What is Statistical Testing?

Statistical testing is an essential part of data analysis. To make a decision for a population, a sample of data is collected from that population. Population and sample are two important terms to draw a conclusion with statistical testing. The term population refers to the entire set of data; on the other hand, a sample indicates the smaller part collected from that population. Some of the examples of sample and population are tabulated below:

Types of Statistical Testing:

There are two types of statistical testing. Based on the nature and assumptions made on the data, the statistical testing can be applied. Parametric and Non-parametric statistical testing are the two types of statistical testing.

To use the correct statistical test, we should correctly identify the types of data as nominal, ordinal, discrete or continuous. Because of the different types of data, the applied statistical test will be different, such as parametric or non-parametric.

Parametric statistical test assumes that the data that are considered follow a specific distribution such as a normal distribution. However, the non-parametric test does not follow the normal distribution. Whenever the assumption of a parametric statistical test is not fulfilled, then the non-parametric test can be applied. It is flexible to use. The non-parametric test is also known as a distribution-free test since it does not follow any distribution like the parametric test.

Assumptions for parametric statistical tests:

1. Specific distribution: The data follows a Normal distribution or Gaussian distribution. To follow the normal distribution, the generated curve from the data must be bell-shaped with a symmetric distribution of data and centred around the mean.

2.Since the data is normally distributed, no such outliers present in data.

3.Since the parametric test follows normal distribution, therefore the considered parameters are mean and standard deviation.

4. Independence of data: The data that are concerned are not correlated; they are independent from one observation to another.

5. Similar in variance: The variances calculated for different groups are similar.

Assumptions for non-parametric statistical tests:

1. Specific distribution: The data that are considered do not follow a specific distribution, like a normal or Gaussian distribution.

2. Since the non-parametric test does not follow a normal distribution, there is no fixed set of parameters, such as mean and standard deviation, as in a parametric test.

3. The non-parametric test can be applied if the distribution is skewed. For skewed distribution, data may have outliers.

4. The non-parametric test tests the median.

5. For a non-parametric test, more data may be required in comparison to a parametric test.

Normally, the parametric test is more powerful than the non-parametric test.

The next part explains three basic parametric tests as below:

The Basic Parametric tests:

i. T-test: T-test is a statistical test that is used to compare the means of two groups. If there are more than two groups exist, then T-test cannot be applied. To start with T-test, the concept of hypothesis testing is applied about the means of two groups are identical which is a null hypothesis. The assumptions behind the T-test are as below:

a.The generated curve from the data within each group should be bell-shaped that means for each group the data should be normally distributed.

b.For each of the group, the data must be independent but not correlated.

c.The calculated variances for each group should be equal.

Different types of T-test:

ii.F test: The F test is a parametric test that compares the variances of two or more samples to check the variances between the samples are equal or not. The information about F test is tabulated as below:

iii.Z test: A Z-test is a statistical technique that is applied to determine whether the sample mean is significantly different from the population mean by calculating the Z score value. The Z score is calculated as follows:

z=(x ̅-μ)/σ

Where,
x ̅, μ and σ are the sample mean, population mean and population standard deviation, respectively.
The Z test is applied if the sample dataset size is greater than 30.

Conclusion: The above article explains the introduction of statistical testing, along with the types and assumptions associated with each type. It also focuses on three basic parametric tests like the T-test, F-test and Z-test.

A Study on Basic Parametric Statistical Testing was originally published in Predict on Medium, where people are continuing the conversation by highlighting and responding to this story.

Usually, it comes to our mind that an AI career is only compatible with students who have programming skills and a computer science background. But the concept is not like that. Let, assume the following scenarios.

1. Are you a student with an Arts background?

2. Are you a student with a science background without having programming courses in your syllabus?

3. Are you a student of Computer Science but not so interested in programming skills?

And you want to choose AI careers, as it is now the trending topic. However, don’t panic, as there are numerous possibilities for an AI career, even without programming skills. The possibilities are:

1. AI Product Manager: AI Product Manager is a person who works with data scientists and engineers and is responsible for the development of an AI product by identifying their purpose, features and functionality.

Responsibility:

· He should earn the basic theoretical knowledge of AI, like what the AI can do, its pros and cons, without programming skills.

· Capacity to make the proper decision at the planning level.

· Capacity to work with the coordination of Data Scientists and Engineering teams, Design teams to translate the business ideas into technical ideas in a clear manner.

· Communication with the leaders to report the progress of the work.

· Keep the users’ feedback to track their requirements.

2. AI Community Manager: AI community manager has a response to create, engage and support a community of people like users, business stakeholders, developers and researchers who are interested in AI products.

Responsibility:

· Managing, commenting and encouraging participation through the discussion on social media or community platforms.

· Sharing the information like AI tools, features and newly launched products.

· Sharing of tutorials, tips and guides to the users for understanding the AI tools.

· Arranging workshops and webinars from time to time among the community people.

· Collecting feedback from the community and sending it to the AI team members.

3.AI Business Analyst: An AI business analyst is a person who makes the bridge between business stakeholders and data scientists to solve business problems with AI.

