শুক্রবার, ১৪ ফেব্রুয়ারি, ২০২০
বুধবার, ১২ ফেব্রুয়ারি, ২০২০
[Intermediate] Spatial Data Analysis with R, QGIS & More
PRACTICAL TRAINING WITH REAL SPATIAL DATA FROM DIFFERENT SOURCES.
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This course is designed to take users who use R and QGIS for basic spatial data/GIS analysis to perform more advanced GIS tasks (including automated workflows and geo-referencing) using a variety of different data. In addition to making you proficient in R and QGIS for spatial data analysis, you will be introduced to another powerful free GIS software.. GRASS.
The course is taught by Minerva Singh, A PhD graduate from Cambridge University, UK, who has several years of research experience in Quantitative Ecology and an MPhil in Geography and Environment from Oxford University. Minerva has published papers in international peer reviewed journals and given talks at international conferences.
The underlying motivation for the course is to ensure you can put spatial data analysis into practice today and develop sound GIS analysis skills. You’ll be able to start analyzing spatial data for your own projects, and IMPRESS YOUR FUTURE EMPLOYERS with examples of your PRACTICAL spatial data analysis abilities. This course is different from other training resources. Each lecture seeks to enhance your GIS skills in a demonstrable and tangible manner and provide you with practically implementable GIS solutions.
This is an intermediate course in spatial data analysis, i.e. we will build on on basic spatial data analysis tasks (such as those covered in the beginner version course: Core Spatial Data Analysis: Introductory GIS with R and QGIS) and teach users how to practically implement more complex GIS tasks such as interpolation, mapping spatial data, geo-referencing and detailed vector processing. Additionally you will be introduced to preliminary geo-statistics and mapping/visualizing spatial data.
This course covers complex GIS techniques, and by completing this course, you will be implementing these PRACTICALLY in freely-available software, thus making you MORE ATTRACTIVE TO EMPLOYERS.
It is a practical, hands-on course, i.e. we will spend a tiny amount of time dealing with some of the theoretical concepts pertaining to the different spatial data analysis techniques demonstrated in the course. However, majority of the course will focus on working with real spatial data from different sources. After each video you will learn how to practically implement a new concept or technique in the different softwares used for the course.
During the course of my research I have discovered that R is a powerful tool for collating and analyzing spatial data acquired from different sources. Proficiency in spatial data analysis in R and QGIS has helped me publish more peer reviewed papers faster. Feel free to check out my profile on ResearchGate.
FREE BONUS: You will have access to all the data used in the course, along with the R code files. You will also have access to future lectures, resources and R code files. Enroll in the course today & take advantage of this special bonus!
- People who have a basic understanding of spatial data analysis and want to learn more
- Students interested in building up on skills acquired through my previous course Core Spatial Data Analysis: Introductory GIS with R and QGIS
- Academics
- Conservation managers
- GIS Technicians.
95% OFF #coupon offer for Spatial #Data #Analysis with R, QGIS & More #udemy #course
https://www.udemy.com/intermediate-spatial-data-analysis-with-r-qgis-more/?couponCode=10PROMO
Become Proficient In Spatial Data Analysis Using R & QGIS By Working On A Real Project - Get A Job In Spatial Data
Do you find GIS & Spatial Data books & manuals too vague, expensive & not practical and looking for a course that takes you by hand, teaches you all the concepts, and get you started on a real life project?
Or perhaps you want to save time and learn how to automate some of the most common GIS tasks?
I'm very excited you found my spatial data analysis course. My course provides a foundation to carry out PRACTICAL, real-life spatial data analysis tasks in popular and FREE software frameworks.
My name is MINERVA SINGH and i am an Oxford University MPhil (Geography and Environment) graduate. I am currently pursuing a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life spatial data from different sources and producing publications for international peer reviewed journals.
In this course, actual spatial data from the Tam Dao National Park in Vietnam will be used to give a practical hands-on experience of working with real life spatial data and understanding what kind of questions spatial data can help us answer. The underlying motivation for the course is to ensure you can put spatial data analysis into practice today. Start analyzing spatial data for your own projects, whatever your skill level and IMPRESS your potential employers with an actual example of your spatial data analysis abilities.
This is a core course in spatial data analysis, i.e. we will focus on learning the most important and widely encountered spatial data analysis tasks in both R and QGIS
It is a practical, hands-on course, i.e. we will spend a tiny amount of time dealing with some of the theoretical concepts related to spatial data analysis. However, majority of the course will focus on working with the spatial data from the Tam Dao National Park, Vietnam. After each video you will learn a new concept or technique which you may apply to your own projects.
