AI PROJECT CYCLE 1.pptx

This is the ppt for AI project cycle

1. A.I Project Cycle CLASS IX

2. What is A.I project cycle? It is a step-by-step process that a person should follow to develop an AI Project to solve a problem. Let us take some daily examples as project, requiring steps to solve the problem. Making Tea Boil Water . Put Tea Powder Put Milk

3. It mainly has 5 ordered stages which distribute the entire development in specific and clear steps: 

4. PROBLEM SCOPING: Identifying a problem and having a vision to solve it, is called Problem Scoping. Scoping a problem is not that easy as we need to have a deeper understanding so that the picture becomes clearer while we are working to solve it. Problem scoping is the process by which student designers “figure out” the problem that they need to solve . Students identify the key elements or factors to which they need to attend, and also consider the context of the problem.  DATA ACQUISITION: Data Acquisition is  the process of collecting accurate and reliable data to work with . Data acquisition is the second step in the project cycle, we should ensure the data collected is collected from authentic and reliable sources for effective Decision Making. It is  the process of digitizing data from the world around us so it can be displayed, analyzed, and stored in a computer .  DATA EXPLORATION: Data exploration is  the first step of data analysis used to explore and visualize data to uncover insights from the start or identify areas or patterns to dig into more . Data exploration is  a critical step in Artificial Intelligence and Machine Learning . With data exploration, analysts attempt to find patterns and details in large pools of data. MODELLING: Modelling is  the fourth stage of the AI Project Cycle, which deals with creating models from the data . Modelling is the process in which different models based on the visualized data can be created and even checked for the advantages and disadvantages of the model. EVALUATION: Evaluation is the last stage of the AI project cycle 123 .  It is the process of understanding the reliability and performance of an AI model 45 .  After a model has been created and trained, it must be thoroughly tested to determine its efficiency and performance 2 .  Evaluation is done by checking the performance of the AI model against testing data with the correct outcome 5

5. Ques: Now, it’s your turn to describe what you have learnt. Explain the concept of AI project cycle with the help of a suitable example.

6. i. Problem Scoping: Problem Scoping is the first stage of the AI project cycle. In this stage of AI development, problems will be identified.  In AI project cycle everything will be failed if problem scoping is failed or without appropriate problem scoping. Incorrect problem scoping also leads to failure of the project as well. The 4Ws of Problem Scoping: The 4Ws are very helpful in problem scoping. They are: Who?  – Refers that who is facing a problem and who are the stakeholders of the problem What?  – Refers to what is the problem and how you know about the problem Where?  – It is related to the context or situation or location of the problem Why?  – Refers to why we need to solve the problem and what are the benefits to the stakeholders after solving the problem The outcome of problem scoping in ai is the problem statement template.

7. The problem statement template When the above 4Ws are completely filled you need to prepare a summary of these 4Ws. This summary is known as the problem statement template. This template explains all the key points in a single template. So, if the same problem arises in the future this statement helps to resolve it easily. Who? The “Who” block helps you in analyzing the people getting affected directly or indirectly due to it. Under this, you find out who the ‘Stakeholders’ to this problem are and what you know about them. Stakeholders are the people who face this problem and would be benefitted with the solution. Let us fill the “Who” canvas: Who are the stakeholders? What do you know about them?

8. What? Under the “What” block, you need to look into what you have on hand. At this stage, you need to determine the nature of the problem. What is the problem and how do you know that it is a problem? Under this block, you also gather evidence to prove that the problem you have selected actually exists. Newspaper articles, Media, announcements, etc. are some examples. What is the problem? How do you know that it is a problem? Where? Now that you know who is associated with the problem and what the problem actually is; you need to focus on the context/situation/location of the problem. This block will help you look into the situation in which the problem arises, the context of it, and the locations where it is prominent. Let us fill the “Where” canvas! What is the context/ situation the stakeholders experience the problem? Where is the problem located? Why? You have finally listed down all the major elements that affect the problem directly. Now it is convenient to understand who the people that would be benefitted by the solution are; what is to be solved; and where will the solution be deployed. These three canvases now become the base of why you want to solve this problem. Thus, in the “Why” canvas, think about the benefits which the stakeholders would get from the solution and how would it benefit them as well as the society. Let us fill the “Why” canvas! Why will this solution be of value to the stakeholders? How will the solution improve their situation?