Responsibility:

· Connect with business teams to understand their business problems and challenges.

· Find out the AI opportunities to improve the business processes by saving time.

· Planning in AI projects, including goals and time limits.

· Make the documentation part, including the tasks of AI and send it to the technical team.

· Presentation of AI results in simple ways, such that business teams can get the idea easily.

4. AI Project Manager: AI Project Manager is a person who communicates with users through online mode. The companies like Chatgpt, Runway and Hugging Face etc. have the AI project manager.

Responsibility:

· He/ She does not need to write the code for the development of an AI product, but should have the proper guiding capacity for planning the development of the product.

· He should be conscious of the timing management of the product from development to the finishing and launching of the product.

· Capacity to properly divide the task and provide each task to the proper team member.

· Keep track of the progress of the project with the predefined time bounds.

· Act as a resource management person for the team members for their necessary requirements.

5. Data Annotator / AI Trainer: A Data Annotator / AI Trainer is a person who is responsible for the labelling processes in images, audio, videos and text in an AI system.

Responsibility:

· The task of adding the tags or labelling to images, audio, videos and text, such that AI can easily learn about them.

· For correctness and consistency of the labelling, follow the instructions with rules and regulations.

· Use special software for the labelling system.

· Coordination with AI team members.

· Follow the privacy rules for sensitive information.

6. AI Content Creator / Educator: An AI Content Creator / Educator is a person who uses AI tools to create educational content or teaches the topic on AI articles, including AI tools and technologies.

Responsibility:

· Utilizing AI tools such as ChatGPT, Synthesia, DALL-E, and Grammarly to create educational content with scripts, audio, and video.

· Include the AI learning topics in educational content as machine learning, generative AI, and ethical AI.

· Arrange the workshops, from time to time, to demonstrate the uses of AI tools.

· Create the content in different platforms such as blogs, social media posts, or podcasts, short videos, etc.

· Stay updated with new features and tools of AI.

7. AI Research Coordinator: An AI research coordinator is a person who organizes and manages the research project and works with researchers, data scientists and stakeholders, etc.

Responsibility:

· The task is teamwork with the researchers, data scientists, engineers, and stakeholders.

· Prepare the documentation part of grant applications for the funding proposals by managing the submission deadlines.

· Organise all the records of research related to datasets, documentation and research output, systematically and securely.

· Track and prepare progress reports.

· Manage and organise the required resources for the project.

8. AI Marketing Specialist: An AI Marketing Specialist is a person who applies AI tools to improve the marketing strategy of a company’s products and services.

Responsibility:

· Analyse data about customers’ likes and dislikes with AI tools to define the present and future trends.

· Reply for customer questions 24/7, by using AI-powered chat tools.

· To improve the marketing ads, social media posts or blogs, apply the AI tools.

· Communicate with the technical and design team to run the AI tools properly.

· Stay updated with new AI tools and features to improve the marketing strategy of the company from time to time.

9. Prompt Engineer (Text-based): A Prompt Engineer (Text-based) is a person who guides the AI system by designing the words and structuring the instructions to achieve superior results.

Responsibility:

· Develop clear, detailed, and structured input statements to shape the AI-generated output.

· To get the accuracy of the AI responses, check with multiple versions of the prompts.

· To use the AI effectively, guide the non-technical persons properly.

· Continuous monitoring of the changes in the AI model.

· Communicate with the teams, such as product, design teams, data scientists or engineers.

10. AI Tester / Quality Analyst: An AI Tester/ Quality Analyst is a person who checks whether an AI system is working properly or not.

Responsibility:

· Interaction with the AI tools to check their outputs.

· Compare the predicted result of the AI system with the expected result.

· Labelling of the test dataset for testing with an AI system.

· Test the functionality of the AI systems on different platforms like mobile, desktop and laptop, etc.

· Verify whether the AI systems maintain the privacy policy of user data or not.

Conclusion: The above write-up explains the 10 opportunities with responsibilities for a person without programming coding knowledge.

The AI Careers Without Programming Skills was originally published in Data Science Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

Are you a student, freelancer, stay-at-home, or simply busy exploring side hustle opportunities?

Then there is a list of options based on user interests to make money without any technical background or startup capital. As day by day, technology is growing very fast, AI is one of the inventions of such technological growth. With the proper and right way of using AI, people are starting to earn money. To do so, you should be intellectual in any of the present demanding niches. If you are not so, don’t worry, try to learn your interesting niches by consuming a few months and then try to follow the proper track to earn money. To start with that firstly you should research to find out the probable field which will be compatible with you. Here, no programming skills are required; the only requirement is a willingness to explore new concepts, learn, and research highly demanding, time-relevant concepts with consistency and creativity. The different focused platforms are detailed below:

1.Content Writing

2.Video and Multimedia

3.Web and Business Tools

4.AI Art and Creativity

5.Design and Branding

The different AI tools for each of the categories are:

1.Content Writing:

a. ChatGPT (GPT-3.5):

Free Features:

· It acts as a chat assistant.