- Academics
- Researchers
- Conservation managers
- Anybody who works/will work with spatial data.
90% OFF!! #udemy #course Core #Spatial #Data #Analysis: Introductory #GIS with R and #QGIS get #coupon
https://www.udemy.com/core-spatial-data-analysis-with-r-and-qgis/
Satellite Remote Sensing Data Bootcamp With Opensource Tools
Are you currently enrolled in either of my Core or Intermediate Spatial Data Analysis Courses?
Or perhaps you have prior experience in GIS or tools like R and QGIS?
You don't want to spend 100s and 1000s of dollars on buying commercial software for imagery analysis?
The next step for you is to gain profIciency in satellite remote sensing data analysis.
My course provides a foundation to carry out PRACTICAL, real-life remote sensing analysis tasks in popular and FREE software frameworks with REAL spatial data. By taking this course, you are taking an important step forward in your GIS journey to become an expert in geospatial analysis.
Why Should You Take My Course?
I am an Oxford University MPhil (Geography and Environment) graduate. I also completed a PhD at Cambridge University (Tropical Ecology and Conservation).
In this course, actual satellite remote sensing data such as Landsat from USGS and radar data from JAXA will be used to give a practical hands-on experience of working with remote sensing and understanding what kind of questions remote sensing can help us answer.
This course will ensure you learn & put remote sensing data analysis into practice today and increase your proficiency in geospatial analysis.
Remote sensing software tools are very expensive and their cost can run into thousands of dollars. Instead of shelling out so much money or procuring pirated copies (which puts you at a risk of prosecution), you will learn to carry out some of the most important and common remote sensing analysis tasks using a number of popular, open source GIS tools such as R, QGIS, GRASS and ESA-SNAP. All of which are in great demand in the geospatial sector and improving your skills in these is a plus for you.
This is an introductory course, i.e. we will focus on learning the most important and widely encountered remote sensing data processing and analyzing tasks in R, QGIS, GRASS and ESA-SNAP
You will also learn about the different sources of remote sensing data there are and how to obtain these FREE OF CHARGE and process them using FREE SOFTWARE.
In addition to all the above, you’ll have MY CONTINUOUS SUPPORT to make sure you get the most value out of your investment!
- People with prior expereince of working spatial data
- GIS analysts
- Ecologists
- Forestry and Conservation Practioners
- Geographers
- Geologists
92% off #udemy course #couponcode
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Learn 16 Machine Learning Algorithms in a Fun and Easy along with Practical Python Labs using Keras
Welcome to the Fun and Easy Machine learning Course in Python and Keras.
Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science.
This is a valid question and the answer is simple. This is the ONLY course on Udemy which will get you implementing some of the most common machine learning algorithms on real data in Python. Plus, you will gain exposure to neural networks (using the H2o framework) and some of the most common deep learning algorithms with the Keras package.
We designed this course for anyone who wants to learn the state of the art in Machine learning in a simple and fun way without learning complex math or boring explanations. Each theoretically lecture is uniquely designed using whiteboard animations which can maximize engagement in the lectures and improves knowledge retention. This ensures that you absorb more content than you would traditionally would watching other theoretical videos and or books on this subject.
This is how the course is structured:
- Regression – Linear Regression, Decision Trees, Random Forest Regression,
- Classification – Logistic Regression, K Nearest Neighbors (KNN), Support Vector Machine (SVM) and Naive Bayes,
- Clustering - K-Means, Hierarchical Clustering,
- Association Rule Learning - Apriori, Eclat,
- Dimensionality Reduction - Principle Component Analysis, Linear Discriminant Analysis,
- Neural Networks - Artificial Neural Networks, Convolution Neural Networks, Recurrent Neural Networks.
You DO NOT need any prior Python or Statistics/Machine Learning Knowledge to get Started. The course will start by introducing students to one of the most fundamental statistical data analysis models and its practical implementation in Python- ordinary least squares (OLS) regression. Subsequently some of the most common machine learning regression and classification techniques such as random forests, decision trees and linear discriminant analysis will be covered. In addition to providing a theoretical foundation for these, hands-on practical labs will demonstrate how to implement these in Python. Students will also be introduced to the practical applications of common data mining techniques in Python and gain proficiency in using a powerful Python based framework for machine learning which is Anaconda (Python Distribution). Finally you will get a solid grounding in both Artificial Neural Networks (ANN) and the Keras package for implementing deep learning algorithms such as the Convolution Neural Network (CNN). Deep Learning is an in-demand topic and a knowledge of this will make you more attractive to employers.
So as you can see you are going to be learning to build a lot of impressive Machine Learning apps in this 3 hour course. The underlying motivation for the course is to ensure you can apply Python based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and IMPRESS your potential employers with an actual examples of your machine learning abilities.
It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.
TAKE ACTION TODAY! We will personally support you and ensure your experience with this course is a success. And for any reason you are unhappy with this course, Udemy has a 30 day Money Back Refund Policy, So no questions asked, no quibble and no Risk to you. You got nothing to lose. Click that enroll button and we'll see you in side the course.
- Student who starting out or interested in Machine Learning or Deep Learning.
- Students with Prior Python Programming Exposure Who Want to Use it for Machine Learning
- Students interested in gaining exposure to the Keras library for Deep Learning.
- Data analysts who want to expand into Machine Learning.
- College students who want to start a career in Data Science.
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Learn Data Preprocessing, Data Wrangling and Data Visualisation For Practical Data Science Applications in R
Hello, My name is Minerva Singh. I am an Oxford University MPhil graduate in Geography & Environment & I finished a PhD at Cambridge University in Tropical Ecology & Conservation.
I have +5 of experience in analyzing real-life data from different sources using statistical modelling and producing publications for international peer-reviewed journals. If you find statistics books & manuals too vague, expensive & not practical, then you’re going to love this course!
I created this course to take you by hand and teach you all the concepts, and tackle the most fundamental building block on practical data science - data wrangling and visualisation.
This course is your sure-fire way of acquiring the knowledge and statistical data analysis wrangling and visualisation skills that I acquired from the rigorous training I received at 2 of the best universities in the world, the perusal of numerous books and publishing statistically rich papers in the renowned international journal like PLOS One.
- It will take you (even if you have no prior statistical modelling/analysis background) from a basic level of performing some of the most common data wrangling tasks in R.
- It will equip you to use some of the most important R data wrangling and visualisation packages such as dplyr and ggplot2.
- It will Introduce some of the most important data visualisation concepts to you in a practical manner such that you can apply these concepts for practical data analysis and interpretation.
- You will also be able to decide which wrangling and visualisation techniques are best suited to answer your research questions and applicable to your data and interpret the results..
The course will mostly focus on helping you implement different techniques on real-life data such as Olympic and Nobel Prize winners
After each video, you will learn a new concept or technique which you may apply to your own projects immediately! Reinforce your knowledge through practical quizzes and assignments.
- Practice Activities To Reinforce Your Learning
- My Continuous Support To Make Sure You Gain Complete Understanding & Proficiency
- Access To Future Course Updates Free Of Charge
- I’ll Even Go The Extra Mile & Cover Any Topics That Are Related To The Subject That You Need Help With (This is something you can’t get anywhere else).
- & Access To A Community Of 25,000 Data Scientists (& growing) All Learning Together & Helping Each Other!
Now, go ahead & enrol in the course. I’m certain you’ll love it, but in case you don’t, you can always request a refund within 30 days. No hard feelings whatsoever. I look forward to seeing you inside!
- Students Interested In Getting Started With Data Science Applications In The R & R Studio Environment
- Students Interested in Learning About the Common Pre-processing Data Tasks: Including Cleaning and Munging
- Students Interested in Gaining Exposure to Common R Packages Such As ggplot2
- Those Interested in Learning About Different Kinds of Data Visualisations
- Those Interested in Learning to Create Publication Quality Visualisations.
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Complete Data #Wrangling & #Data #Visualisation In R
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Complete Deep Learning In R With Keras & Others
YOUR COMPLETE GUIDE TO ARTIFICIAL NEURAL NETWORKS & DEEP LEARNING IN R:
This course covers the main aspects of neural networks and deep learning. If you take this course, you can do away with taking other courses or buying books on R based data science.
In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in neural networks and deep learning in R, you can give your company a competitive edge and boost your career to the next level!
My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University.
I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.
Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic .
This course will give you a robust grounding in the main aspects of practical neural networks and deep learning.
Unlike other R instructors, I dig deep into the data science features of R and give you a one-of-a-kind grounding in data science...
You will go all the way from carrying out data reading & cleaning to to finally implementing powerful neural networks and deep learning algorithms and evaluating their performance using R.
Among other things:
- You will be introduced to powerful R-based deep learning packages such as h2o and MXNET.
