Machine Learning training class for Beginners in Helsinki | Learn Machine Learning | ML Training | Machine Learning bootcamp | Introduction to Machine Learning

MaakuntaUusimaa (18)

Are you brand new to Machine Learning? Want to see how fun and easy it can be? This Machine  Learning Training class for beginners course offers a step-by-step guide to understanding and working with Machine Learning and Machine Learning algorithms. Don't worry if you do not know Programming. You can still learn Machine Learning and see how fun it can be to learn and apply Machine Learning in your job or any other applicable scenarios.  Machine Learning uses simple to complex algorithms and has an easy learning curve, and is very forgiving. Gain a new skill or complete a task by the end of each module, and, by the end of the course, you will be applying Machine Learning to applicable scenarios. You also learn basic principles which can make it easier for you to learn other advanced Machine Learning techniques in the future.  Course Schedule Course Duration: 4 weeks (8 sessions) Tuesdays and thursdays every week 6:00pm - 8:00pm US Pacific Daylight Time each day August 13 - September 5, 2019 US Pacific Daylight time Check local date and time for 1st session What are the prerequisites?  No prerequisite is required.
Even if you do not have programming background you will be able to take this course and learn Machine Learning. Course Outline Introduction to Machine Learning Fundamentals of Machine Learning Common Use Cases in Machine Learning Understanding Supervised and Unsupervised Learning Techniques Clustering Similarity Metrics Distance Measure Types: Euclidean, Cosine Measures Creating predictive models Understanding K-Means Clustering Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model Implementing Association rule mining Understanding Process flow of Supervised Learning Techniques Decision Tree Classifier How to build Decision trees Random Forest Classifier What is Random Forests Features of Random Forest Out of Box Error Estimate and Variable Importance Naive Bayes Classifier Problem Statement and Analysis Various approaches to solving a Data Science Problem Pros and Cons of different approaches and algorithms Linear Regression Logistic Regression Text Mining Sentimental Analysis

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