· ChatGPT can support different languages like English, French, Spanish, Jarman, Italian, Hindi, Japanese, and Turkish, etc. Their level of performance may be different among themselves.

· Both the platforms, like mobile and desktop, support the ChatGPT.

· It helps to generate and polish the ideas in content writing like scripts, stories and poetry, etc. Also helps in the Generation of summaries, email drafts.

· It acts as a teaching and learning tool for teachers and students.

b.Grammarly:

Free Features:

· It acts as a grammar and spelling checker.

· It helps in punctuation correction.

· It helps to make the sentence shorter by reducing unnecessary words without losing clarity.

· It gives tips to replace with better words.

· To prevent the loss, it automatically saves the writing content online.

c. Quillbot:

Free Features:

· It acts as a Grammar Checker.

· It helps to generate summaries.

· It works as an AI Detector to detect whether the written content is AI-generated or not.

· It works as a Translator.

· It acts as a Paraphrasing tool.

d. Notion AI:

Free Features:

· It helps to generate ideas for the content.

· It acts as a Paraphrasing tool.

· It is a translator tool for more than 30 languages.

· It helps in Grammatical improvement.

· It provides AI Writing Help.

Ideas to Make Money through different platforms:

i.Fiverr: Earn with Blog Writing, Script Writing, Product Descriptions, Resume & Cover Letter Writing and Translation Help etc.

ii.Upwork: Earn with Blog/Article Writing,, Social Media Content, Script Writing, Resume Writing, Translation Help, E-book Formatting & Ghostwriting, etc. Both Fiverr and Upwork are popular online freelancing platforms which offer the opportunity to earn from home, but differ in their working and job principles.

iii.Medium (Partner Program): Generate informative, demanding and high-quality articles and get payment according to the reader engagement and reading time.

iv.Teachable and Thinkific: Both are online platforms and can earn money by selling digital courses and training programs.

v.YouTube: Make money from YouTube videos by utilising ChatGPT to generate scripts and voiceovers.

2.Video and Multimedia:

a. Pictory: Pictory provides a 14-day trial-free facility with most premium features, meaning that Pictory is not permanently free.

Free Features:

· It turns the written Script into a fully edited Video.

· It helps to convert an article to video by summarising the content.

· It supports voiceovers in multiple languages.

· It helps as an auto-summarisation tool.

· It provides the facility in video to auto-generate captions.

b. InVideo:

Free Features:

· InVideo is an AI video generator which provides the facility for 10 minutes/week.

· It provides the cloud storage of 10 GB.

· It can be used in social media platforms with a watermark.

· Voice cloning is not available.

· It allows the creation of 1 express avatar (a quick‑create AI clone) per account.

c. Descript:

Free Features:

· It provides the facility of Transcription for 1 hr/month.

· It provides the remote recording (e.g., virtual meetings) for 1 hr/month.

· It facilitates one video with no watermark in 720p resolution per month.

· It helps for text-to-speech/AI voice generation for 5 minutes.

· For each of the files, the maximum upload limit is 1 GB per file.

d.Runway ML:

Free Features:

· For different generative models for the creation of video clips, different time frames in seconds are fixed.

· The maximum limit is 3 video projects in the user workspace.

· It allows uploading and generating media with 5 GB of storage capacity.

· The created videos may have a watermark.

· The creation of higher-quality videos is not accessible.

Ideas to Make Money through different platforms:

i.Fiverr, Upwork, Freelancer

ii.Youtube (Affiliate marketing, AdSense)

iii.Teachable

iv.Instagram, Facebook

v. Gumroad, Ko-fi

3.Web and Business Tools:

a. Durable.co:

Free Features:

· It helps to create a 3-page AI website instantly.

· It helps in the creation of AI-generated content.

· Availability of AI-generated free stock of photos.

· It acts as an AI Blog Generator (Limited).

· It helps in basic customisation for text, images, font, colours, etc.

b. AppyPie:

Free Features:

· It is an HTML5 Web App Builder.

· It can access the pre-built template library for various apps.

· It offers unlimited edited options with no cost.

· It allows for basic style customisation, such as modification of font, images, and colours, etc.

· Preview & Testing can be performed during app development via a browser without requiring a login.

c. Photopea:

Free Features:

· It is compatible with multiple platforms like Windows, Linux, Mac and mobile browsers etc.

· Editing of the image can be performed without registration/ login.

· It supports multiple UI languages.

· Basic AI features can be accessed (1 use/day).

· In offline mode also, the tasks can be performed after loaded for only once.

d. Wix ADI:

Free Features:

· A complete website can be created using stock images, templates, and asking questions.

· It provides the facility of AI auto-generated design and layout with suitable theme.

· Automatically the relevant images can be added to the created website with no cost.

· Automatically mobile responsive version can be setup for the created site.

· Themes can be switched from one to another easily with no losing of existing content.

Ideas to Make Money through different platforms:

i.Fiverr, Upwork, Freelancer.

ii. Promote on Facebook, Instagram, and LinkedIn.

iii. Gumroad

iv. Etsy, TPT.

v.Launch the personal website.