9. Who Stakeholders Farmers, Fertilizer Producers, Labours, Tractor Companies What The problem, Issue, Need Determine what will a good time for seeding or crop harvesting? When Context/Situation Decide the mature age for the crop and determine its time Ideal Solution Benefits Take the crop on time and supply against market demand on time Suppose we have selected the theme of Agriculture

10. ii. DATA ACQUISITION: Data:  Data refers to the raw facts, figures, information, or statistics. Acquisition:  Acquisition refers to acquiring data for the project. So, Data Acquisition means Acquiring Data needed to solve the problem. DATA MAY BE THE PROPERTY OF SOMEONE ELSE, AND THE USE OF THAT DATA WITHOUT THEIR PERMISSION IS NOT ACCEPTABLE. But there are some sources from which we can collect data, no hassle whatsoever. Let’s Take a Look:

11. Types of data: Primary Data:  Primary data is the kind of data that is collected directly from the data source. It is real time data and is mostly collected when needed and not stored. Secondary Data:   Secondary data is the data that has been collected in the past by someone else and made available for others to use. Secondary data is usually easily accessible to researchers and individuals because they are shared publicly.

12. Data can be a piece of information or facts and statistics collected together for reference or analysis. Whenever we want an AI project to be able to predict an output, we need to train it first using data. For example, If you want to make an Artificially Intelligent system which can predict the salary of any employee based on his previous salaries, you would feed the data of his previous salaries into the machine. This is the data with which the machine can be trained. Now, once it is ready, it will predict his next salary efficiently. The previous salary data here is known as Training Data while the next salary prediction data set is known as the Testing Data .

13. For better efficiency of an AI project, the Training data needs to be relevant and authentic. In the previous example, if the training data was not of the previous salaries but of his expenses, the machine would not have predicted his next salary correctly since the whole training went wrong. Similarly, if the previous salary data was not authentic, that is, it was not correct, then too the prediction could have gone wrong. Hence…. For any AI project to be efficient, the training data should be authentic and relevant to the problem statement scoped.

14. There could be various ways in which you collect data. Sometimes, you use the internet and try to acquire data for your project from some random websites. Such data might not be authentic as its accuracy cannot be proved. Due to this, it becomes necessary to find a reliable source of data from where some authentic information can be taken. At the same time, we should keep in mind that the data which we collect is open-sourced and not someone’s property. Extracting private data can be an offence. One of the most reliable and authentic sources of information, are the open-sourced websites hosted by the government. These government portals have general information collected in suitable format which can be downloaded and used wisely. Some of the open-sourced Govt. portals are: data.gov.in, india.gov.in

15. System Maps: Components of Water Cycle:

16. iii. DATA EXPLORATION: Data Exploration is the process of arranging the gathered data uniformly for a better understanding. Data exploration, also known as  exploratory data analysis  (EDA) Data can be arranged in the form of a table, plotting a chart, or making a database. Data exploration tools make data analysis easier to present and understand through interactive, visual elements, making it easier to share and communicate key insights . There are two main types of data exploration tools and techniques: manual data exploration and automated data exploration.

17. Line Chart: Line charts are resoundingly popular for a range of business use cases because they demonstrate an overall trend swiftly and concisely, in a way that’s hard to misinterpret. In particular, they’re good for depicting trends for different categories over the same period of time, to aid comparison. Bar & Column chart: Both the  Bar  and the  Column  charts display data using rectangular bars where the length of the bar is proportional to the data value. Both charts compare two or more values. However, the difference lies in their orientation. A bar chart is oriented horizontally, whereas a column chart is oriented vertically.

18. Pie charts: A pie chart is a type of a chart that visually displays data in a circular graph. Pie charts can be helpful for showing the relationship of parts to the whole when there are a small number of levels. Tables: Tables are used to organize data that is too detailed or complicated to be described adequately in the text, allowing the reader to quickly see the results. Infographics: An infographic example is a visual representation of information. Infographics examples include a variety of elements, such as images, icons, text, charts, and diagrams to convey messages at a glance.

19. MODELLING: 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. RULE BASED MODEL: Rule Based Approach Refers to the AI modelling where the relationship or patterns in data are defined by the developer. The machine follows the rules or instructions mentioned by the developer, and performs its task accordingly. For example, suppose you have a dataset comprising of 100 images of apples and 100 images of bananas. To train your machine, you feed this data into the machine and label each image as either apple or banana. Now if you test the machine with the image of an apple, it will compare the image with the trained data and according to the labels of trained images, it will identify the test image as an apple. This is known as Rule based approach. The rules given to the machine in this example are the labels given to the machine for each image in the training dataset.

