Why Scikit-learn is Optimum for Python-based Machine Learning

Why Scikit-learn is Optimum for Python-based Machine Learning

Artificial intelligence (AI) and Machine Learning (ML) are capturing greater business value with powerful algorithms and functionalities. As an emerging [AI development company](https://artificialintelligence.oodles.io/ "AI development company")...

Artificial intelligence (AI) and Machine Learning (ML) are capturing greater business value with powerful algorithms and functionalities. As an emerging AI development company, Oodles AI has witnessed a growing demand for cutting-edge AI solutions across markets. It has propelled the development of significant ML libraries, such as ‘Scikit-learn’ for machine learning applications like predictive analytics, sentiment analysis, and more.

This blog post analyzes the inside out of Scikit-learn and maps effective business applications with the comprehensive ML library.

Significance of Scikit-Learn for Python-based Machine Learning
Scikit-learn is a machine learning library developed in Python. It was initially started as a Google Summer of Code project by David Cournapeau in 2007. The name “Scikit” has its roots attached to the “SciPy”, a scientific computing library developed in 2001. Scikit-learn is developed on top of two such computational libraries and functions, namely SciPy, NumPy, and Matplotlib.

Today, global businesses like JP Morgan and Spotify use Scikit-learn’s ML algorithms, making it the most widely-used Python package.

With expansive implementations of machine learning algorithms, Scikit-learn is considered as the most comprehensive ML library. Its open-source nature makes Scikit-learn easily accessible along with the following features-

a) Efficient Data Analysis
Scikit -learn accelerates the development of ML models with considerable support for large volumes of data mining and analysis.

b) Supervised and Unsupervised learning algorithm
The scikit-learn toolbox is a mixed bag for diverse supervised and unsupervised algorithms. From supervised linear regression and support vector to clustering and principal component analysis under unsupervised neural networks.

c) High Accuracy Rate
With features like cross-validation, scikit-learn offers a high level of accuracy in the development of machine learning solutions.

Python-based Machine Learning Applications using Scikit-learn

  1. Predictive Analytics Models
    With an explosion in digital information and data, today businesses are able to explore new machine learning opportunities. Combined with in-depth analytics, machine learning and artificial intelligence are making strides with accurate, comprehensive, and timely insights.

Contrary to traditional predictive analytics that ran with a considerable time lag, AI-powered predictive modeling with scikit-learn apply the following methodologies-

a) Linear Regression
Linear regression and classification are the two types of supervised machine learning algorithms. While the former is used to predict a continuous value, the latter predicts a discrete value as the output. Scikit-learn’s machine learning library supports both dense and sparse inputs to predict near-real-time values or targets.

scikit-learn machine learning algorithms

b) Classification
The scikit-learn library offers two classification models, namely Logistic Regression (LogReg) and K-Nearest Neighbors (KNN). While LogReg is used to predict multi-class targets, KNN classifies the value based on the nearest numbers.

Under Scikit-learn, Regression and Classification are two important ML algorithms that improve the accuracy and efficiency of predictive analytics models significantly.

Also read- Mobilizing Big Data for Cloud-based Predictive Analytics

Diabetes Predictive System built by Oodles AI
At Oodles, we used scikit-learn’s to implement machine learning algorithms for developing a Diabetes Predictive System. The model predicts diabetes in a person based on several data inputs such as glucose, kin thickness, blood pressure, etc. We applied Scikit-learn’s AdaBoostClasssifier machine learning algorithm along with large volumes of structured datasets.

diabetes predictive system scikit-learn machine learning

Our AI team used various algorithms such as-

a) Linear Regression

b) Logistic Regression

c) Support Vector Machine, and more.

Our Diabetes Prediction System has successfully achieved above 95% accuracy in predicting human diabetes levels without any intervention of physicians.

Also read- Improving Diagnostics with AI-powered Predictive Analytics in Healthcare

  1. Sentiment Analysis Models
    The proliferation of digital communication and socializing channels has given way to a trove of textual information and audience expressions. Businesses can channelize this data to extract actionable insights that strengthen their marketing and decision-making abilities.

