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Showing posts with the label MACHINE LEARNING

Learn HTML5 Programming By Building Projects

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Learn HTML5 Programming By Building Projects About this Course In order to master a technique in web development or any artistic field, the best approach is one that's hands-on. This allows you to not only learn concepts, but also apply them right away to build your own projects from scratch. That's why this course guides you through the process of actually creating a variety of games, apps, and sites using HTML5 and JavaScript. Master HTML5 and JavaScript, starting with the creation of 10 apps and games. •  HTML5 and JavaScript, Including Semantic Markup and Boilerplate Framework •  CSS3 Media Queries and Responsive Design •  JQuery, Fancybox, APIs, and More •  10 Hands-On Projects, Including Games and Apps Use HTML5 and JavaScript to Create a Variety of Projects Not all sites and apps use HTML5 yet, but the ones that do are advanced and cutting edge, and this is the way of the future in web design and app development. Using HTML5, CSS3, JavaScript, and other tools

Machine Learning - Regression and Classification (math Inc.)

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Machine Learning - Regression and Classification (math Inc.) What you'll learn Understand and implement a Decision Tree in Python Understand about Gini and Information Gain algorithm Solve mathematical numerical related decision trees Learn about regression trees Learn about simple, multiple, polynomial and multivariate regression Learn about Ordinary Least Squares Algorithms Solve numerical related to Ordinary Least Squares algorithm Learn to create real world predictions and classification projects Learn about Gradient Descent Learn about Logistic Regression and hyper parameters Requirements Basic mathematical concepts of addition, multiplication and so on Knowing python beforehand would be handful Description Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so. In data science, an algorithm is a sequence of statistical processing steps

Complete Machine Learning with R Studio - ML for 2021

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What you'll learn Learn how to solve real life problem using the Machine learning techniques Machine Learning models such as Linear Regression, Logistic Regression, KNN etc. Advanced Machine Learning models such as Decision trees, XGBoost, Random Forest, SVM etc. Understanding of basics of statistics and concepts of Machine Learning How to do basic statistical operations and run ML models in R Indepth knowledge of data collection and data preprocessing for Machine Learning problem How to convert business problem into a Machine learning problem Requirements Students will need to install R and R studio software but we have a separate lecture to help you install the same Description You're looking for a complete  Machine Learning course  that can help you launch a flourishing career in the field of Data Science, Machine Learning, R and Predictive Modeling, right? You've found the right Machine Learning course! After completing this course,  you will be able to : ·

Complete Machine Learning with R Studio - ML for 2021

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What you'll learn Learn how to solve real life problem using the Machine learning techniques Machine Learning models such as Linear Regression, Logistic Regression, KNN etc. Advanced Machine Learning models such as Decision trees, XGBoost, Random Forest, SVM etc. Understanding of basics of statistics and concepts of Machine Learning How to do basic statistical operations and run ML models in R Indepth knowledge of data collection and data preprocessing for Machine Learning problem How to convert business problem into a Machine learning problem Requirements Students will need to install R and R studio software but we have a separate lecture to help you install the same Description You're looking for a complete  Machine Learning course  that can help you launch a flourishing career in the field of Data Science & Machine Learning, right? You've found the right Machine Learning course! After completing this course  you will be able to : · Confidently build predi

Machine Learning : A Beginner's Basic Introduction

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Requirements You should be able to use a PC at beginner level Basic knowledge of Python would help but not mandatory Description Machine learning relates to many different ideas, programming languages, frameworks. Machine learning is difficult to define in just a sentence or two. But essentially, machine learning is giving a computer the ability to write its own rules or algorithms and learn about new things, on its own. In this course, we'll explore some basic machine learning concepts and load data to make predictions. Value estimation—one of the most common types of machine learning algorithms—can automatically estimate values by looking at related information. For example, a website can determine how much a house is worth based on the property's location and characteristics. In this course, we will  use machine learning to build a value estimation system that can deduce the value of a home.   Although the tool  we will build in this course focuses on real estate

Machine Learning Real World projects in Python

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Requirements Basic knowledge of Python programming is recommended. Description Machine Learning is one of the hottest technology field in the world right now! This field is exploding with opportunities and career prospects. Machine Learning techniques are widely used in several sectors now a days such as banking, healthcare, finance, education transportation and technology. This course covers several technique in a practical manner, the projects include coding sessions as well as Algorithm Intuition: So, if you’ve ever wanted to play a role in the future of technology development, then here’s your chance to get started with Machine Learning. Because in a practical life, machine learning seems to be complex and tough,thats why we’ve designed a course to help break it down into real world use-cases that are easier to understand. 1 .Task #1 @Predicting the Hotel booking  :  Predict Whether booking  is going to cancel or not 3 .Task #2 @Predict Whether Person has a Chronic Dise

Machine Learning – Regression and Classification (math Inc.)

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A complete Beginner to Advance level guide to Machine Learning. Hands-on Learning approach with in-depth math concepts 591 students enrolled  English [Auto] Understand and implement a Decision Tree in Python Understand about Gini and Information Gain algorithm Solve mathematical numerical related decision trees Learn about regression trees Learn about simple, multiple, polynomial and multivariate regression Learn about Ordinary Least Squares Algorithms Solve numerical related to Ordinary Least Squares algorithm Learn to create real world predictions and classification projects Learn about Gradient Descent Learn about Logistic Regression and hyper parameters Requirements Basic mathematical concepts of addition, multiplication and so on Knowing python beforehand would be handful Description Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so. I

R Programming:For Data Science With Real Exercises

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Requirements Basic knowledge about any programming language like specifically graph,vectors,type of graph,median,means Description This course will  introduces the R statistical processing language, including how to install R on your computer, read data from SPSS and spreadsheets, and use packages for advanced R functions. The course continues with examples on how to create charts and plots, check statistical assumptions and the reliability of your data, look for data outliers, and use other data analysis tools. Finally, learn how to get charts and tables out of R and share your results with presentations and web pages. The following topics are include: ·         What is R? ·         Installing R and R studio (IDE) ·         Creating bar character for categorical variables ·         Building histograms ·         Calculating frequencies and descriptive ·         Computing new variables ·         Creating scatter plots ·         Comparing means ===============================

Machine Learning & Deep Learning in Python & R

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Requirements Students will need to install Anaconda software but we have a separate lecture to guide you install the same Description You're looking for a complete  Machine Learning and Deep Learning course  that can help you launch a flourishing career in the field of Data Science & Machine Learning, right? You've found the right Machine Learning course! After completing this course  you will be able to : · Confidently build predictive Machine Learning and Deep Learning models to solve business problems and create business strategy · Answer Machine Learning related interview questions · Participate and perform in online Data Analytics competitions such as Kaggle competitions Check out the table of contents below to see what all Machine Learning and Deep Learning models you are going to learn. How this course will help you? A  Verifiable Certificate of Completion  is presented to all students who undertake this Machine learning basics course. If you are a busine