AI Project Cycle Class 10
The AI Project Cycle is a step-by-step process that a company must follow in order to derive value from an AI project and to solve the problem.
The AI Project Cycle offers us a suitable framework that can guide us in the right direction. The AI project cycle consists of five main stages:
Starting with problem scoping, you define the objective of your AI project by identifying the issue that you hope to address. When problem scoping, we consider several factors that have an impact on the issue we’re trying to address to make the situation more evident.
● You need to acquire data which will become the base of your project as it will help you in understanding what the parameters that are related to problem scoping are.
● You go for data acquisition by collecting data from various reliable and authentic sources. Since the data you collect would be in large quantities, you can try to give it a visual image of different types of representations like graphs, databases, flow charts, maps, etc. This makes it easier for you to interpret the patterns which your acquired data follows.
● After exploring the patterns, you can decide upon the type of model you would build to achieve the goal. For this, you can research online and select various models which give a suitable output.
● You can test the selected models and figure out which is the most efficient one.
● The most efficient model is now the base of your AI project and you can develop your algorithm around it.
● Once the modelling is complete, you now need to test your model on some newly fetched data. The results will help you in evaluating your model and improving it.
● Finally, after evaluation, the project cycle is now complete and what you get is your AI project.
It is a fact that we are surrounded by problems. They could be small or big, sometimes ignored or sometimes even critical. Many times, we become so used to a problem that it becomes a part of our life. Identifying such a problem and having a vision to solve it, is what Problem Scoping is about.
The Sustainable Development Goals are a set of 17 objectives that the United Nations has stated. These objectives are meant to be accomplished by the year 2030. Every UN member state has made a commitment to doing this.
These sustainable development objectives line up with the issues that we might also see in our immediate environment. Such issues should be sought out and addressed because doing so would improve the lives of many people and advance the objectives of our nation.
4Ws Problem Canvas
A problem can be difficult to scope since we need to have a deeper understanding of it in order to have a clearer image when trying to solve it. Hence, we use the 4Ws Problem Canvas to help us out.
The 4 W’s of Problem Scoping are Who, What, Where, and Why. This 4 W’s helps to identify and understand the problem in a better manner.
a. Who – The “Who” element helps us to understand and categorize who is directly and indirectly affected by the problem, and who are known as Stakeholders.
b. What – The “What” section aids us in analyzing and recognizing the nature of the problem, and you may also gather evidence to establish that the problem you’ve chosen exists under this block.
c. Where – What is the situation, and where does the problem arise.
d. Why – Refers to why we need to address the problem and what the advantages will be for the stakeholders once the problem is solved.
Problem Statement Template with space to fill details according to your Goal:
The method of collecting accurate and trustworthy data to work with is referred to as data acquisition. Data can be acquired from a variety of sources, including websites, journals, newspapers, and other media, such as text, video, photographs, and audio.
Dataset is a collection of data in tabular format. Dataset contains numbers or values that are related to a specific subject. For example, students’ test scores in a class is a dataset.
The dataset is divided into two parts
a. Training dataset – Training dataset is a large dataset that teaches a machine learning model. Machine learning algorithms are trained to make judgments or perform a task through training datasets. Maximum part of the dataset comes under training data (Usually 80%)
b. Test dataset – Data that has been clearly identified for use in tests, usually of a computer program, is known as test data. 20% of data used in test data
Recheck your problem description and try to identify the data attributes needed to solve this challenge. The type of data you want to collect is referred to as data features. The salary amount, increase percentage, increment time, bonus, etc. are examples of data features.
There can be various ways in which you can collect data. Some of them are:
You must have noted while collecting data that it is a complicated thing; it is full of numbers, and in order to make sense of it, one needs identify certain patterns in it.
For Example, if you go to the library and choose a random book, you try to rapidly go through its content by turning pages and reading the description before you decide to borrow it for yourself. This helps you determine whether or not the book is suitable for your requirements and interests this is data exploration.
To analyze the data, you need to visualize it in some user-friendly format so that you can:
● Quickly get a sense of the trends, relationships and patterns contained within the data.
● Define strategy for which model to use at a later stage.
● Communicate the same to others effectively. To visualize data, we can use various types of visual representations.
An AI model is a program that has been trained to recognize patterns using a set of data. AI modeling is the process of creating algorithms, also known as models, that may be educated to produce intelligent results. This is the process of programming code to create a machine artificially.
Generally, AI models can be classified as follows:
Rule Based Approach
AI modelling in which the developer sets the rules. The machine executes its duty in accordance with the rules or instructions specified by the developer.
Learning Based Approach
AI modelling in which the computer learns on its own. The AI model is trained on the data provided to it under the Learning Based technique, and after that, it is able to create a model that is flexible to the change in data.
The learning-based approach can further be divided into three parts:
1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning
Supervised Learning – In a supervised learning model, the dataset which is fed to the machine is labelled. In
other words, we can say that the dataset is known to the person who is training the machine only then he/she is able to label the data.
Unsupervised Learning – Such models work on continuous data. For example, if you wish to predict your next salary, then you would put in the data of your previous salary, any increments, etc., and would train the
model. Here, the data which has been fed to the machine is continuous.
Unsupervised Learning – An unsupervised learning model works on unlabelled dataset. This means that the data which is fed to the machine is random and there is a possibility that the person who is training the model does not have any information regarding it.
Unsupervised learning models can be further divided into two categories:
Clustering – Refers to the unsupervised learning algorithm which can cluster the unknown data according to the patterns or trends identified out of it. The patterns observed might be the ones which are known to the developer or it might even come up with some unique patterns out of it.
Dimensionality Reduction – We humans are able to visualise upto 3-Dimensions only but according to a lot of theories and algorithms, there are various entities which exist beyond 3-Dimensions. For example, in Natural language Processing
Once a model has been created and trained, it must undergo appropriate testing in order to determine the model’s effectiveness and performance. Thus, the model is evaluated using Testing Data (which was taken from the dataset generated at the Data Acquisition stage), and the effectiveness of the model is determined using the parameters listed below –
Neural networks are based in part on how neurons function in the human brain. The main benefit of neural networks is their ability to automatically extract data features without the assistance of a programmer.
Employability skills Class 10 Notes
- Unit 1- Communication Skills Class 10 Notes
- Unit 2- Self-Management Skills Class 10 Notes
- Unit 3- Basic ICT Skills Class 10 Notes
- Unit 4- Entrepreneurial Skills Class 10 Notes
- Unit 5- Green Skills Class 10 Notes
Employability skills Class 10 MCQ
- Unit 1- Communication Skills Class 10 MCQ
- Unit 2- Self-Management Skills Class 10 MCQ
- Unit 3- Basic ICT Skills Class 10 MCQ
- Unit 4- Entrepreneurial Skills Class 10 MCQ
- Unit 5- Green Skills Class 10 MCQ
Employability skills Class 10 Questions and Answers
- Unit 1- Communication Skills Class 10 Questions and Answers
- Unit 2- Self-Management Skills Class 10 Questions and Answers
- Unit 3- Basic ICT Skills Class 10 Questions and Answers
- Unit 4- Entrepreneurial Skills Class 10 Questions and Answers
- Unit 5- Green Skills Class 10 Questions and Answers
Artificial Intelligence Class 10 Notes
- Unit 1 – Introduction to Artificial Intelligence Class 10 Notes
- Unit 2 – AI Project Cycle Class 10 Notes
- Unit 3 – Natural Language Processing Class 10 Notes
- Unit 4 – Evaluation Class 10 Notes
- Advanced Python Class 10 Notes
- Computer Vision Class 10 Notes
Artificial Intelligence Class 10 MCQ
- Unit 1 – Introduction to Artificial Intelligence Class 10 MCQ
- Unit 2 – AI Project Cycle Class 10 MCQ
- Unit 3 – Natural Language Processing Class 10 MCQ
- Unit 4 – Evaluation Class 10 MCQ