The AI behind sentiment analysis enables machines to analyze the ‘sentiment’ or ‘emotion’ underlying words, sentences, social media comments, and articles. The system uses Natural Language Processing (NLP) algorithms to classify opinions as a positive, negative, skeptic, etc. to improve the following business operations-

a) Enhance customer support services and experience using audience insights

b) Augment better Public Relations (PR) practices

c) Formulation of effective marketing strategies

d) Management of business reputation early-warning systems

e) Monitor remote and cross-border customers

Scikit-learn’s machine learning algorithms enable businesses to analyze real-time online customer interactions with logistic regression model development. The library can be easily used to monitor customer behavior across social media channels and messaging applications.

Also read- Potential Business Applications of Sentiment Analysis Across Industries

Deploying Scikit-learn for Machine Learning Applications with Oodles AI
We, at Oodles AI, have hands-on experience in deploying function-specific machine learning algorithms of Scikit-learn to build dynamic AI solutions. Our machine learning capabilities with Scikit-learn extend to text classification, predictions, and NLP systems along with business-oriented cloud machine learning solutions. With adept knowledge in model training for supervised and unsupervised machine learning algorithms, we match the AI requirements of multiple industry businesses.

How Cloud-based Machine Learning Solutions Amplify Business Growth

How Cloud-based Machine Learning Solutions Amplify Business Growth

Artificial Intelligence (AI) has proliferated business infrastructures both on-premise and in the cloud. Extreme cloud computing services have empowered emerging and established businesses to practice cloud-based machine learning techniques...

Artificial Intelligence (AI) has proliferated business infrastructures both on-premise and in the cloud. Extreme cloud computing services have empowered emerging and established businesses to practice cloud-based machine learning techniques proficiently. At Oodles, we are exploring innovative ways to integrate our AI development services in cloud setups to assist business automation significantly.

Let’s discuss how cloud-based machine learning development services accelerate business applications along with a functional predictive model built by Oodles AI.

Significance of Cloud-based Machine Learning Applications
Cloud computing and machine learning are the major driving factors of Industry 4.0. High computational powers and machine learning algorithms together are transforming business intelligence, IT security, and financial trading across global industries. Here are the key benefits of hosting machine learning models in cloud-based infrastructures-

  1. High-Performance Infrastructure
    The struggle of procuring high-capacity hardware and servers is one of the major causes of low business performance. Cloud-based infrastructures provide several data accessing tools using SQL interfaces and boost the data processing capabilities of businesses. Businesses can choose private, public, or hybrid cloud storage options to store, process, and scale databases and train machine learning models effectively.

  2. Cost efficiency
    The ability to run and maintain huge databases comes with a heavy tag. With cloud-based virtual machines (VMs), businesses can avail of high computational services with minimal expenses and resources. It enables businesses to market their ML models quickly and efficiently without paying for enormous data centers, servers, and other equipment.

  3. Scalability
    Businesses are experiencing an explosion of data from terabytes to zettabytes including big data, social media activities, IoT data, and real-time online records. Cloud services enable businesses to resize their application bandwidth and manage the inflow of data streams at scale. Cloud-based vertical and horizontal scalability boosts business performances, handles heavy web traffic, and increases the response time significantly across digital channels.

Building Efficient Predictive Models with Cloud-based Machine Learning
Data is the most valuable asset for any organization. It is with data that businesses are able to run an in-depth analysis of patterns, historical events, and audience inputs. However, traditional analytics techniques are limited in scope and bandwidth to reach the full potential of big data.

Cloud-based machine learning has opened new business opportunities with comprehensive data lake architecture to predict target values.

Cloudtp explains how cloud data lake implements predictive Analytics and machine learning.