4.AI Art & Creative Assets:

a. Leonardo AI:

Free Features:

· It allows editing and generating 150 images per day.

· It allows for 75 background removals per day.

· Does not allow for generating and editing multiple images at a time; rather, one image at a time can be performed.

· Instant AI updates can be checked by directly drawing and editing on the canvas.

· It provides the full rights for commercial purposes.

b.Fotor:

Free Features:

· Supports the basic editing options like brightness, resize, rotate, crop, etc.

· Supports AI background remover.

· Multiple images can be edited simultaneously.

· Cloud storage capacity is 500MB.

· There may be a watermark.

c. Craiyon:

Free Features:

· It supports unlimited image generation using text prompts.

· It acts as a background remover.

· No account or login is necessary to work with it.

· Free images have the watermark.

· The processing speed is low for the free version.

Ideas to Make Money through different platforms

i. Fiverr, Upwork, Freelancer.

ii. Amazon KDP, Etsy.

iii. Gumroad, Ko-fi.

iv. Teachers Pay Teachers.

v.. LinkedIn, Personal website.

5.Design and Branding:

a. Canva (Free Plan):

Free Features:

· It can be used as a designing tool for photo adjustments, text and many more.

· It permits 5 GB of free cloud storage.

· Limited use of AI features is available.

· Downloaded file formats are basic ones like JPG, PNG, and PDF.

· It can be accessed through multiple platforms.

b. Looka:

Free Features:

· It offers unlimited logo design and customisation in colours, font and symbol.

· Before buying, logo redesigning can be performed, as on the satisfaction.

· Applying hundreds of options, the AI-powered logos can be generated.

· A preview of the logo can be checked on multiple platforms like social media, websites and etc.

· Work on designing can be started with no user account or login option.

c. Adobe Express:

Free Features:

· Thousands of free music, videos and photoscan be accessed.

· It can be used as a core editing tool.

· Quick actions can be performed like GIF conversion, PDF edits, background removal and resize etc.

· Availabilty of 5 GB as a cloud storage for projects.

· It acts as a integrated learning and community with no cost.

Ideas to Make Money through different platforms:

i.Udemy, Teachable.

ii. Redbubble, TeeSpring.

iii. Etsy, Gumroad.

iv.Upwork, Reddit.

v. LinkedIn, Instagram, Facebook.

Conclusion:

Above is the list of AI tools and platforms through which money can be earned. Now, it’s time to decide your interested field with which you can grow yourself by earning money. Study each of the above-mentioned tools with their pros and cons.

Earn More Money Online with Free Features of AI Tools was originally published in Everyday AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

Is the data we handle in our day-to-day life of a similar kind? Consider the following examples:

i. Gender of a person

ii. Ratings for a Restaurant

iii. Age of a person

iv. Height and weight of a person

v. Temperature in a city for one week.

From the examples given above, what can we conclude? These are data of different characteristics.

Separation of data into types by analysing the characteristics present among themselves is known as classification of data. The data that have similar features are taken under one group. The diagrammatic representation of the classification of data is given below:

1. Qualitative Data: Qualitative data is also known as Categorical data. These types of data express the data in terms of its labels or categories. It does not provide the information in a meaningful numerical form. For example, the information like the gender of a person and the occupation of a person cannot be expressed in numbers, but can be expressed in different labels like male/female and HR/manager, respectively. Based on the nature, the qualitative data can again be divided into nominal data and ordinal data. The two types of qualitative data are nominal and ordinal data.

a) Nominal Data: Nominal data is one of the qualitative data types. The data that cannot be quantified or compared to determine whether less than or greater than, or cannot be calculated the operations like addition, subtraction or average of the values. They separated the data into groups or labels based on their characteristics. The nominal data does not consider the ranked or ordered data.

The Bar charts, Pie charts and Frequency tables are used to represent the nominal data.

Here, the non-parametric tests like the chi-squared test, Fisher’s exact test are applied to analyse the nominal data. These tests help to know about the relationship or independence between two nominal variables and also to frequency counts for observations for each label. The chi-squared test is used for larger datasets; however, for smaller datasets, Fisher’s exact test is applied.

Some common examples of nominal data are:

i. Gender of a person.

ii. Blood Group (A,B,O ) of a person

iii. Nationality of a person(Indian, American, Chinese)

iv. Hair color of a person(Black, Brown, White)

v. Language spoken by a person(English, Hindi, Franch)

b) Ordinal Data: The ordinal data can rank or provide the order to the data. Since here the concept of order or rank is applied, therefore, it deals with the relative operation of “label of comparison”. Ordinal data can be represented in pictorial forms, of a bar chart and a line chart.

Here, non-parametric tests like the Wilcoxon Signed-Rank test and the Mann-Whitney U test are applied to analyse the ordinal data. The Wilcoxon Signed-Rank test is used for comparing two matched or paired (dependent) samples. However, the Mann-Whitney U test is applied for two independent samples.