- You will be introduced to deep neural networks (DNN), convolution neural networks (CNN) and unsupervised methods.
- You will learn how to implement convolutional neural networks (CNN)s on imagery data using the Keras framework
- You will learn to apply these frameworks to real life data including credit card fraud data, tumor data, images among others for classification and regression applications.
With this course, you’ll have the keys to the entire R Neural Networks and Deep Learning Kingdom!
You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.
My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real-life.
After taking this course, you’ll easily use data science packages like caret, h2o, mxnet, keras to implement novel deep learning techniques in R. You will get your hands dirty with real life data, including real-life imagery data which you will learn to pre-process and model
You’ll even understand the underlying concepts to understand what algorithms and methods are best suited for your data.
We will also work with real data and you will have access to all the code and data used in the course.
- People Wanting To Master The R & R Studio Environment For Data Science
- Anyone With Prior Exposure To Common Machine Learning Concepts Such As Supervised Learning
- Students Wishing To Learn The Implementation Of Neural Networks On Real Data In R
- Students Wishing To Learn The Implementation Of Basic Deep Learning Concepts In R.
92% discount #coupon #udemy #course for
Complete #Deep #Learning In R With Keras & Others
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Master the Most Important Deep Learning Frameworks (Tensorflow & Keras) for Python Data Science
THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH TENSORFLOW & KERAS IN PYTHON!
It is a full 7-Hour Python Tensorflow & Keras Neural Network & Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning using two of the most important Deep Learning frameworks- Tensorflow and Keras.
This course is your complete guide to practical machine & deep learning using the Tensorflow & Keras framework in Python..
This means, this course covers the important aspects of Keras and Tensorflow (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Tensorflow and Keras based data science.
In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of Tensorflow and Keras is revolutionizing Deep Learning...
By gaining proficiency in Keras and and Tensorflow, you can give your company a competitive edge and boost your career to the next level.
But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).
I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.
Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning..
This gives students an incomplete knowledge of the subject. My course, on the other hand, will give you a robust grounding in all aspects of data science within the Tensorflow framework.
Unlike other Python courses, we dig deep into the statistical modeling features of Tensorflow & Keras and give you a one-of-a-kind grounding in these frameworks!
• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda
• Getting started with Jupyter notebooks for implementing data science techniques in Python
• A comprehensive presentation about Tensorflow & Keras installation and a brief introduction to the other Python data science packages
• Brief introduction to the working of Pandas and Numpy
• The basics of the Tensorflow syntax and graphing environment
• The basics of the Keras syntax
• Machine Learning, Supervised Learning, Unsupervised Learning in the Tensorflow & Keras frameworks
• You’ll even discover how to create artificial neural networks and deep learning structures with Tensorflow & Keras
• Getting started with Jupyter notebooks for implementing data science techniques in Python
• A comprehensive presentation about Tensorflow & Keras installation and a brief introduction to the other Python data science packages
• Brief introduction to the working of Pandas and Numpy
• The basics of the Tensorflow syntax and graphing environment
• The basics of the Keras syntax
• Machine Learning, Supervised Learning, Unsupervised Learning in the Tensorflow & Keras frameworks
• You’ll even discover how to create artificial neural networks and deep learning structures with Tensorflow & Keras
You’ll start by absorbing the most valuable Python Tensorflow and Keras basics and techniques.
I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts.
My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python based data science in real -life.
After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python along with gaining fluency in Tensorflow and Keras. I will even introduce you to deep learning models such as Convolution Neural network (CNN) !!
The underlying motivation for the course is to ensure you can apply Python based data science on real data into practice today, start analyzing data for your own projects whatever your skill level, and impress your potential employers with actual examples of your data science abilities.
This course will take students without a prior Python and/or statistics background background from a basic level to performing some of the most common advanced data science techniques using the powerful Python based Jupyter notebooks
It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results..
After each video you will learn a new concept or technique which you may apply to your own projects!
- People Interested In Learning Python Based Tensorflow and Keras For Data Science Applications
- People With Prior Exposure To Python Programming &/Or Data Science Concepts
- People Interested In Implementing Neural Networks & Deep Learning Models With Tensorflow
- People Interested In Implementing Neural Networks & Deep Learning Models With Keras.
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#Tensorflow and Keras For Neural Networks and #Deep #Learning
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Harness The Power Of Machine Learning For Unsupervised & Supervised Learning In R -- With Practical Examples
This course your complete guide to both supervised & unsupervised learning using R...
