AI PROJECTppt.pptx
- 1. CAPSTONE PROJECT IDEAS BY- ANKUSH KASHYAP
- 2. CAPSTONE PROJECT The final project of an academic program, typically integrating all of the learning from the program is called the Capstone Project. A capstone project is a project where students must research a topic independently to find a deep understanding of the subject matter. It gives an opportunity for the student to integrate all their knowledge and demonstrate it through a comprehensive project.
- 3. OBJECTIVES 1. Students should apply their learning in solving real world problems 2. Students should be comfortable in expressing their solution in non-technical words 3. Students should be in a position to choose and apply right algorithm to solve the problem
- 4. THESE ARE SOME SIMPLE CAPSTONE PROJECT IDEAS 1. Stock Prices Predictor 2. Develop A Sentiment Analyzer 3. Movie Ticket Price Predictor 4. Students Results Predictor 5. Human Activity Recognition using Smartphone Data set 6. Classifying humans and animals in a photo
- 5. 1. Understanding The Problem Artificial Intelligence is perhaps the most transformative technology available today. At a high level, every AI project follows the following six steps: 1. Problem definition i.e. Understanding the problem 2. Data gathering 3. Feature definition 4. AI model construction 5. Evaluation & refinements 6. Deployment
- 6. 2. Decomposing The Problem Through DT Framework Design Thinking is a design methodology that provides a solution- based approach to solving problems. It’s extremely useful in tackling complex problems that are ill-defined or unknown.
- 7. The five stages of Design Thinking are as follows:
- 8. 3. Analytic Approach Those who work in the domain of AI and Machine Learning solve problems and answer questions through data every day. They build models to predict outcomes or discover underlying patterns, all to gain insights leading to actions that will improve future outcomes.
- 10. ● If the question is to determine probabilities of an action, then predictive model might used. ● If the question is to show relationships, a descriptive approach maybe be required. ● Statistical analysis applies to problems that require counts: if the question requires a yes/ no answer, then a classification approach to predicting a response would be suitable.
- 12. Prior to undertaking the data collection and data preparation stages of the methodology, it's vital to define the data requirements for decision-tree classification. This includes identifying the necessary data content, formats and sources for initial data collection. Techniques such as descriptive statistics and visualization can be applied to the data set, to assess the content, quality, and initial insights about the data. Gaps in data will be identified and plans to either fill or make substitutions will have to be made.
- 13. 5. Modeling Approach Data Modeling focuses on developing models that are either descriptive or predictive. ● An example of a descriptive model might examine things like: if a person did this, then they're likely to prefer that. ● A predictive model tries to yield yes/no, or stop/go type outcomes. These models are based on the analytic approach that was taken, either statistically driven or machine learning driven.
- 15. 6. How to validate model quality 6.1 Train-Test Split Evaluation a. Prerequisites for Train and Test Data b. Splitting
- 17. 7. Metrics of model quality by simple Math and examples All the algorithms in machine learning rely on minimizing or maximizing a function, which we call “objective function”. The group of functions that are minimized are called “loss functions”. A loss function is a measure of how good a prediction model does in terms of being able to predict the expected outcome. A most commonly used method of finding the minimum point of function is “gradient descent”. Think of loss function like undulating mountain and gradient descent is like sliding down the mountain to reach the bottom most point.
- 19. 7.1 RMSE (Root Mean Squared Error) In machine Learning when we want to look at the accuracy of our model we take the root mean square of the error that has occurred between the test values and the predicted values mathematically
- 20. 7.2 MSE (Mean Squared Error) Mean Square Error (MSE) is the most commonly used regression loss function. MSE is the sum of squared distances between our target variable and predicted values.