Some common examples of ordinal data are:

i. Education status (High School, Higher Secondary, Degree, Master’s Degree)

ii. Economic position (Low, Middle, High)

iii. Customer satisfaction level (Not satisfied, satisfied, highly satisfied)

iv. Degree of proficiency in a field (Beginner, In-between, Expert)

2. Quantitative Data: Quantitative data can represent the data in numerical form. It provides quantitative measurements like height-weight and the total number of students present in a school, and so on. Discrete and continuous data are types of quantitative data.

a) Discrete data: The data which are discrete or single-valued is known as discrete data. Discrete data are countable data, which are whole numbers.

Some of the common examples of discrete data are:

i.Total number of students in a college.

ii. Total number of students who obtained marks more than 90 in Mathematics in a school.

iii. Number of members in a family.

iv. Number of grocery items sold out on a day for a shop.

v. Population in a state

The discrete data can be represented by bar diagram, box plot etc.

To analyse the discrete data, t-test, z-test or Chi-square test, ANOVA test, etc. can be applied.

b) Continuous data: Continuous data are not countable but measured data. The values in continuous data are fractional values or decimal values. Continuous data is expressed within a specific range of data.

Some of the common examples of continuous data are:

i. Height and weight of a person

ii. Temperature in a city

iii. Blood pressure of a person

iv. Distance between two places

v. Speed of a car

A scatter diagram or histogram can represent continuous data.

To analyse continuous data, different statistical tests can be applied, like the Kolmogorov-Smirnov test, T-test, ANOVA test, Wilcoxon signed-rank test and so on.

Conclusion: Data is nothing but information. Before analysing any data, we should know about its types. Data can be represented in different pictorial forms, like bar chart, pie chart, histogram, box plot, line diagram and so on. The data, like gender, height, education status, etc., are statistical data of different types. This writing contributes to the classification of statistical data.

A Study on Classification of Data in Statistics was originally published in Data Science Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

Today, AI is widespread worldwide. The technology is growing so fast that tasks can be automatically completed without direct human physical touch.

Today, people tend to have extra income along with their prime income. Some others may also search for prime income in the digital world. Today, in the digital world, there are many ways to earn. And AI is one of them. It is not mandatory that to earn money with AI, he/she must know programming languages like C, C++, Java, Python, etc. But there is a scope for non-technical people also who can earn money without programming skills. The non-technical persons may be a homemaker, student, retired person, job seeker, freelancer, working professional (as a side income), but they may be experts in their specific fields. It will be very easy for a person to get the idea of a roadmap for earning with AI. However, for non-technical people, it will be difficult to understand such a roadmap. Because he may be confused about from what point he will going to start, even though he is familiar with the term AI. So, this writing is basically for those who are non-technical.

The different perspectives of AI, from which earnings can be made, are:

1. CONTENT WRITER:

Writing is one of the skills that can help in earning. For tasks such as grammar and spelling correction, plagiarism detection, enhancing the clarity and readability, etc., the Grammarly, LanguageTool, Hemingway Editor, and Ginger Software etc. can be used as AI-based writing tools to improve the writing style. After writing the content, they can upload their content in personal blogs made by WordPress or Blogger and in community-based publication systems like Medium, Substack, Tealfeed and Vocal Media, etc. Before using any AI assisting tools, they need to research their scope and limitations.

To grow the idea behind any topic or as an editing tool, they can also take the help of ChatGPT, DeepSeek coder, etc.

2. VIDEO CREATION:

Creating videos by researching users’ demanding trends in the market like technology, financial, teaching, recipes, gardening, clothing designing and sewing, home decoration, etc., as well as creators’ interests, they can earn money. To develop any concept and scripting of their videos, they may take the help of ChatGPT, DeepSeek, etc., as AI writing tools. The HeyGen, ElevenLabs, Resemble.ai, and Murf.ai can help in voiceovers & dubbing. Pictory, Synthesia, and InVideo can be used for the conversion of videos from text. VidIQ, TubeBuddy and ChatGPT help to upload SEO-friendly titles, tags and descriptions.

By creating videos of their working skills, they can upload the videos on YouTube, different social media such as Instagram, Facebook, Twitch and Snapchat, etc.

3. ON-DEMAND FREELANCE SERVICES PLATFORM:

These are platforms where the user can earn money by providing services from home or even from remote places. The services include writing and translation, audio-video-music-animation, graphics and design, etc. If he/ she has skills among these services, then they can forward to the platforms like Fiverr, Upwork and Freelancer.com, etc. The available AI tools are:

For writing and translation: ChatGPT, Grammarly, Google Translate, Microsoft Translator, etc.

For audio-video-music-animation: Adobe Creative Cloud (Suite), Blender, Audacity, etc. Although they may not be purely AI tools, but they integrate AI features.

For graphics and design: Canva, Runway ML, Logo AI, Uizard, etc.

4. SELLING OF SELF-PUBLISHING EBOOKS:

By writing and editing with AI tools (like ChatGPT, Grammarly, Jasper, Canva, etc.), earnings can be made by selling eBooks, low-content books, Hardcover Books, etc. The self-publish sites are Amazon KDP, Google play, Lulu, Blurb, etc.

