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Machine Learning by Learnbay

At its most basic level, machine learning is the study of teaching a computer programme or algorithm how to improve over time on a given task. Machine learning can be seen through the prism of theoretical and mathematical modelling of how this method operates in terms of science. However, the analysis of how to construct applications that exhibit this iterative improvement is more realistic. There are a variety of ways to express this concept, but the three most common are supervised learning, unsupervised learning, and reinforcement learning.<br><br>For more details regarding Machine learning courses in Bangalore, Data analytics courses in Bangalore, Data Science Courses In Bangalore, Ai courses in Bangalore, Best machine learning course online, Big data analytics training in Bangalore etc visit:http://bit.ly/DatascienceCourse

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Machine Learning by Learnbay

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  1. MACHINE MACHINE LEARNING LEARNING

  2. What is Machine Learning? The term Machine Learning was first coined by Arthur Samuelinthe year1959. “A computerprogramissaid tolearn from experience E withrespecttosome classof tasks T and performance measure P if itsperformance attasksin T, asmeasured by P, improveswith experience E.” By Tom M. Mitchell Insimple terms, Machine learning is a subsetof Artificial Intelligence (AI) which enablesmachinestomimic like human

  3. Machine Learning Stages Stage 1: Define the objective of the Problem Statement Stage 2: Data Gathering Stage 3: Data Preparation Stage 4: Exploratory Data Analysis Stage 5: Building a Machine Learning Model Stage 6: Model Evaluation & Optimization Stage 7: Predictions

  4. Machine Learning Types The three approaches in which machine can be learn 1. 2. 3. Supervised Learning Unsupervised Learning Reinforcement Learning

  5. Supervised Learning Inthistechnique we use knownorlabelled data totrainthe machine. The labeled data settrainsmachine tounderstand patternsinthe data. The top algorithms currently being used forsupervised learning are: ·Polynomialregression ·Random forest ·Linearregression ·Logistic regression ·Decisiontrees ·K-nearestneighbors ·Naive Bayes

  6. Types of Supervised Learning Classification : It is a Supervised Learning task where output is having defined labels(discrete value). Regression : It is a Supervised Learning task where output is having continuous value.

  7. Unsupervised Learning Unsupervised learning involvestraining the machine byusing unlabeled data and allowing the modelto actonthatinformationwithout guidance. Inthis type of Machine Learning,the modelisnot fed withlabeled data, asinthe modelhasno clue. Common algorithmsused in unsupervised learning include Clustering Anomaly detection Neural networks

  8. Regression Learning Regression analysis consistsof a setof machine learning methodsthat allowustopredict a continuousoutcome variable (y) based onthe value of one ormultiple predictorvariables(x). The differentregressiontechniques are: ·Linear Regression. ·Logistic Regression. ·Ridge Regression. ·Lasso Regression. ·Polynomial Regression. ·Bayesian Linear Regression.

  9. APPLICATION OF MACHINE LEARNING Image Recognition Speech Recognition Traffic prediction Product recommendations Self-driving cars Email Spam and Malware Filtering Virtual Personal Assistant Online Fraud Detection Stock Market trading Medical Diagnosis

  10. For more such content visit: http://bit.ly/DatascienceCourse Or follow us on our social media platforms: ØFacebook: Learnbay Ø ØInstagram: Learnbay_datascience Ø ØTwitter: Learnbay1 Ø ØLiknkedIn: Learnbay Ø ØYoutube (Tutorials): https://www.youtube.com/channel/UC-ntE_GnjjiUuKYqih9ENYA

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