tag:blogger.com,1999:blog-75541676014770915172024-04-05T06:39:32.050-07:00TUTORIALTPOINT- Java Tutorial, C Tutorial, DBMS TutorialTutorialpoint, javatpoint, corejava tutorial, c tutorial, c++ turorial, c programs, java programs dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.comBlogger540125tag:blogger.com,1999:blog-7554167601477091517.post-56737894388088433412024-04-05T06:38:00.000-07:002024-04-05T06:38:46.423-07:00NumPy Joining Array In NumPy, joining arrays refers to combining the elements of multiple arrays into a single new array. There are two main ways to achieve this:Concatenation: This involves joining arrays along a specified axis. The most common function for concatenation is np.concatenate.
It takes a sequence of arrays as its first argument and optionally the
axis along which to join them. By default, dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-78559721908340935252024-04-05T06:30:00.000-07:002024-04-05T06:30:09.857-07:00NumPy Array IteratingYou can go through NumPy arrays in two main ways: 1. Using a for loop: This is the easiest way to go through each item in a NumPy collection. You can just do a loop over the array, and each time you do that, you'll be able to reach the current element.2. Indexing with a for loop: You can also use a for loop and indexing to go through the parts of a NumPy array. This method works well when you dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-76357704739253985702024-03-01T16:37:00.000-08:002024-03-01T16:37:50.245-08:00Pruning in Decision Tree Pruning is a method employed in decision tree algorithms to avoid overfitting and enhance the model's generalization capacity. Overfitting happens when a decision tree is too intricate and collects irrelevant details in the training data instead of the fundamental patterns in the data. Pruning is eliminating tree components that lack substantial predictive value, resulting in a more dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-10557640160466341962024-02-22T09:25:00.000-08:002024-02-22T09:25:22.285-08:00Basic algorithm for decision tree induction.Given a sample dataset on patients visitation to a general clinic, we want to build a classification model to immediately classify a new patient based on the diagnosis of the conditions and symptoms presented. The dataset is given below. Construct a Decision Tree based on this data, using the basic algorithm for decision tree induction.Solution:dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-53643192555422996192024-02-22T09:13:00.000-08:002024-02-22T09:13:51.217-08:00ID3 Algorithm Decision Tree Solved Example Machine LearningProblem Definition:
Build a decision tree using ID3 algorithm for the
given training data in the table (Buy Computer data), and predict the
class of the following new example: age<=30, income=medium, student=yes, credit-rating=fair
ageincomestudentCredit ratingBuys computer<=30highnofairno<=30highnoexcellentno31…40highnofairyes>40mediumnofairyes>40lowyesfairyes>dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-16449884243754879922024-02-22T08:58:00.000-08:002024-02-22T08:58:41.794-08:00How to calculate Entropy and Information Gain Entropy:To
Define Information Gain precisely, we begin by defining a measure which
is commonly used in information theory called Entropy. Entropy
basically tells us how impure a collection of data is. The term impure
here defines non-homogeneity. In other word we can say, “Entropy is the
measurement of homogeneity. It returns us the information about an
arbitrary dataset that how impure/dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-18056655170624925822024-02-22T08:46:00.000-08:002024-02-22T08:46:27.743-08:00Appropriate Problems For Decision Tree Learning1. Instances are represented by attribute-value pairs.
“Instances are described by a fixed set of attributes (e.g.,
Temperature) and their values (e.g., Hot). The easiest situation for
decision tree learning is when each attribute takes on a small number of
disjoint possible values (e.g., Hot, Mild, Cold). However, extensions
to the basic algorithm allow handling real-valued attributes asdodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-9785465335565727512024-02-22T08:34:00.000-08:002024-03-01T16:38:29.804-08:00Decision Tree Learning in Machine Learning What is a decision tree?
A decision tree is a tree-like structure where each internal node represents a feature (attribute) of the data, and each branch represents a decision rule based on that feature. The leaves of the tree represent the predicted outcome or class label.
