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Simpler Explanation of paper 1

DNA is the smallest part (like the letters). Genes are made up of DNA and are specific sections that tell your body to do certain things (like words). Genome is the entire set of instructions, including all the genes and DNA, that makes your body function (like a sentence or book). Plants, like all living things, have DNA, which is like their instruction manual. DNA tells the plant how to grow, how many beans to make, and other important things.  Scientists are trying to find out which parts of a plant’s instruction manual (DNA) are responsible for how many beans it makes, so they can help grow plants that are better at producing beans. This is known as GAWS.  By knowing which parts of the DNA affect bean production, scientists can help farmers grow plants that produce more beans or have other desirable traits. This is like figuring out how to make a better recipe. The old ways of figuring this out don't always work well, especially for plants that have a lot of similar genes. T
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K-fold cross-validation

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K-fold cross-validation is not an algorithm itself; it is a technique or method used for model evaluation. It helps assess how well a machine learning model performs by splitting the dataset into multiple parts (folds) and repeatedly training and testing the model on different subsets of the data.  For instance, suppose you want to test three algorithms—Algorithm A, Algorithm B, and Algorithm C using a dataset with 10 data points and 5-fold cross-validation. You start by dividing the dataset into 5 folds, each containing 2 data points: Fold 1 (data points 1 and 2), Fold 2 (data points 3 and 4), Fold 3 (data points 5 and 6), Fold 4 (data points 7 and 8), and Fold 5 (data points 9 and 10). For each algorithm, you train the model on 4 of the 5 folds and test it on the remaining fold. You repeat this process for all 5 folds, recording the performance score each time and then averaging these scores. For example, after applying this method: Algorithm A might have an average performance score
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A JOURNEY IN MACHINE LEARNING

A JOURNEY IN MACHINE LEARNING                  Week1 – Week4 Links Pandas Pandas videos  (first 7 videos) Numpy   Numpy videos  (first 3 videos, the last video in the playlist)  Matplot Matplotlib videos  (all videos) Kaggle  5 Kaggle datasets My Learning Netflix_exploratory_data_analysis   Car-data-analysis Market-data-analysis                  Week5 – Week8 Links ML ML videos  (first 21 videos)   Exercise   Exercises    Kaggle Work on Kaggle datasets  My Learning Salary prediction Multiple Linear Regression Dataset Hearing test classification problem HR Analytics Titanic survivors Handwritten digits Drug prediction Iris classification Cancer Prediction Fetal health classification      Week9 – Week12 Links Deep-Learning   Deep Learning videos  (49 videos) Projects Project  (last 7 videos) Kaggle Work on Kaggle datasets  My Learning
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Basics of Neural networks

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  Neural networks  Imagine a group of people who have never seen " KOALA" (an animal) in their life!!! So now our job is to teach them if any image is of "KOALA" or not.... We create a team and ask each and every student to work and study each feature of the koala, such as Mike can work on detecting eyes, Jyoti works on forelimbs, Chen can work on hindlimbs, and Mohan can work on the nose. So they make assumptions by using a score of 0 to 1. For example, where 0 means definitely not Koala's eyes, 0.5 means maybe or maybe not Koala's eyes and 1 means definitely Koala's eyes. Then Serena, their superior notes down their observations and based on a formula tells us finally if an animal is a Koala or not.   This is nothing but a neural network. Each individual person here is an individual neuron. They're working on a particular subtask and pass the result of their subtask to the next group. Here, Serena and Nidhi are the hidden layer, Mike, Mohan, etc fo
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Introduction to Jupyter and libraries

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What is jupyter? Jupyter is the short form of Julia, Python, and R. It's open-source software that creates interactive notebook documents containing live code, equations, visualizations, media, and other computational outputs. Moreover, it supports more than 40 languages as of now. What is pandas? Pandas is an open-source Python library.  It has functions for analyzing, cleaning, exploring, and manipulating data. Pandas has two types of data structures:  a) Series -It's a one-dimensional array with indexes, it stores a single column or row of data in a Data Frame. b) Data Frame - It's a tabular spreadsheet-like structure representing rows containing one or multiple columns. Why use pandas? Pandas allow us to analyze big data and make conclusions based on statistical theories. Pandas can clean messy data sets and make them readable and relevant. The process of cleaning messy data is called data munging or data wrangling.   Data Frame: It represents data using rows and column
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What is a neuron?

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  Linear Regression Sigmoid or Logit function or Activation function. In our houses, we have LED lights that light up on turning on the switch and turn off on turning off the switch so similarly activation function gives yes or no results i.e., 0 or 1 results. In ML the input function (dependent variable) i.e. the age here is called a feature. This oval here represents a neuron. Neuron is basically a combination of linear equations and activation functions. Neural Network Neural network for handwritten digits
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In depth of regression ml

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  On calculating the above example, we figure out that the value of slop =1.5 and the value of intercept (b) = -2. So, if we have a pizza having a diameter of 20 inches then substituting this value in y=mx+c we get y as 28 inches. Not all shopkeepers need to sell the pizza at the same price it may vary, this variation is known as error.
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