This is a blog by Dr M Khalid Munir, for kids to understand science easily using videos , images and descriptions. Use the search box to search for a topic. Comments and suggestions are welcome. Mail me at medlifeasia@gmail.com
Interesting experiment to show effect of heat on magnetic effect on a needle
SMART goals stands for Specific, Measurable, Achievable, Relevant, and Time-Bound. Defining these parameters help you as they help to ensure that your objectives are attainable within a certain time frame. Example1: If a class 8 student is working on a science project about plant growth. Here's how they could apply SMART goals: Specific: The student's goal could be to investigate the effect of different types of soil on the growth of bean plants. Measurable: The student could measure plant growth by recording the height of the plants every week for a month. Achievable: The student ensures they have access to different types of soil and necessary materials for planting and measuring growth within their means. Relevant: The project aligns with the curriculum and aims to deepen the student's understanding of plant biology and environmental science. Time-bound: The student sets a timeline to complete the project within a certain number of weeks, with milestones for planting,...
A. Here’s an analogy using a science fair project to explain the AI project cycle : 1. Problem Scoping → Choosing a Science Fair Project Topic Before starting a science project, you first decide what problem you want to solve. For example, "How can we make plants grow faster?" 2. Data Acquisition → Collecting Materials & Information You gather what you need—seeds, soil, water, and information on plant growth. In AI, this step is like collecting data from various sources. 3. Data Exploration → Observing and Preparing the Data Before planting, you check if your soil is good, your seeds are healthy, and if anything needs to be adjusted. In AI, this is where you clean and analyze data to make sure it’s useful. 4. Modelling → Conducting the Experiment You plant seeds in different conditions (e.g., more sunlight, less water) and observe the growth. In AI, this is like training different models to see which one works best. 5. Evaluation → Checking the Results After ...
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