That means, this course covers all the main aspects of practical data science and if you take this course, you can do away with taking other courses or buying books on R based data science.
In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised & supervised learning in R, you can give your company a competitive edge and boost your career to the next level.
My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University.
I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.
Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic...
This course will give you a robust grounding in the main aspects of machine learning- clustering & classification.
Unlike other R instructors, I dig deep into the machine learning features of R and gives you a one-of-a-kind grounding in Data Science!
You will go all the way from carrying out data reading & cleaning to machine learning to finally implementing powerful machine learning algorithms and evaluating their performance using R.
• A full introduction to the R Framework for data science
• Data Structures and Reading in R, including CSV, Excel and HTML data
• How to Pre-Process and “Clean” data by removing NAs/No data,visualization
• Machine Learning, Supervised Learning, Unsupervised Learning in R
• Model building and selection...& MUCH MORE!
By the end of the course, you’ll have the keys to the entire R Machine Learning Kingdom!
You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.
My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life.
After taking this course, you’ll easily use data science packages like caret to work with real data in R...
You’ll even understand concepts like unsupervised learning, dimension reduction and supervised learning. Again, we'll work with real data and you will have access to all the code and data used in the course.
- Students Interested In Getting Started With Data Science Applications In The R & R Studio Environment
- Students Wishing To Learn The Implementation Of Unsupervised Learning On Real Data
- Students Wishing To Learn The Implementation Of Supervised Learning (Classification) On Real Data Using R.
92% discount #coupon #udemy #course for
#Clustering & #Classification With #Machine #Learning In R
#couponcode
https://www.udemy.com/clustering-classification-with-machine-learning-in-r/
Spatial Data Analysis With ArcGIS Desktop: Master GIS Techniques and Open Doors to Amazing Geospatial Careers
I WANNA LEARN SPATIAL DATA ANALYSIS, BUT…
- I found most spatial data books & manuals vague
- There are no courses that actually teach me how it’s actually done
- Available resources are expensive & I can’t afford them
- I want to get a job in the field of GIS and geospatial analysis
- I work in the field of ecology or quantitative social sciences or hydrology or civil engineering or geography
If you found yourself in one of these situations, then..
I’VE GOT GREAT NEWS FOR YOU!
Over the past few months, I have published multiple courses on Udemy around this topic which will be tremendously helpful to you & most important of all AFFORDABLE!
Today, I’ve created yet another powerful resource for you!
In this course, over 50+ hands-on and practical lecture, I will help you master the most common and important geo-processing tasks that can be performed with ArcGIS Desktop, one of THE MOST important GIS software tools available.
I will also show you the kind of questions answered through Spatial Analysis & data used.
First of all, we’ll start some basic GIS tasks like “Zooming”.
Then, we’ll move into more complex processing tasks like “Geo-Statistics”.
We’ll also deal with some theoretical concepts related to Spatial Data Analysis, and then we’ll focus on implementing some of the most common GIS techniques (all the way showing you how to execute these tasks in ArcGIS Desktop).
The stuff you’ll learn from this course will be extremely useful in terms of you being able to implement it on future Spatial Data projects you’ll be working on (in a variety of disciplines from ecology to engineering).
My name is MINERVA SINGH.
I am an Oxford University MPhil (Geography and Environment) graduate.
I recently finished my PhD at Cambridge University (Tropical Ecology and Conservation).
I have SEVERAL YEARS OF EXPERIENCE in analyzing REAL LIFE DATA from different sources in ArcGIS Desktop.
I’ve also published my work in many international peer reviewed journals.
My course is a HANDS ON TRAINING with REAL data.
It’s a step by step course covering both the THEORY & APPLICATION of Spatial Data Analysis.
I teach Practical Stuff that you can learn quickly and start implementing NOW.
This is one of the most comprehensive courses on this topic.
I advise you to take advantage of it & enroll in the course TODAY!
Please make sure you have access to ArcGIS before enrolling
- Academics and Researchers
- Conservation managers, field ecologists and social scientists
- People looking to get started in the field of GIS Analysis
- Students of Geography, Environmental Sciences, Geology, Hydrology, Engineering, Earth Sciences and Ecology
- People looking to use ArcGIS Desktop in academic or professional settings.
#ArcGIS Desktop For Spatial Analysis: Go From Basic To Pro #udemy #course 95% off !!! #couponcode
https://www.udemy.com/arcgis-desktop-for-spatial-analysis-go-from-basic-to-pro/?couponCode=ARCGIS10A
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