Cloud-based machine learning technologies enable businesses to target prospects, support customers, build advanced products, and respond to market needs effectively. Here’s how cloud computing enhances machine learning capabilities of businesses-

a) A cloud-based data lake can be easily integrated with other systems to ensure seamless streaming, pattern matching, running Extract-Transform-Load (ETL) engines, and more.

b) Cloud features such as elasticity, automated recovery, and multi-zone accessibility optimize predictive analytics outcomes to be more accurate and highly contextual.

c) Google Cloud and AWS provide a rich set of trained models to deploy complex machine learning models with ease. Google Prediction APIs quickly identify patterns in large data sets to make optimum predictions and business-oriented recommendations.

How does Oodles AI use cloud-based machine learning platforms for predictive analytics?
We, at Oodles, have experiential knowledge in deploying cloud-based machine learning models to fulfill the artificial intelligence requirements of global businesses. Our most recent machine learning application in the cloud is built on Amazon’s Sagemaker platform. The model training is done with raw CSV data from sensor-based drilling machines and labeled using function-specific ML algorithms. This model’s purpose is to extract actionable insights and make predictions regarding the presence of certain resources under the earth’s surface.

Here’s the infrastructure architecture of the model-

Amazon Sagemaker’s high-volume data processing and scaling capabilities enable us to generate near accurate predictions efficiently.

Besides predictive analytics, our cloud-based machine learning services extend to Google Cloud, IBM Watson, Azure, and AWS Cloud Consulting Services. Our AI team is adept at integrating computer vision, natural language processing, conversational AI, and other machine learning techniques in the cloud.

Consult our AI development team to learn more about our artificial intelligence services.

Machine Learning Full Course - Learn Machine Learning

Machine Learning Full Course - Learn Machine Learning

This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of Machine Learning.

Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial

It covers all the basics of Machine Learning (01:46), the different types of Machine Learning (18:32), and the various applications of Machine Learning used in different industries (04:54:48).This video will help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression (23:38), K Means Clustering (01:26:20), Decision Tree (02:15:15), and Support Vector Machines (03:48:31) are some of the important algorithms you will understand with a hands-on demo. Finally, you will see the essential skills required to become a Machine Learning Engineer (04:59:46) and come across a few important Machine Learning interview questions (05:09:03). Now, let's get started with Machine Learning.

Below topics are explained in this Machine Learning course for beginners:

  1. Basics of Machine Learning - 01:46

  2. Why Machine Learning - 09:18

  3. What is Machine Learning - 13:25

  4. Types of Machine Learning - 18:32

  5. Supervised Learning - 18:44

  6. Reinforcement Learning - 21:06

  7. Supervised VS Unsupervised - 22:26

  8. Linear Regression - 23:38

  9. Introduction to Machine Learning - 25:08

  10. Application of Linear Regression - 26:40

  11. Understanding Linear Regression - 27:19

  12. Regression Equation - 28:00

  13. Multiple Linear Regression - 35:57

  14. Logistic Regression - 55:45

  15. What is Logistic Regression - 56:04

  16. What is Linear Regression - 59:35

  17. Comparing Linear & Logistic Regression - 01:05:28

  18. What is K-Means Clustering - 01:26:20

  19. How does K-Means Clustering work - 01:38:00

  20. What is Decision Tree - 02:15:15

  21. How does Decision Tree work - 02:25:15 

  22. Random Forest Tutorial - 02:39:56

  23. Why Random Forest - 02:41:52

  24. What is Random Forest - 02:43:21

  25. How does Decision Tree work- 02:52:02

  26. K-Nearest Neighbors Algorithm Tutorial - 03:22:02

  27. Why KNN - 03:24:11

  28. What is KNN - 03:24:24

  29. How do we choose 'K' - 03:25:38

  30. When do we use KNN - 03:27:37

  31. Applications of Support Vector Machine - 03:48:31

  32. Why Support Vector Machine - 03:48:55

  33. What Support Vector Machine - 03:50:34

  34. Advantages of Support Vector Machine - 03:54:54

  35. What is Naive Bayes - 04:13:06

  36. Where is Naive Bayes used - 04:17:45

  37. Top 10 Application of Machine Learning - 04:54:48

  38. How to become a Machine Learning Engineer - 04:59:46

  39. Machine Learning Interview Questions - 05:09:03

Google Cloud AutoML for Machine Learning Solutions: Automation Exemplified

Google Cloud AutoML for Machine Learning Solutions: Automation Exemplified

Artificial Intelligence (AI) and Machine Learning (ML) are propelling advanced business solutions across industries. Algorithm-based [machine learning development...

