Machine Learning Training Classes in Lakeville, Minnesota
Learn Machine Learning in Lakeville, Minnesota 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 Lakeville, Minnesota: Machine Learning Training
Machine Learning Training Catalog
Business Analysis Classes
Python Programming Classes
Course Directory [training on all levels]
- .NET Classes
- Agile/Scrum Classes
- Ajax Classes
- Android and iPhone Programming Classes
- Blaze Advisor Classes
- C Programming Classes
- C# Programming Classes
- C++ Programming Classes
- Cisco Classes
- Cloud Classes
- CompTIA Classes
- Crystal Reports Classes
- Design Patterns Classes
- DevOps Classes
- Foundations of Web Design & Web Authoring Classes
- Git, Jira, Wicket, Gradle, Tableau Classes
- IBM Classes
- Java Programming Classes
- JBoss Administration Classes
- JUnit, TDD, CPTC, Web Penetration Classes
- Linux Unix Classes
- Machine Learning Classes
- Microsoft Classes
- Microsoft Development Classes
- Microsoft SQL Server Classes
- Microsoft Team Foundation Server Classes
- Microsoft Windows Server Classes
- Oracle, MySQL, Cassandra, Hadoop Database Classes
- Perl Programming Classes
- Python Programming Classes
- Ruby Programming Classes
- Security Classes
- SharePoint Classes
- SOA Classes
- Tcl, Awk, Bash, Shell Classes
- UML Classes
- VMWare Classes
- Web Development Classes
- Web Services Classes
- Weblogic Administration Classes
- XML Classes
- Introduction to Spring 5, Spring Boot, and Spring REST (2022)
20 November, 2023 - 24 November, 2023 - Beginning Python
16 October, 2023 - 18 October, 2023 - 20483: Programming in C#
4 December, 2023 - 8 December, 2023 - LINUX SHELL SCRIPTING
6 November, 2023 - 7 November, 2023 - GLUSTERFS STORAGE ADMINISTRATION
9 October, 2023 - 11 October, 2023 - See our complete public course listing
Blog Entries publications that: entertain, make you think, offer insight
The interpreted programming language Python has surged in popularity in recent years. Long beloved by system administrators and others who had good use for the way it made routine tasks easy to automate, it has gained traction in other sectors as well. In particular, it has become one of the most-used tools in the discipline of numerical computing and analysis. Being put to use for such heavy lifting has endowed the language with a great selection of powerful libraries and other tools that make it even more flexible. One upshot of this development has been that sophisticated business analysts have also come to see the language as a valuable tool for those own data analysis needs.
Greatly appreciated for its simplicity and elegance of syntax, Python makes an excellent first programming language for previously non-technical people. Many business analysts, in fact, have had success growing their skill sets in this way thanks to the language's tractability. Long beloved by specialized data scientists, the iPython interactive computing environment has also attracted great attention within the business analyst’s community. Its instant feedback and visualization options have made it easy for many analysts to become skilled Python programmers while doing valuable work along the way.
Using iPython and appropriate notebooks for it, for example, business analysts can easily make interactive use of such tools as cohort analysis and pivot tables. iPython makes it easy to benefit from real-time, interactive researches which produce immediately visible results, including charts and graphs suitable for use in other contexts. Through becoming familiar with this powerful interactive application, business analysts are also exposing themselves in a natural and productive way to the Python programming language itself.
Gaining proficiency with this language opens up further possibilities. While interactive analytic techniques are of great use to many business analysts, being able to create fully functioning, independent programs is of similar value. Becoming comfortable with Python allows analysts to tackle and plumb even larger data sets than would be possible through an interactive approach, as results can be allowed to accumulate over hours and days of processing time.
This ability can sometime allow business analysts to address the so-called "Big Data" questions that can otherwise seem the sole province of specialized data scientists. More important than this higher level of independence, perhaps, is the fact that this increased facility with data analysis and handling allows analysts to communicate more effectively with such stakeholders. Through learning a programming language which allows them to begin making independent inroads into such areas, business analysts gain a better perspective on these specialized domains, and this allows them to function as even more effective intermediaries.
Related:
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.
Unlike Java, Python does not have a string contains method. Instead, use the in operator or the find method. The in operator finds treats the string as a word list whereas the find method looks for substrings. In the example shown below, 'is' is a substring of this but not a word by itself. Therefore, find recoginizes 'is' in this while the in operator does not.
s = "This be a string"
if s.find("is") == -1:
print "No 'is' here!"
else:
print "Found 'is' in the string."
if "is" in s:
print "No 'is' here!"
else:
print "Found 'is' in the string."