5. SELLING OF COURSES:

By applying their own knowledge on specialised contents along with the help from AI tools, different self-courses can be made to sell. There are different sites to sell such products, like Udemy, Teachable, Podia, etc. The areas of courses that can be uploaded for sale are Programming, AI, cybersecurity, Math, Science, languages, graphic design, Yoga, art-craft, photography, writing, marketing, finance, personal development, etc.

6. SELLING OF PRINT-ON-DEMAND (POD) ITEMS:

For an artist or a creative-minded person, they can earn money by designing items like t-shirts, eco-friendly bags, pen stands, phone cases, water bottles and mugs, etc. To generate designs, they can take advantage of the AI tools like Canva, Dall-E and Midjourney. The POD platforms to sell items are Myntra, Amazon, Flipkart, Shopify and Etsy etc.

7. SELLING OF JEWELLERY ITEMS:

Jewellery design with AI technique comprises a 3D printing model, which is different from the traditional print-on-demand (POD) model. Here, an AI tool is used to generate a unique design, and then that design is converted to a 3D model. For design purposes, the available AI tools are DALL·E, Midjourney, Runway ML, Adobe Firefly, etc. Based on the requirement, the CAD (Computer-Aided Design) can also be used. The available platforms, such as Amazon, Flipkart, Meesho, Myntra, Shopify and Etsy, etc. can be considered for selling the items.

8. AFFILIATE MARKETING:

This is a commission-based earning method by promoting others (may be individual or company) products. The different ways to promote the products are YouTube videos, Blogging, Instagram, etc, where the affiliate links are provided. Some of the affiliate marketing platforms are Amazon Associates, ShareASale, Canva, Teachable, ClickBank. etc. The products that can be promoted are laptops, headphones, furniture, baby products, health and fitness products, eBooks, online courses, software, web hosting, etc.

CONCLUSION:

Although, here only 8 ways are mentioned, there are many more ways available to earn money with AI assistants. Based on the creator’s interest, they may go ahead. To use any AI tools, they have to be concerned about their pros and cons.

Thank you for being a part of the community

Before you go:

👉 Be sure to clap and follow the writer ️👏️️

👉 Follow us: X | Medium

👉 Follow our publication, CodeToDeploy, for Daily insights on :

• Software Engineering | AI | Tech
• Tech News
• AI Tools | Dev Tools
• Tech Careers & Productivity

Boost Your Tech Career with Hands On Learning at Educative.io

Want to land a job at Google, Meta, or a top startup?
Stop scrolling tutorials — start building real skills that actually get you hired.

✅ Master FAANG interview prep
✅ Build real world projects, right in your browser
✅ Learn exactly what top tech companies look for
✅ Trusted by engineers at Google, Meta & Amazon

📈 Whether you’re leveling up for your next role or breaking into tech, Educative.io helps you grow faster — no fluff, just real progress.

Users get an additional 10% off when they use this link.

👉 Start your career upgrade today at Educative.io

Note: Educative.io is a promotional post and includes an affiliate link.

AI Tools Needed for the Growth of Online Business and Monetisation was originally published in CodeToDeploy on Medium, where people are continuing the conversation by highlighting and responding to this story.

Forecasting is necessary to prepare for the future. Humans have a tendency to predict the future. In forecasting, the pattern of the future is predicted based on the hidden patterns of past and present data.

Where the Forecasting Concept can be applied:

The concept of forecasting can be found in the fields of weather forecasting, healthcare, agriculture, the stock market, selling items, budgeting and finance planning, population growth rate, etc., as given below:

Weather forecasting: To know about the future climate behaviour or to predict the rate of rainfall for the near future, weather needs to be forecast.

Healthcare: To predict hospital admission or resource allocation for any epidemic, the pattern of past data is analysed.

Agriculture: To know about future crop production or pest infestations, a prediction is made. With agriculture forecasting, weather forecasting is also related.

Stock market: By analysing the past market trends, future stock prices can be predicted.

Selling items: The pattern of demand from the past data is analysed to predict the allocation of resources for the future.

Budgeting and finance planning: By knowing past records of income, expenses, profit, and revenues, etc., future financial planning can be prepared.

Population growth rate: Future trends of the population growth rate need to be studied to prepare different policy-making strategies, such as economic development, urban planning, and education, etc.

Statistical Forecasting Models:

There are different statistical forecasting models, such as Time Series, Cause-And-Effect and Judgmental methods, which depend on the following aspects:

1. Nature and availability of historical data: Selection of a forecasting model is a crucial task. What the forecasting model will be used that depends on how much historical data is present. The pattern that exists in historical data is also considered an important factor in applying the forecasting model. For horizontal, seasonal, cyclical and trend patterns, the time series forecasting model is used. By observing the relationship between dependent and independent variables, the Cause-and-Effect model can be used. (For example, the relationship between the selling of umbrellas and the amount of rainfall in a rainy season is the dependent and independent variables.) The judgmental method is applied when there is no availability of historical data.

2. Purpose: The different purposes of forecasting imply different time horizons for considering the historical data. Usually, daily weather forecasting is a short-term forecasting, but the 5-year government-based planning will be a long-term forecasting. So, depending on the purpose, the forecasting model will differ.

