Statistical Data in AI Class 10 Notes – The CBSE has changed the syllabus of Std. X. The new notes are made based on the new syllabus and based on the New CBSE textbook. All the important Information are taken from the Artificial Intelligence Class X Textbook Based on CBSE Board Pattern.
Statistical Data in AI Class 10 Notes
What is data sciences?
Data science is a study of data which helps to extract information using modern tools and techniques. Data science uses complex machine learning algorithms to extract data from different sources or using different formats. Data sciences is a domain of AI related to data systems and processes, in which the system collects numerous data, maintains data sets and derives meaning/sense out of them.
Application of data science
1. Internet Search – Nowadays search engines are using data science to know what users want to search on the internet and also search engines want to know that the searching information is useful for users or not, some of the search engines using data science are Google, Yahoo, Bing and DuckDuckGo etc.
2. Targeted Advertising – Nowadays people are using the internet; digital advertising can be shown to only specific people based on their interests. This is the reason why digital ads have been able to get a much higher CTR (Click-Through Rate) than traditional advertisements. They can be targeted based on a user’s past behavior.
3. Website Recommendations – Website recommendations help the users to find relevant products from billions of products available on e-commerce websites. A lot of companies promote their products on e-commerce websites based on the interests and relevance of the users. Internet giants like Amazon, Twitter, Google Play, Netflix, LinkedIn, IMDb, and many more use this system to improve the user experience. The recommendations are made based on previous search results for a user.
4. Genetics & Genomics – Data science techniques allow integration of different kinds of data with genomic data in disease research, which provides a deeper understanding of genetic issues in reactions to particular drugs and diseases. As soon as we acquire reliable personal genome data, we will achieve a deeper understanding of human DNA.
5. Finance – Data science also plays a crucial role in the finance sector. Data science can help banks to identify the fraud and risk of losses. Nowadays, the finance sector wants to identify and analyze the risk of loss automatically, here data science can play a crucial role in identifying the risk factor of losses in the banking sector. Data science can also examine the past behavior of the stock market and make predictions for future outcomes.
6. Health Care – Nowadays many health industries use data science for identifying tumors, medical related image analysis, Patient health record maintenance, pharmaceutical development, predictive diagnosis etc. Data science also help the hospital to make more accurate predictions which reduce the rate of treatment failure.
Define High-Code, Low-Code and No-Code AI.
- High-Code – High code development refers to traditional software development where programmers write code manually using programming languages like Java, Python, C# etc. High-code is also known as custom-code.
- Low-Code AI – The person has some coding knowledge to create AI applications with minimum coding. Low-code users have some programming skills, and they can build their own applications. Low-code AI users can also use a drag-and-drop interface to build the components of AI.
- No-Code AI – It is a tool and platform where the users can build AI applications without writing any code. No-Code AI uses a drag-and-drop interface to build the components of AI and make it easy for the people who do not have a technical background.
Differences between Code and No-Code
| High-code | Low-code | No-code |
|---|---|---|
| A team of software coders need to write all the code manually | The person with some programming knowledge can build the application | Anyone can build applications |
| Only programmer can resolve the programming error | If any programming error is there, then it can take some time | The application can be developed quickly |
| it is expensive. | Developing costs will be high compared to the No-Code | Developing costs less |
| Flexible for adding more features | Flexible for adding more features | Limited customization and Limited to predefined templates only |
| The company can own the product they create. You can create anything and customise your product in any way | Example: IBM Watson Studio, DataRobot | Example: Google AutoML, Microsoft AI Builder |
Why do we need No-Code AI?
- We tend to run into many types of errors when we are coding, and it can be very troublesome at times.
- In No-Code AI since we do not need to code, we won’t have any code errors!
- No-Code AI helps to save cost for businesses as it is costly to implement completely coded AI systems.
- Companies can implement AI with less stress and without the need to hire an AI staff with No-Code AI.
- No-Code AI is easy to use – even middle school students can create AI using No-Code tools
- Since it has visual & drag-and-drop features, anyone can see what they are building in real-time.
Who can use No-Code AI?
- No-Code AI makes AI more accessible to the general public.
- Non-technical people such as doctors, architects, musicians may quickly construct accurate AI models with no coding involved.
What are the benefits of No-Code Tools?
- Accessibility – No-Code empowers non-technical makers to create websites and apps and also employ machine learning to solve business problems without programming.
- Fast – The speed at which no-code platforms enable you to build bespoke business solutions is significantly faster than traditional development.
- Easy to use – It includes drag-and-drop features that enable one to create an application with ease without any coding knowledge.
- Innovation – Since business users can now build solutions for their unique problems themselves, it creates a culture of innovation.
What are the disadvantages of No-Code Tools?
- Lack of Flexibility – Drag-and-drop elements can be very convenient. On the other hand, you are limited to those fixed elements.
- Automation Bias – Automation bias is the tendency for humans to favor suggestions from automated decision-making systems and to ignore contradictory information made without automation, even if it is correct.
- Security Issues – No code platforms do not essentially force you to think of security first or even evaluate security best practices. Therefore, these applications are only best suited for companies that don’t deal with sensitive data.
What are the popular No-Code Tools?
| No-Code tool | Details | Released |
|---|---|---|
| Azure Machine Learning | Cloud-based service provided by Microsoft | July 2014 |
| Google Cloud AutoML | Cloud-based service provided by Google | January 2018 |
| Orange Data Mining | An open-source data visualization, machine learning and data mining toolkit. Developer: University of Ljubljana | October 1996 |
| Lobe AI | Lobe AI is a machine learning platform that enables to create custom machine learning models using a visual interface | 2015 |
| Teachable Machine | Teachable Machine is a web-based tool that makes creating machine learning models fast, easy, and accessible to everyone. | November 2017 |
What is statistics in AI?
Statistics play an important role in analysis and dealing with data in data science. Statistics is used for collecting, exploring, and analyzing the data. It also helps in drawing conclusions from data.
Important concepts in statistics
Statistical sampling
- The entire set of raw data that you may have available for a test or experiment is known as the population.
- You cannot necessarily measure the patterns and trends across the entire population.
- Take a sample, or portion of the population, perform some computations.
What is descriptive statistics?
Descriptive statistics refers to a set of methods used to summarize and describe the main features of a dataset. Helps us to describe the data and enables us to understand the underlying characteristics.
- Mean – the central value, commonly called the average.
- Median – the middle value if we ordered the data from low to high and divided it exactly in half.
- Mode – the value which occurs most often.
What is the Orange data mining tool?
Orange is an open-source software of machine learning that helps to design based on a no-code or low-code framework. With the help of Orange software, you can design the data visualization, predictive modeling, and analysis of the data. The orange tool is easy to use and has a drag-and-drop interface, basically used in education, research, business, etc.
https://orangedatamining.com/download
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Statistical Data in AI Class 10 Notes
Statistical Data in AI Class 10 Notes
Statistical Data in AI Class 10 Notes
Statistical Data in AI Class 10 Notes
Statistical Data in AI Class 10 Notes