Artificial Intelligence (AI) and Machine Learning (ML) are propelling advanced business solutions across industries. Algorithm-based machine learning development services are overcoming the business challenges of processing heavy data volumes. With accuracy and efficiency, cloud technologies like Google cloud AutoML are encouraging the development of dynamic machine learning solutions. At Oodles, we are testing the model performance of AutoML Natural Language to build domain-specific solutions for global businesses. Let’s explore how cloud AutoML for machine learning solutions is triggering automation beyond human intelligence.

How does Google AutoML Natural Language work?
AutoML Natural Language runs on large volumes of data and algorithms. The cloud service enables businesses to perform supervised machine learning by training a system to identify patterns from labeled data. With AutoML Natural Language service, businesses can easily build custom and cloud-based machine learning solutions for specific tasks like content recognition from textual data. On a deeper level, machine learning in AutoML Natural Language involves three major steps-

  1. Data Preparation
    The first step to build a domain-specific model is to organize data in the form of inputs and answers. While inputs include labeled examples of certain text that a business wants to classify, answers include categories to be predicted by the ML model. A bare minimum of 1000 examples is essential to train the model efficiently.

  2. Evaluation
    After training AutoML Natural Language with rich and goal-oriented data, it is time to evaluate its performance. The main idea is to assess what model’s output on test examples with techniques such as score threshold, true positives and negatives, precision, and recall.

AutoML uses Rest API to generate predictions by analyzing a wide array of data structures over the cloud.

  1. Model Testing
    AutoML has this unique ability to reserve and use 10% of every dataset for testing purposes automatically. However, developers can also perform manual sanity checks by feeding text examples into the text box of the Predict page. Testing with variable example texts enables developers to check what label does the model chooses and why.

AutoML Natural Language Use Cases
a) AutoML Text Classification

b) Sentiment Analysis

c) Entity Extraction

Sorting and processing an explosion of unstructured textual information is a major challenge for digital businesses. With AutoML, businesses can categorize complex digital content such as blogs, articles, news stories, social media posts, etc. AutoML Natural Language supports both native and scanned PDFs in the English language with a training capacity of up to 1 million documents. From eCommerce to healthcare, businesses can use AutoML’s text classification in the following ways-

a) Classify and translate real-time eCommerce customer query data into definite product names to optimize the supply chain.

b) Gauge the sentiment and emotions of users across social media tweets to track and evaluate user feedback on certain business services.

c) Categorizing candidate qualifications from resumes and CVs to save manual labor in sorting and classifying candidate capabilities.

Difference between Google AutoML Natural Language and Natural Language API
Google Cloud AutoML is a gateway for businesses seeking to build personalized machine learning solutions than the standard Natural Language API. Customization is the key differentiator between Google AutoML Natural Language and standard Natural Language API. While Natural Language API proves effective for chatbot development services, AutoML is more essential for driving insights and predictions from textual and user data.

Deploying Cloud AutoML for Machine Learning Solutions with Oodles AI
As we leap into the new year 2020, big data analytics powered by machine learning technologies are poised to generate greater value for businesses. We, at Oodles, are combining our forces with emerging cloud-based technologies to develop business-oriented machine learning solutions.

Our machine learning development services are effective at handling prodigious volumes of business and consumer data to extract actionable insights and predictions. Our team has developed various cloud-based machine learning systems including an automated Diabetic Prediction System that identifies diabetes in patients with over 90% accuracy. In addition, our AI team has experiential knowledge in IBM Watson, Azure, and AWS Cloud consulting services.