Again, each of the forecasting models has different models as given below:

i)The Time Series Model can be divided into Moving Average, Exponential Smoothing(Single, Double and Triple), Trend Models, Box Jenkins(or Autoregressive Integrated Moving Average, ARIMA).

ii)The Cause-and-Effect model can be divided into Regression, Econometrics, and Neural Network.

iii)The Judgmental model can be divided into Analog, Delphi, Diffusion, PERT( Performance Evaluation Review Technique)

The different forecasting models as described above are broadly classified as Quantitative and Qualitative methods. The Time Series and Cause-and-Effect model are considered as Quantitative methods. On the other hand, the Judgmental model is known as a Qualitative method.

AI/ML Forecasting Models:

The full form for AI is Artificial Intelligence, and ML means Machine Learning. The AI/ML forecasting models deal with complex data with complex patterns which can predict the future. The large dataset is needed for training of AI models in comparison to traditional statistical forecasting models. Some of the AI/ML forecasting models are: Support Vector Machine (SVM), Decision Tree, Random Forest, K-Nearest Neighbors (KNN) etc.

SVM forecasting model is used to deal with non-linear patterns, such as the prediction of share prices in share markets. Decision Tree model is a tree-based model which can be used for short-term forecasting such as prediction of hourly temperature in a city . The random forest model is suitable for non-linear data with short-term forecasting. The KNN forecasting model is suitable whenever repetitive patterns of the data are to be observed and it is easy to implement.

Conclusion:

The concept of a statistical forecasting model enhances the formation of an AI forecasting model. Although the objective for both is the prediction of the future, their accuracy will be different based on the dataset size and the measure of complexity of the data. Since the large training dataset is needed for an AI forecasting model, it usually seems to predict more accurate results for complex patterned data with AI than the traditional one.

An Overview on Predictive Models for Forecasting the Future was originally published in Predict on Medium, where people are continuing the conversation by highlighting and responding to this story.

This article covers the basics of artificial intelligence from its definition to its importance, as given below.

1. What is AI?

The Term AI means Artificial Intelligence, which behaves like a machine. It refers to the computer system that can solve complex tasks efficiently by applying human knowledge to make the proper decision based on human reasoning. The system can solve the problem by analysing the data to predict the hidden behaviour of the data and applying different algorithms and models.

2. Types of AI:

Based on the capability and Functionality, the AI can be broadly classified into the following ways:

Based on capabilities, AI can be classified as Narrow AI, General AI and Super AI.

1. Narrow AI: It is also termed as Weak AI. The Narrow AI can perform a narrow or single task. Its performance is often better than human beings, but it can perform only the designated task and not the outside of its designated one. Examples include: Voice assistants like Alexa or Siri, and recommendation engines used by Netflix.

2. General AI: It is also termed as Strong AI. The General AI can perform a wide range of tasks. It can have the capacity of understanding, learning and applying knowledge like human intelligence to perform any intellectual task. Due to it being a theoretical concept, the researchers are working on this type of AI. The Robots that may have the independence to make decisions for any task with any challenges may be an example of General AI.

3. Super AI: Super AI is the ability to perform any task better than a human being from any perspective. It is still a hypothetical concept.

Based on functionalities, AI can be classified as Reactive Machines, Limited Memory in AI, Theory of Mind, and Self-Awareness AI.

1. Reactive Machines: The Reactive Machines make decisions based on the present/current data without having previous experiences. Examples include Google’s AlphaGo and IBM’s Deep Blue.

2. Limited Memory in AI: This type of AI makes decisions based on previous/past data. Limited memory means it has the capacity of limited memory that does not have long-lived memory. An example is self-driving cars.

3. Theory of Mind AI: The Theory of Mind AI emphasises human intelligence like desires, emotions, beliefs etc. to perform a specific task. Still, it is developing, the researchers are working on it.

4. Self-Awareness AI: The Self-Awareness AI is a system which will have human-like abilities about its consciousness, emotional reactions, understandability etc. It is a theoretical concept till now and far from its goal.

3. Who invented/discovered AI?

There are a number of pioneers whose names are related to the invention or discovery of AI. Their time-to-time contributions in the field of research tend to invent AI as given below:

a.Alan Turing (1912–1954): Worked on the Turing Machine and Turing Test.

b.John McCarthy(1927–2011): Developed the LISP Programming Language.

c.Marvin Minsky(1927–2016): Worked on the theory of Machine Learning and Cognition.

d. Allen Newell (1927–1992) and Herbert A. Simon (1916–2001): Created the General Problem Solver (GPS) and different algorithms.

e. Geoffrey Hinton, Yann LeCun, and Yoshua Bengio: Inventors of deep learning.

4. When did AI start?

Although Alan Turing (1912–1954)worked on the Turing Machine and Turing Test but the term “Artificial Intelligence” was first used by John McCarthy in 1956 at the workshop of Dartmouth, New Hampshire.

5. When did AI start in India?

Although earlier research was going on in Mathematics and Computational areas to enhance the development of AI, the actual development of AI was started in India in 1980.