Here's an example of a decision tree for predicting whether someone will buy a car: In this example, the root nodedodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-38163581652433709072024-02-21T15:56:00.000-08:002024-02-21T15:56:31.605-08:00An Overview of Supervised Machine Learning Models Supervised learning in machine learning is an algorithm attempting to model a target variable based on provided input data. The algorithm is provided with a collection of training data that includes labels. The program will derive a rule from a large dataset to predict labels for fresh observations. Supervised learning algorithms are given historical data and tasked with identifying the dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-39198108433566761012024-02-21T15:30:00.000-08:002024-02-21T15:31:56.211-08:00The main evaluation metrics for regression and classificationEvaluation CriteriaEvaluating machine learning algorithms using metrics is crucial. The selection of metrics impacts the evaluation and comparison of machine learning algorithms. The measurements impact how you prioritize certain aspects in the results and your final algorithm selection.The main evaluation metrics for regression and classification are illustrated in below figure 1. The mean dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-74239442358788244642024-02-20T07:58:00.000-08:002024-02-20T08:00:23.352-08:00Array Reshaping NumPy Reshaping arrays is a fundamental operation in NumPy. It allows you to change the way elements are arranged within the array without modifying the actual data. Here's a breakdown of the most common methods:
1. Using reshape:
This is the most versatile method, taking the original array and a new shape as arguments.
The new shape must be compatible with the total number of elements in the dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-22725251622422596892024-02-20T07:48:00.000-08:002024-02-20T07:48:08.887-08:00shape of a NumPy arrayThe shape of a NumPy array refers to the number of elements along each of its dimensions. In simpler terms, it specifies how many rows and columns a two-dimensional array has, or how many rows, columns, and depth a three-dimensional array has, and so on.
For example, consider a 2D array representing a table of values:
[[1, 2, 3],
[4, 5, 6],
[7, 8, 9]]
This array has 3 rows (each containing 3 dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-55887130645882697482024-02-19T07:31:00.000-08:002024-02-19T07:31:58.777-08:00NumPy Array Copy vs View It is essential to comprehend the distinction between copying and viewing arrays in NumPy to ensure optimal memory management and prevent unforeseen behavior. Here is an analysis of the main ideas:Copy: Generates a fresh array with a distinct data buffer.Changes made to the duplicate do not impact the initial array, and conversely.Beneficial for data isolation, sending arrays to functions dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-72161564481812493122024-01-25T04:21:00.000-08:002024-01-25T04:21:19.829-08:00Prepare a Classification model using decision tree Classifier.A decision tree is a flowchart-like tree structure where an internal node represents a feature(or attribute), the branch represents a decision rule, and each leaf node represents the outcome. The topmost node in a decision tree is known as the root node. It learns to partition on the basis of the attribute value. It partitions the tree in a recursive manner called recursive partitioning. This dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-5367385257395314982023-12-04T16:38:00.000-08:002023-12-04T16:38:14.820-08:00Perform clustering using k-means clustering algorithm.K-means clusteringK-means is an unsupervised learning method for clustering data points. The algorithm iteratively divides data points into K clusters by minimizing the variance in each cluster.Here, we will show you how to estimate the best value for K using the elbow method, then use K-means clustering to group the data points into clusters.How does it work?First, each data point is Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-8500768677591581762023-11-30T16:11:00.000-08:002023-11-30T16:21:09.612-08:00Build a prediction model to perform logistic regression.Logistic regression aims to solve classification problems. It does
this by predicting categorical outcomes, unlike linear regression that
predicts a continuous outcome.Logistic regression is a statistical method used for binary classification tasks. It predicts the probability of an event occurring, such as whether an email is spam or not, or whether a customer will churn or not. Logistic dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-92046265443292558862023-11-30T15:58:00.000-08:002023-12-09T07:20:56.613-08:00Build a prediction model for multiple linear regression.Multiple linear regression (MLR) is a statistical technique that uses multiple explanatory/Independent variables to predict the outcome of a response variable. It is a generalization of simple linear regression, which uses only one explanatory variable. MLR is a powerful tool that can be used to model complex relationships between variables, and it is widely used in a variety of fields, includingdodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.comtag:blogger.com,1999:blog-7554167601477091517.post-80810135199143762362023-11-25T18:54:00.000-08:002023-11-26T05:54:14.962-08:00Build a prediction model for simple linear regression Simple Linear Regression is a type of Regression algorithms that
models the relationship between a dependent variable and a single
independent variable. The relationship shown by a Simple Linear
Regression model is linear or a sloped straight line, hence it is called
Simple Linear Regression.
