sony Ericsson saw drops this quarter.

Tech Life in New York

City The Big Apple is home of two of the world?s largest stock market exchanges, the New York Stock Exchange and NASDAQ. As a leading business center in the United States, New York has more Fortune 500 headquartered companies than any other city. Technology is blossoming in the Big Apple as major internet conglomerates like Google move their offices into ?telecom hotels? such as the 311,000 square feet office space downtown. As in any other city there are pros and cons of living in New York City. For instance, there is so much to do, it?s easy to get around with the transit system, it?s safe, convenient, and has plenty of job opportunities. On the other hand, it can be overwhelmingly expensive, overcrowded, a bit impersonal and fast paced. New Yorkers enjoy Central Park, multi cultural activities and food, theatre, film festivals, farmers markets, fashion and anything else they could possibly think of...it?s all there.
Learning how to learn is life's most important skill. Anonymous
other Learning Options
Software developers near Yonkers 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 New York that offer opportunities for Machine Learning developers
Company Name City Industry Secondary Industry
NYSE Euronext, Inc. New York Financial Services Securities Agents and Brokers
Anderson Instrument Company Inc. Fultonville Manufacturing Tools, Hardware and Light Machinery
News Corporation New York Media and Entertainment Radio and Television Broadcasting
Philip Morris International Inc New York Manufacturing Manufacturing Other
Loews Corporation New York Travel, Recreation and Leisure Hotels, Motels and Lodging
The Guardian Life Insurance Company of America New York Financial Services Insurance and Risk Management
Jarden Corporation Rye Manufacturing Manufacturing Other
Ralph Lauren Corporation New York Retail Clothing and Shoes Stores
Icahn Enterprises, LP New York Financial Services Investment Banking and Venture Capital
Viacom Inc. New York Media and Entertainment Media and Entertainment Other
Omnicom Group Inc. New York Business Services Advertising, Marketing and PR
Henry Schein, Inc. Melville Healthcare, Pharmaceuticals and Biotech Medical Supplies and Equipment
Pfizer Incorporated New York Healthcare, Pharmaceuticals and Biotech Pharmaceuticals
Eastman Kodak Company Rochester Computers and Electronics Audio, Video and Photography
Assurant Inc. New York Business Services Data and Records Management
PepsiCo, Inc. Purchase Manufacturing Nonalcoholic Beverages
Foot Locker, Inc. New York Retail Department Stores
Barnes and Noble, Inc. New York Retail Sporting Goods, Hobby, Book, and Music Stores
Alcoa New York Manufacturing Metals Manufacturing
The Estee Lauder Companies Inc. New York Healthcare, Pharmaceuticals and Biotech Personal Health Care Products
Avon Products, Inc. New York Healthcare, Pharmaceuticals and Biotech Personal Health Care Products
The Bank of New York Mellon Corporation New York Financial Services Banks
Marsh and McLennan Companies New York Financial Services Insurance and Risk Management
Corning Incorporated Corning Manufacturing Concrete, Glass, and Building Materials
CBS Corporation New York Media and Entertainment Radio and Television Broadcasting
Bristol Myers Squibb Company New York Healthcare, Pharmaceuticals and Biotech Biotechnology
Citigroup Incorporated New York Financial Services Banks
Goldman Sachs New York Financial Services Personal Financial Planning and Private Banking
American International Group (AIG) New York Financial Services Insurance and Risk Management
Interpublic Group of Companies, Inc. New York Business Services Advertising, Marketing and PR
BlackRock, Inc. New York Financial Services Securities Agents and Brokers
MetLife Inc. New York Financial Services Insurance and Risk Management
Consolidated Edison Company Of New York, Inc. New York Energy and Utilities Gas and Electric Utilities
Time Warner Cable New York Telecommunications Cable Television Providers
Morgan Stanley New York Financial Services Investment Banking and Venture Capital
American Express Company New York Financial Services Credit Cards and Related Services
International Business Machines Corporation Armonk Computers and Electronics Computers, Parts and Repair
TIAA-CREF New York Financial Services Securities Agents and Brokers
JPMorgan Chase and Co. New York Financial Services Investment Banking and Venture Capital
The McGraw-Hill Companies, Inc. New York Media and Entertainment Newspapers, Books and Periodicals
L-3 Communications Inc. New York Manufacturing Aerospace and Defense
Colgate-Palmolive Company New York Consumer Services Personal Care
New York Life Insurance Company New York Financial Services Insurance and Risk Management
Time Warner Inc. New York Media and Entertainment Media and Entertainment Other
Cablevision Systems Corp. Bethpage Media and Entertainment Radio and Television Broadcasting
CA Technologies, Inc. Islandia Software and Internet Software
Verizon Communications Inc. New York Telecommunications Telephone Service Providers and Carriers
Hess Corporation New York Energy and Utilities Gasoline and Oil Refineries

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 New York 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.