6. Where is AI used?

The different application areas for AI are:

a.Agriculture: To enhance productivity, AI can be used to analyse the data by monitoring soil nature, to detect crop diseases and to predict weather forecasting, which can help in crop growth rate by applying different algorithms. Thus, AI can help in productivity in the field of agriculture.

b.Healthcare: AI can be used to detect and predict human diseases. The AI-powered chatbots and virtual assistants can provide different health-related services from scheduling appointments to health advice services.

c.Education: Nowadays, the Education sector is also immensely influenced by AI, which can help teachers and students in a broader way. It can help them by introducing an intelligent tutoring system, Voice assistants, etc.

d.Entertainment: AI can help with content creation like video games, music and movies. Today, the most popular platforms like YouTube and Netflix also use the AI concept.

e.Security: AI can help in security purposes like cybersecurity, facial recognition and threat detection by using AI-powered drones.

7. How does AI work?

The working principle consists of a number of stages as given below:

a.Input: It is nothing but the data collection stage, where the collected data may be in the form of audio, video, text, images, etc. The performance of an AI system may depend on the quality and quantity of data collected for that specific system.

b.Processing: After the data is collected, the next step is to process the data in terms of manipulation, analysis and interpretation of data. Here, the data is processed to make it normalised, structured and standardised by handling the missing values or removing the data duplicates.

c.Training the Model: The next stage is to select the proper model or algorithm on the processed data to properly predict the outcome of the system. Algorithms like clustering, naïve Bayes classifier, support vector machine, decision tree, logistic regression model, neural network, deep learning model, and many more may be selected based on the dataset pattern and objectives. The accuracy of the system may depend on how the selected model deals with the input data.

d.Testing: In order to perform the testing process, a separate set of data from a similar family is taken into consideration to check whether the system works correctly or not. If it is not, then some updation on the selection of algorithms or any other parameters may be needed until it becomes a proper one.

e.Deployment: It is the stage of deployment of the developed AI system to interact with users or other systems, which can predict or make decisions to solve the problems.

8. What can AI do?

It represents the application areas where the concept of AI is applied. Such application areas are Data analysis and Prediction, Agriculture, Healthcare, Education, Entertainment, and Security. Other than these, some other areas where the tasks can be performed by AI are Speech and Image recognition, Natural Language Processing, Autonomous Vehicles, Robotics and Gaming, etc.

9. AI versus human intelligence

a.Human Intelligence has the capacity for creativity, imagination, awareness, consciousness, thinking, emotion, and decision making, but AI lacks those capacities.

b. AI can make decisions only by analysing the input data as well as patterns, and also by an algorithm which is fed by human beings to the system, but human intelligence can make decisions by the experiences collected from the natural, behavioural and biological development throughout the ages.

c.The task performed by AI is faster than that of human intelligence.

d.The systems generated by AI are digital, but it is analogous to the human brain.

e.AI can solve a specific problem, but human beings can deal with complex situations.

f.Modification of the AI system can be done by updating the input data, and algorithms faster than those of human beings, which can update themselves from their experiences or their knowledge.

g. AI lacks emotion and it acts as a decision maker only based on the input data, pattern and the applied algorithms. But in some practical applications, the emotion of human intelligence may influence the decision-making system.

h. The working boundary of AI is limited, which is already set by the programmer, but such a boundary is not a limit for human intelligence.

i. The result acquired by AI is more accurate than human intelligence.

j.The more and more data that can be handled at a time by AI than by human intelligence.

10. Can AI replace Human Intelligence?

Although the development growth rate of AI is increasing day by day, it cannot be answered positively as AI can replace human intelligence. The base of any AI system is dependent upon human intelligence. No AI system can be a self-generated system. The system can be generated with the interaction of human intelligence, which feeds the data to that system and based on the inherent pattern from that data, the human-designed algorithms/ models are applied to act as a problem solver for specific tasks. After multiple number of tests, an AI system becomes a full-fledged, successful system which can act as a problem solver or decision maker.

11. Can AI be a threat to Human Intelligence?

Since there is a rapid growth of AI, there may be a threat to human intelligence. No AI system is a self-generated system. With the interaction of human intelligence, the AI system is built. The data, algorithms or models are all implemented by human beings to build AI systems. Some of the AI systems are speech recognition systems, Autonomous Vehicles and Robotics, etc. Any AI system is automatic. But if this autonomous behaviour crosses the boundary, the human being may be entirely controlled by AI in future, which may become a threat to human intelligence.

12. Why is AI important in the modern world?

AI is rapidly growing day by day. In the modern era, in many areas, the concept of AI is used to perform specific tasks in order to replace the traditional way of doing the task with human intelligence. The AI system can handle more and more data at a time. It reduces the time required for problem-solving, increases accuracy in outcome, faster decision maker, etc. Some of the application areas are Agriculture, healthcare, education, entertainment, security, and weather forecast, etc.

Fundamentals of Artificial Intelligence (AI) was originally published in Everyday AI on Medium, where people are continuing the conversation by highlighting and responding to this story.