The key point in Simple Linear Regression is that the dependent variable must be a continuous/Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-72525365353652776912023-11-24T21:00:00.000-08:002023-12-09T07:19:31.435-08:00Cloud-based machine learning (ML) platformsCloud-based machine learning (ML) platforms provide a convenient and scalable way to develop, train, and deploy ML models without the need for extensive infrastructure and expertise. These platforms offer a variety of features, including:
Managed infrastructure: The platform handles the provisioning, management, and scaling of computing resources, such as CPUs, GPUs, and storage, eliminating dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-33943027237669634732023-11-24T20:57:00.000-08:002023-11-24T20:57:50.721-08:00Popular machine learning librariesThere are numerous popular machine learning libraries available, each offering a unique set of features and capabilities. Here are some of the most widely used and well-regarded libraries across different programming languages:
Python:
TensorFlow: TensorFlow is a powerful and versatile open-source library for developing and deploying deep learning models. It offers a high-level API for buildingdodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-2122389828257021662023-11-24T20:55:00.000-08:002023-11-24T20:55:32.610-08:00Programming languages for machine learningSeveral programming languages are widely used for machine learning (ML) applications, each with its strengths, weaknesses, and suitability for different tasks. Here's an overview of the most popular programming languages for ML:
Python: Python is a versatile and widely used language for ML due to its simplicity, readability, and extensive libraries. It offers a rich ecosystem of ML libraries, dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-37929292253206384212023-11-24T20:51:00.000-08:002023-11-24T20:51:38.302-08:00Challenges of machine learningDespite the remarkable advancements in machine learning, there are still several challenges that need to be addressed to fully realize its potential. These challenges encompass various aspects, from data-related issues to ethical considerations.
Data Challenges:
Data Quality and Availability: Machine learning models rely heavily on high-quality data for accurate and reliable predictions. dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-84669091064411859732023-11-24T20:49:00.000-08:002023-11-24T20:49:44.573-08:00Benefits of machine learningMachine learning (ML) has revolutionized various industries and aspects of our lives, offering numerous benefits that enhance efficiency, productivity, and decision-making. Here are some of the key benefits of machine learning:
Automated Decision-Making: ML algorithms can analyze vast amounts of data and identify patterns, enabling automated decision-making processes. This automation reduces dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-49633884386615153762023-11-24T20:45:00.000-08:002023-11-24T20:45:28.588-08:00Machine Learning Steps - Steps to build a machine learning modelThe task of imparting intelligence to machines seems daunting and
impossible. But it is actually really easy. It can be broken down into 7
major steps :
1. Collecting Data:
As you know, machines initially learn from the data
that you give them. It is of the utmost importance to collect reliable
data so that your machine learning model can find the correct patterns.
The quality of the data dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0tag:blogger.com,1999:blog-7554167601477091517.post-30962910992935837092023-11-24T20:24:00.000-08:002023-11-24T20:24:10.704-08:00Data set - Types of data sets A dataset is a collection of data that is used for analysis or training a
machine learning model. Data sets can be of different types, depending
on the type of data they contain and the way they are organized.Types of Datasets
In Machine Learning while training a model we often encounter the problem of over-fitting and underfitting.
In order to overcome the situation, we need to divide dodda venkatareddyhttp://www.blogger.com/profile/00089604532905760893noreply@blogger.com0