20. Learning based model: Refers to the AI modelling where the relationship or patterns in data are not defined by the developer. In this approach, random data is fed to the machine, and it is left on the machine to figure out patterns and trends out of it. Generally, this approach is followed when the data is unlabeled and too random for a human to make sense out of it. Thus, the machine looks at the data, tries to extract similar features out of it and clusters same datasets together. In the end as output, the machine tells us about the trends which it observed in the training data. For example, suppose you have a dataset of 1000 images of random stray dogs of your area. Now you do not have any clue as to what trend is being followed in this dataset as you don’t know their breed, or colour or any other feature. Thus, you would put this into a learning approach- based AI machine and the machine would come up with various patterns it has observed in the features of these 1000 images. It might cluster the data on the basis of colour , size, fur style, etc. It might also come up with some very unusual clustering algorithm which you might not have even thought of

21. Rule based v/s Learning based Approach:

22. Decision Trees: A decision tree is a tree-like structure that represents a series of decisions and their possible consequences.  .They are an example of a rule based approach . The basic structure of a Decision Tree starts from the root which the point where the decision tree starts. From there, the tree diverges into multiple directions with the help of arrows called branches. These branches depict the condition because of which the tree diverges. In the end, the final decision is where the tree ends. These decisions are termed as the leaves of the tree. You would realize that this looks like an upside-down tree. A decision tree simply asks a question, and based on the answer (Yes/No), it further split the tree into subtrees. In a Decision tree, there are two nodes, which are the  Decision Node  and  Leaf Node.  Decision nodes are used to make any decision and have multiple branches, whereas Leaf nodes are the output of those decisions and do not contain any further branches. Why use decision trees? Decision Trees usually mimic human thinking ability while making a decision, so it is easy to understand. The logic behind the decision tree can be easily understood because it shows a tree-like structure.

23. Decision Tree Terminologies Root Node:  Root node is from where the decision tree starts. It represents the entire dataset, which further gets divided into two or more homogeneous sets. Leaf Node:  Leaf nodes are the final output node, and the tree cannot be segregated further after getting a leaf node. Splitting:  Splitting is the process of dividing the decision node/root node into sub- nodes according to the given conditions. Branch/Sub Tree:  A tree formed by splitting the tree. Pruning:  Pruning is the process of removing the unwanted branches from the tree. Parent/Child node:  The root node of the tree is called the parent node, and other nodes are called the child nodes.

24. Machine Learning : Machine learning is an application of AI. Machine learning aims to teach a machine how to perform a specific task and provide accurate results by identifying patterns. This enables a computer system to continue learning and improving on its own based on experience. Types of Machine Learning: Supervised learning It is a type of machine learning that uses labeled data to train machine learning models. In labeled data, the output is already known. The model just needs to map the inputs to the respective outputs.  An example of supervised learning is to train a system that identifies the image of an animal.  Example, you can see that we have our trained model that identifies the picture of a cat. Application of Supervised Learning: Weather forecasting, Sales forecasting

25. Unsupervised Learning: Unsupervised learning is a type of machine learning that uses unlabeled data to train machines. Unlabeled data doesn’t have a fixed output variable. The model learns from the data, discovers the patterns and features in the data, and returns the output.  Depicted below is an example of an unsupervised learning technique that uses the images of vehicles to classify if it’s a bus or a truck. The model learns by identifying the parts of a vehicle, such as a length and width of the vehicle, the front, and rear end covers, roof hoods, the types of wheels used, etc. Based on these features, the model classifies if the vehicle is a bus or a truck. Application: One of the applications of unsupervised learning is customer segmentation. Based on customer behavior, likes, dislikes, and interests, you can segment and cluster similar customers into a group. Reinforcement Learning: Reinforcement learning follows trial and error methods to get the desired result. After accomplishing a task, the agent receives an award. An example could be to train a dog to catch the ball. If the dog learns to catch a ball, you give it a reward, such as a biscuit. Reinforcement Learning methods do not need any external supervision to train models. Reinforcement learning problems are reward-based. For every task or for every step completed, there will be a reward received by the agent. If the task is not achieved correctly, there will be some penalty added.  Applications: Reinforcement learning algorithms are widely used in the gaming industries to build games. It is also used to train robots to do human tasks.

26. Deep Learning: Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.  Using deep learning computers process data in a way that is inspired by the human brain. Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions.