Learning SQL with Microsoft SQL Server Training Course


21 hours (usually 3 days including breaks)


  • NO previous SQL or database experience is required.


Microsoft SQL Server is a relational database management system (RDBMS) for storing and retrieving data.

In this instructor-led, live training (onsite or remote), participants will learn the essentials of the SQL language needed to query a Microsoft SQL Server database.

By the end of this training, participants will be able to:

  • Install and configure Microsoft SQL Server
  • Query a Microsoft SQL Server database using Microsoft’s Transact-SQL (T-SQL) language
  • Understand the essentials of database design
  • Optimize a database through normalization
  • Use aggregate functions to generate results similar to those of “pivot-tables”
  • Access Microsoft SQL Server through Excel


  • Data analysts
  • Business Intelligence professionals
  • Business managers
  • Excel experts who wish to expand their analysis skill set

Format of the Course

  • Interactive lecture and discussion
  • Lots of exercises and practice
  • Hands-on implementation in a live-lab environment

Course Customization Options

  • This training is based on the latest version of Microsoft SQL Server
  • To request a customized training for this course, please contact us to arrange.

Course Outline


Installing and Configuring Microsoft SQL Server

Overview of Microsoft SQL Server Features

Overview of Transact-SQL Concepts

Querying a Database using SELECT

Filtering the Data with WHERE

Sorting the Data with ORDER BY

Querying Multiple Tables with Joins

Grouping Data with GROUP BY

Using Aggregate Functions to Perform Calculations on Resulting Data

Filtering Groups with HAVING

Creating a New Database

Understanding the Database Model

Optimizing the Database through Normalization

Enhancing your Queries with Procedures

Writing Functions and Sub-routines

Using Conditions to Control the Flow

Connecting to Microsoft SQL Server through Excel


Summary and Conclusion

MS SQL Server 2016 Training Course


14 hours (usually 2 days including breaks)


As a core of the Microsoft data platform, SQL Server is a leading operational database management system.

Course Outline

Performanace and Management

  • Enhanced Database Caching
  • Query data store
  • In-Memory OLTP in SQL Server 2016


  • Temporal Database
  • Temporary Table and Variable Table in-memory
  • Native JSON

High Availability and Security

  • Enhanced AlwaysOn
  • Always Enrypted
  • Row-level Security
  • Dynamic Data Masking

Data Insight and Business Intelligence

  • Operational Analytics
  • New functionality Columnstore Index
  • Direct Query in SSAS Tabular
  • R Integration (language R in SQL Server)
  • Enhanced SSIS
  • Enhanced MDS

Reporting Services

  • New Report Server
  • Mobil Reports
  • SQL Server Mobile Report Publisher

Cloud and Hybrid

  • Stretch Database
  • Enhanced backup to Azure
  • Migration SQL Server to Azure
  • SSIS and Data Factory

Add Natural Language Processing AI power to App by LUIS API

Integrate Natural Language Processing in App by Microsoft Cognitive Services Language Understanding Intelligent Service


  • Experience as a C# .NET developer
  • Create a chatbot using Bot Builder SDK (Basic Level)
  • Visual Studio 2015/2017 Community Edition
  • Bot Framework Emulator
  • Azure Subscription
  • LUIS account


Why you should enroll for this course?

Artificial Intelligence (AI)  is going to be a core component of traditional applications.

Microsoft Cognitive Service APIs like LUIS API enables developers to build custom machine learning language model.

Artificial Intelligence in the form of Cognitive APIs like Language Understanding Intelligent Service (Natural Language Processing – NLP enables application to process natural language.

AI powered Chatbot with natural language processing capabilities will dominate traditional web and mobile app.

Microsoft Cognitive Service APIs like LUIS API is product of Artificial Intelligence, created using Machine Learning specially by Active Learning (Semi-Supervised Learning – SSL).

Course Includes:

Briefly introduced:

  • Overview of Microsoft Cognitive Services
  • Overview of Language Understanding Intelligent Service (LUIS)

LUIS Basic concept:

Every concept of LUIS building block is explained with real-world example and hands on coding supported by extensive code walk-through

  • What is LUIS modelIntent?
  • What is entity (simple, pre-built, hierarchical, composite, list)?
  • How a list entity helps to increase entity detection?
  • What is features in machine learning?
  • What is phrase list and how phrase list helps to improve LUIS performance?  
  • How phrase list and list entity differs and when to use which one?

Design the custom LUIS model 

Designing of custom LUIS model includes every concept and building block of LUIS with a real world use case.

  • Identifying  model and Intent.
  • Identifying entities.
  • Identifying phrase list.
  • Identifying utterances and typo/misspelling consideration.

Build the custom LUIS model 

  • Build the LUIS model by creating LUIS model, intent, entities.
  • Adding utterances to intent and labeling the entities.

Followed by

  • Train and Test the LUIS model (interactive testing)
  • Create Bing Spell Check API in Azure portal
  • Adding Bing Spell Check API to correct typo/misspelling from user query/utterances.
  • Create Azure LUIS API in Azure portal and get endpoint key (with free/paid tier).
  • Publishing to HTTP endpoint using this endpoint key
  • How LUIS  improves its performance using Phrase List and by active learning  – review endpoint utterances.
  • Build the LUIS model with prebuilt domain model: from model training, testing and publishing to HTTP endpoint; Integrating with IOT App.
  • Demonstrationintegration of LUIS model with chabot  and IOT app — debugging and code walk-through on how LUIS gets natural language from apps and parse query and get back to chatbot and web app. 
  • Bonus LectureImprove LUIS model performance using phrase list and reviewing the endpoint utterances.

Who this course is for:

  • Developer/Decision maker wants to integrate natural language processing AI capabilities in Chatbot or IOT app Microsoft Cognitive Services LUIS API
  • C# and .NET Developers passionate about new technology and wants to up skill by learning/implementing Microsoft Cognitive Services LUIS API
  • College students who passionate to explore and shape their career in Artificial Intelligence, Machine Learning and Natural Language Processing
  • Existing Python, Java,JavaScript, Node js, PHP, Ruby developer who wants to integrate natural language processing AI capabilities in application Microsoft Cognitive Services LUIS API
  • Developer/Decision maker who wants to create custom machine learning model without help of data scientist.

Course content

14 sections • 78 lectures • 3h 25m total length

The Rise of the Machines: Assessing The Ethical Risks of Machine Learning And AI

As the world progresses towards digitalization, more people are adopting Artificial Intelligence (AI). The pandemic has accelerated this adoption. There are predictions that computers and robots will become more capable of comprehending multiple languages and knowledge.

Machine learning is an association of artificial intelligence (AI) and computer science that operates data and algorithms to emulate how humans learn, gradually enhancing its accuracy, defined mainly as a machine’s capability to mimic intelligent human demeanour.

Machine Learning involves machines learning on their own without explicit programming. These systems use quality data to build various machine-learning models with the help of algorithms. The selection of algorithms is determined by the nature of the data and the specific task that needs to be accomplished.

Dark AI Scenarios and Malevolent AI:
It’s important to remember that everything has two sides much like a coin for everything good , will have something bad associated with it!, including machine learning.

While it has become a popular solution for many applications, hackers and crackers are finding ways to exploit these approaches. Although machine learning can bring innovation and adaptation to various sectors, it raises concerns and potential issues.

With powerful AI applications, personal secrets have the potential to be unravelled at the behest of Artificial intelligence much against our consent. Protecting personal information is crucial, and it’s important to remain vigilant.

While certain technologies were designed with good intentions, they can be misused if they end up in the wrong hands. As we explore the neverending possibilities of this innovative technology,

it’s important to remain mindful of its ramifications and negative impacts. While using Artificial Intelligence can be highly beneficial, it can pose a significant security and privacy risk.

Label Flipping:

Label flipping involves swapping the expected outcomes. A poisoning attack occurs when the attacker adds inadequate data to your model’s training dataset, leading it to learn inappropriate information. The most anticipated result of a poisoning attack is that the model’s boundary limits shift somehow.

threats with the Machine learning model:
Adversarial Examples/Evasion Attack:
One crucial security threat to machine learning systems is Adversarial Examples or Evasion Attacks, which are extensively studied. This attack involves manipulating the input or testing data to make the machine learning system predict incorrect information.

This compromises the system’s integrity, and the confidence in the system is affected. It has been noted that a system that overfits data is vulnerable to evasion attacks.

If a hacker intercepts the interaction between the model and the interface responsible for showing results, they can display manipulated information. This type of attack is named the output integrity attack. Due to our absence of understanding of the actual inner working of a machine learning system theoretically, it becomes difficult to predict the natural result. Hence, when the system has shown the output, it is taken at face value. The attacker can control this naivety by compromising the integrity of the production.

Although machine learning algorithms have existed for decades, their popularity has increased with the growth of artificial intelligence, particularly in deep learning models that power today’s most advanced AI applications. Many major vendors, including Amazon, Google, Microsoft, IBM, and others, compete to sign up customers for their machine learning platforms.

These platforms cover machine-learning activities, including data collection, preparation, classification, model building, training, and application development. There is a growing trend towards utilizing a critical technology that many businesses across various industries are steadily adopting at a rapid pace.

24 Machine Learning Examples and Applications to Know

Machine learning is at the helm of social media, self-driving cars and more daily applications.

Machine learning, in which a computer simulates human thinking by using data models to recognize patterns and make predictions, is being applied in nearly every industry.

Indeed, machine learning examples are numerous, and they can be found in fields ranging from healthcare and banking to marketing and sports. The list of machine learning applications below will give you an idea of how the technology is used on a daily basis.


  • Social media personalization.
  • Image recognition.
  • Business intelligence optimization.
  • TV, movie and video recommendations.
  • Healthcare personalization.

Machine Learning and Image Recognition


Face ID authentication by Apple utilizes machine learning to carry out image recognition and unlock mobile devices. Apple’s biometric technology is powered by Vision, a deep learning framework which is able to detect the features of users’ faces and quickly match them to previous device records. The Vision framework can also be used to detect barcodes, text and landmarks through device cameras.


Microsoft’s Azure, a cloud platform of over 200 services, is utilizing its machine learning and DevOps features to fight against animal extinction in the Wild Me project. The Wild Me open-source platforms help researchers track, document and conduct analyses on wildlife population data based on animal photographs, overall accelerating research endeavors that may be understaffed or underfunded.


Waymo’s self-driving vehicles use machine learning sensors to crunch surrounding environment data in real time and help guide vehicle responses when faced with various situations, from a red light to a human walking across the crosswalk.


Online reviews site Yelp relies on machine learning to sort through tens of millions of photos users upload to its site and then uses the technology to group them into various categories, such as, food, menus, inside the establishment or outside photos.

Amazon Web Services

Amazon’s cloud service AWS provides Amazon Rekognition, which uses machine learning to automatically identify objects, people, text, and activities in both images and videos. AWS also offers free machine learning services and products to help developers and data scientists build, train and deploy customized machine learning models. 

Blue River Technology

Blue River Technology, an agriculture tech company, grafts together machine learning and computer vision to differentiate between crops and weeds, as well as achieve proper spacing between plants. The company’s See & Spray rig targets specific plants and sprays them with herbicide or fertilizer.

Machine Learning and Speech Recognition


Duolingo, the language learning app, incorporates machine learning-based speech recognition to gauge a user’s spoken language skills. The closer a user’s pronunciation is to native speaker data stored in Duolingo’s system, the higher the user will be scored during speaking and conversational lessons.


Google Translate can detect and switch between languages seamlessly, thanks to the Google Neural Machine Translation (GNMT) system, which is powered by machine learning and recurrent neural network technology. 

Using language datasets, the GNMT system can train models how to input, output and compare words and phrases between languages, making translation faster and more accurate over time. Google is continuing to use this technology to allow feats like text translation from images and under-resourced language translation.


Etsy, whose online marketplace platform for users to buy and sell products, applies machine learning to personalize the shopping experience, providing customized product recommendations and ads based on previous purchases or product searches.


A provider of AI-powered technology for pathology research, PathAI helps healthcare professionals measure the accuracy of diagnoses and the efficacy of complex diseases. Using predictive machine learning, the company’s technology can be used to make medicinal solutions more accurate, reproducible and personalized based on patient history.

Fit Analytics

Fit Analytics, which helps consumers find the right sized clothes, uses machine learning to make recommendations on the best-fit styles. It also uses the technology to assist brands in gaining insights into their customers from popular styles to average customer measurements.


In a process called collaborative filtering, Netflix uses machine learning to analyze the viewing habits of its millions of customers to make predictions on which media viewers may also enjoy. Recommendations are based on those predictions and determine what shows, movies and videos will display on the homepage and watch-next reel of each user.


NielsenIQ’s Label Insight platform manages a gargantuan database of product nutrients, product ingredients and product claims. Its product metadata platform uses machine learning to give a personalized view of each food product, such as ingredients, suppliers, and supply chain history, which helps customers decide whether to purchase an item.


Social media giant Twitter relies on machine learning to prioritize tweets that are the most relevant to every user. Twitter’s machine learning ranks tweets with a relevance score based on what you engage with the most and other metrics. High-ranking tweets based on similar engaged posts are placed at the top of feeds, so users are more likely to see them.


Quora, a social media question and answer website, uses machine learning to determine which answers are pertinent to your personal search queries. The company ranks answers based on results from its machine learning, such as thoroughness, truthfulness and reusability, when seeking to give the best response to a question.

Machine Learning and Finance

Capital One

Financial institution Capital One uses machine learning to detect, diagnose and remediate anomalous app behavior in real time. It also uses the technology as part of its anti-money laundering and fraud tactics to adapt quickly to changes in criminals’ behaviors.


Deserve, a fintech company that lets institutions build and launch their own credit card and cryptocurrency programs, uses machine learning for its security, event management and compliance features. On personal platforms, the security feature analyzes event logs to detect user account patterns and quickly alert of any abnormalities.

Trading Technologies

Trading Technologies, a futures trading platform, uses machine learning to track analytics and identify trading behavior that could result in regulatory inquiries.

Machine Learning and Business

McDonald’s Global Technology

Tech is big at McDonald’s, which has been working to develop applications for new technology in the food and beverage industry. The company continues to push the boundaries of how AI and machine learning can optimize the process of making and serving food, using machine learning to automate order taking and to predict what menu items will sell the best at drive-thru windows.


Adtech company Yieldmo offers the Yieldmo Smart Exchange: a “global omnichannel exchange” for ad content. Different ad buyers have different KPIs, and Yieldmo’s predictive analytics are geared toward curating ad inventory to serve specific performance indicators. The exchange uses machine learning to analyze contextual ad data and pair ad publishers and buyers, with the goal of maximizing monetization and performance according to ad spend.


RS21 is a mission-driven data company that collects and analyzes information to create data-driven recommendations for clients in the healthcare, government and space infrastructure domains. Its main offering is the proprietary AI engine MOTHR, which serves as a replacement for data platforms that are challenging to scale. MOTHR’s comprehensive AI product development system includes a data warehouse and lakes, an AI solution library and apps for data visualization and projection.

Civis Analytics

Data analytics company Civis Analytics helps businesses make informed decisions. AI-powered business predictions and analytics are developed to give companies insights about how to identify, attract and engage their customers best.


Fashion retailer ASOS uses machine learning to determine the customer lifetime value (CLTV). This metric estimates the net profit a business receives from a specific customer over time. Machine learning aids ASOS in determining which customers are likely to continue buying its products and which customers are likely to have low CLTV, which in turn could affect ASOS offering them free shipping or other promotions.


Marketing, sales and service business software provider HubSpot uses machine learning in a number of ways. It gives content marketers insight into what search engineers associate their content with to assigning predictive lead scores for sales teams to use when assessing which customers are ready to purchase their products.

What is Generative AI, the technology behind OpenAI’s ChatGPT?


Like other forms of artificial intelligence, generative AI learns how to take actions from past data. It creates brand new content – a text, an image, even computer code – based on that training, instead of simply categorizing or identifying data like other AI.

The most famous generative AI application is ChatGPT, a chatbot that Microsoft-backed OpenAI released late last year. The AI powering it is known as a large language model because it takes in a text prompt and from that writes a human-like response.

GPT-4, a newer model that OpenAI announced this week, is “multimodal” because it can perceive not only text but images as well. OpenAI’s president demonstrated on Tuesday how it could take a photo of a hand-drawn mock-up for a website he wanted to build, and from that generate a real one.


Demonstrations aside, businesses are already putting generative AI to work.

The technology is helpful for creating a first-draft of marketing copy, for instance, though it may require cleanup because it isn’t perfect. One example is from CarMax Inc (KMX.N), which has used a version of OpenAI’s technology to summarize thousands of customer reviews and help shoppers decide what used car to buy.

Generative AI likewise can take notes during a virtual meeting. It can draft and personalize emails, and it can create slide presentations. Microsoft Corp and Alphabet Inc’s Google each demonstrated these features in product announcements this week.


A response by ChatGPT, an AI chatbot developed by OpenAI, is seen on its website in this illustration picture taken February 9, 2023. REUTERS/Florence Lo/Illustration/File Photo

Nothing, although there is concern about the technology’s potential abuse.

School systems have fretted about students turning in AI-drafted essays, undermining the hard work required for them to learn. Cybersecurity researchers have also expressed concern that generative AI could allow bad actors, even governments, to produce far more disinformation than before.

At the same time, the technology itself is prone to making mistakes. Factual inaccuracies touted confidently by AI, called “hallucinations,” and responses that seem erratic like professing love to a user are all reasons why companies have aimed to test the technology before making it widely available.


Those two companies are at the forefront of research and investment in large language models, as well as the biggest to put generative AI into widely used software such as Gmail and Microsoft Word. But they are not alone.

Large companies like Salesforce Inc (CRM.N) as well as smaller ones like Adept AI Labs are either creating their own competing AI or packaging technology from others to give users new powers through software.


He was one of the co-founders of OpenAI along with Sam Altman. But the billionaire left the startup’s board in 2018 to avoid a conflict of interest between OpenAI’s work and the AI research being done by Telsa Inc (TSLA.O) – the electric-vehicle maker he leads.

Musk has expressed concerns about the future of AI and batted for a regulatory authority to ensure development of the technology serves public interest.

“It’s quite a dangerous technology. I fear I may have done some things to accelerate it,” he said towards the end of Tesla Inc’s (TSLA.O) Investor Day event earlier this month.

“Tesla’s doing good things in AI, I don’t know, this one stresses me out, not sure what more to say about it.”