#prints out the following:
Found 'is' in the string
No 'is' here!
Let’s face it, fad or not, companies are starting to ask themselves how they could possibly use machine learning and AI technologies in their organization. Many are being lured by the promise of profits by discovering winning patterns with algorithms that will enable solid predictions… The reality is that most technology and business professionals do not have sufficient understanding of how machine learning works and where it can be applied. For a lot of firms, the focus still tends to be on small-scale changes instead of focusing on what really matters…tackling their approach to machine learning.
In the recent Wall Street Journal article, Machine Learning at Scale Remains Elusive for Many Firms, Steven Norton captures interesting comments from the industry’s data science experts. In the article, he quotes panelists from the MIT Digital Economy Conference in NYC, on businesses current practices with AI and machine learning. All agree on the fact that, for all the talk of Machine Learning and AI’s potential in the enterprise, many firms aren’t yet equipped to take advantage of it fully.
Panelist, Michael Chui, partner at McKinsey Global Institute states that “If a company just mechanically says OK, I’ll automate this little activity here and this little activity there, rather than re-thinking the entire process and how it can be enabled by technology, they usually get very little value out of it. “Few companies have deployed these technologies in a core business process or at scale.”
Panelist, Hilary Mason, general manager at Cloudera Inc., had this to say, “With very few exceptions, every company we work with wants to start with a cost-savings application of automation.” “Most organizations are not set up to do this well.”
Tech Life in Minnesota
Company Name | City | Industry | Secondary Industry |
---|---|---|---|
The Affluent Traveler | Saint Paul | Travel, Recreation and Leisure | Travel, Recreation, and Leisure Other |
Xcel Energy Inc. | Minneapolis | Energy and Utilities | Gas and Electric Utilities |
Thrivent Financial for Lutherans | Minneapolis | Financial Services | Personal Financial Planning and Private Banking |
CHS Inc. | Inver Grove Heights | Agriculture and Mining | Agriculture and Mining Other |
Hormel Foods Corporation | Austin | Manufacturing | Food and Dairy Product Manufacturing and Packaging |
St. Jude Medical, Inc. | Saint Paul | Healthcare, Pharmaceuticals and Biotech | Medical Devices |
The Mosaic Company | Minneapolis | Agriculture and Mining | Mining and Quarrying |
Ecolab Inc. | Saint Paul | Manufacturing | Chemicals and Petrochemicals |
Donaldson Company, Inc. | Minneapolis | Manufacturing | Tools, Hardware and Light Machinery |
Michael Foods, Inc. | Minnetonka | Manufacturing | Food and Dairy Product Manufacturing and Packaging |
Regis Corporation | Minneapolis | Retail | Retail Other |
Fastenal Company | Winona | Wholesale and Distribution | Wholesale and Distribution Other |
Securian Financial | Saint Paul | Financial Services | Insurance and Risk Management |
UnitedHealth Group | Minnetonka | Financial Services | Insurance and Risk Management |
The Travelers Companies, Inc. | Saint Paul | Financial Services | Insurance and Risk Management |
Imation Corp. | Saint Paul | Computers and Electronics | Networking Equipment and Systems |
C.H. Robinson Worldwide, Inc. | Eden Prairie | Transportation and Storage | Warehousing and Storage |
Ameriprise Financial, Inc. | Minneapolis | Financial Services | Securities Agents and Brokers |
Best Buy Co. Inc. | Minneapolis | Retail | Retail Other |
Nash Finch Company | Minneapolis | Wholesale and Distribution | Grocery and Food Wholesalers |
Medtronic, Inc. | Minneapolis | Healthcare, Pharmaceuticals and Biotech | Medical Devices |
LAND O'LAKES, INC. | Saint Paul | Manufacturing | Food and Dairy Product Manufacturing and Packaging |
General Mills, Inc. | Minneapolis | Manufacturing | Food and Dairy Product Manufacturing and Packaging |
Pentair, Inc. | Minneapolis | Manufacturing | Manufacturing Other |
Supervalu Inc. | Eden Prairie | Retail | Grocery and Specialty Food Stores |
U.S. Bancorp | Minneapolis | Financial Services | Banks |
Target Corporation, Inc. | Minneapolis | Retail | Department Stores |
3M Company | Saint Paul | Manufacturing | Chemicals and Petrochemicals |
training details locations, tags and why hsg
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.
- We have provided software development and other IT related training to many major corporations in Minnesota since 2002.
- 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…