PYTHON 2022 - Machine Learning, NLP & Python-Cut to the Chase | Simpliv
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Website https://www.simpliv.com/python/from-0-to-1-machine-learning-nlp-python-cut-to-the-chase |
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Category Python
Deadline: December 30, 2022 | Date: December 30, 2022
Venue/Country: Online Courses, U.S.A
Updated: 2018-05-07 16:17:10 (GMT+9)
Call For Papers - CFP
Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. This course is a down-to-earth, shy but confident take on machine learning techniques that you can put to work todayLet’s parse that.The course is down-to-earth : it makes everything as simple as possible - but not simplerThe course is shy but confident : It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.You can put ML to work today : If Machine Learning is a car, this car will have you driving today. It won't tell you what the carburetor is.The course is very visual : most of the techniques are explained with the help of animations to help you understand better.This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python.The course is also quirky. The examples are irreverent. Lots of little touches: repetition, zooming out so we remember the big picture, active learning with plenty of quizzes. There’s also a peppy soundtrack, and art - all shown by studies to improve cognition and recall.What's Covered:Machine Learning: Supervised/Unsupervised learning, Classification, Clustering, Association Detection, Anomaly Detection, Dimensionality Reduction, Regression.Naive Bayes, K-nearest neighbours, Support Vector Machines, Artificial Neural Networks, K-means, Hierarchical clustering, Principal Components Analysis, Linear regression, Logistics regression, Random variables, Bayes theorem, Bias-variance tradeoffNatural Language Processing with Python: Corpora, stopwords, sentence and word parsing, auto-summarization, sentiment analysis (as a special case of classification), TF-IDF, Document Distance, Text summarization, Text classification with Naive Bayes and K-Nearest Neighbours and Clustering with K-MeansSentiment Analysis: Why it's useful, Approaches to solving - Rule-Based , ML-Based , Training , Feature Extraction, Sentiment Lexicons, Regular Expressions, Twitter API, Sentiment Analysis of Tweets with PythonMitigating Overfitting with Ensemble Learning:Decision trees and decision tree learning, Overfitting in decision trees, Techniques to mitigate overfitting (cross validation, regularization), Ensemble learning and Random forestsRecommendations: Content based filtering, Collaborative filtering and Association Rules learningGet started with Deep learning: Apply Multi-layer perceptrons to the MNIST Digit recognition problemA Note on Python: The code-alongs in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for both Python 2 and Python 3 wherever possibleWho is the target audience?Yep! Analytics professionals, modelers, big data professionals who haven't had exposure to machine learningYep! Engineers who want to understand or learn machine learning and apply it to problems they are solvingYep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learningYep! Tech executives and investors who are interested in big data, machine learning or natural language processingYep! MBA graduates or business professionals who are looking to move to a heavily quantitative roleBasic knowledgeNo prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is providedWhat you will learnIdentify situations that call for the use of Machine LearningUnderstand which type of Machine learning problem you are solving and choose the appropriate solutionUse Machine Learning and Natural Language processing to solve problems like text classification, text summarization in PythonGmail: supportsimpliv.comPhone no: 5108496155 Click to Continue Reading: https://www.simpliv.com/searchRegistration Link: https://www.simpliv.com/python/from-0-to-1-machine-learning-nlp-python-cut-to-the-chaseSimpliv Youtube Course & Tutorial : https://www.youtube.com/channel/UCZZevQcSlAK689KbsrMvEog?view_as=subscriberFacebook Page: https://www.facebook.com/simplivllcLinkedin: https://www.linkedin.com/company/simplivTwitter: https://twitter.com/simplivllc
Keywords: Accepted papers list. Acceptance Rate. EI Compendex. Engineering Index. ISTP index. ISI index. Impact Factor.
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