Become a Probability & Statistics Master

Become a Probability & Statistics Master, Learn everything from Probability & Statistics, then test your knowledge on 340+ quiz questions

• Created by Krista King

What Will I Learn?

• Visualizing data
• Analyzing data
• Data distributions
• Probability
• Discrete random variables
• Sampling
• Hypothesis testing

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HOW BECOME A PROBABILITY & STATISTICS MASTER IS SET UP TO MAKE COMPLICATED MATH EASY:

This 163-lesson course includes video and text explanations of everything from Probability and Statistics, and it includes 45 quizzes (with solutions!) and an additional 8 workbooks with extra practice problems, to help you test your understanding along the way. Become a Probability & Statistics Master is organized into the following sections:

Visualizing data, including bar graphs, pie charts, Venn diagrams, histograms, and dot plots

Analyzing data, including mean, median, and mode, plus range and IQR and box-and-whisker plots

Data distributions, including mean, variance, and standard deviation, and normal distributions and z-scores

Probability, including union vs. intersection and independent and dependent events and Bayes' theorem

Discrete random variables, including binomial, Bernoulli, Poisson, and geometric random variables

Sampling, including types of studies, bias, and sampling distribution of the sample mean or sample proportion, and confidence intervals

Hypothesis testing, including inferential statistics, significance levels, type I and II errors, test statistics, and p-values

Regression, including scatterplots, correlation coefficient, the residual, coefficient of determination, RMSE, and chi-square

AND HERE'S WHAT YOU GET INSIDE OF EVERY SECTION:

Videos: Watch over my shoulder as I solve problems for every single math issue you’ll encounter in class. We start from the beginning... I explain the problem setup and why I set it up that way, the steps I take and why I take them, how to work through the yucky, fuzzy middle parts, and how to simplify the answer when you get it.

Notes: The notes section of each lesson is where you find the most important things to remember. It’s like Cliff Notes for books, but for math. Everything you need to know to pass your class and nothing you don’t.

Quizzes: When you think you’ve got a good grasp on a topic within a course, you can test your knowledge by taking one of the quizzes. If you pass, great! If not, you can review the videos and notes again or ask for help in the Q&A section.

Workbooks: Want even more practice? When you've finished the section, you can review everything you've learned by working through the bonus workbook. The workbooks include tons of extra practice problems, so they're a great way to solidify what you just learned in that section.

HERE'S WHAT SOME STUDENTS OF BECOME A PROBABILITY & STATISTICS MASTER HAVE TOLD ME:

“Krista is an experienced teacher who offers Udemy students complete subject matter coverage and efficient and effective lessons/learning experiences. She not only understands the course material, but also selects/uses excellent application examples for her students and presents them clearly and skillfully using visual teaching aids/tools.” - John

“Really good, thorough, well explained lessons.” - Scott F.

“This is my second course (algebra previously) from Ms. King's offerings. I enjoyed this course and learned a lot! Each video explains a concept, followed by the working of several examples. I learned the most by listening to Ms King's teaching of the concept, stopping the video, and then attempting to work the example problems. After working the problems, then watching her complete the examples, I found that I really retained the concepts. A great instructor!” - Charles M.

YOU'LL ALSO GET:

Lifetime access to Become a Probability & Statistics Master

Friendly support in the Q&A section

Udemy Certificate of Completion available for download

30-day money back guarantee

Enroll today!

I can't wait for you to get started on mastering probability and statistics.

- Krista :)

Who this course is for:

• Current probability and statistics students, or students about to start probability and statistics who are looking to get ahead
• Homeschool parents looking for extra support with probability and statistics
• Anyone who wants to study math for fun after being away from school for a while

Deep Learning and NLP A-Z™: How to create a ChatBot

Deep Learning and NLP A-Z™: How to create a ChatBot, Learn the Theory and How to implement state of the art Deep Natural Language Processing models in Tensorflow and Python

• BESTSELLER
• Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team
•  English
•  English [Auto-generated]
• 11.5 hours on-demand video
• 14 articles
• 6 downloadable resources
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion
• Have a coupon?

Preview This Course - GET COUPON CODE

What you'll learn

• Why this is important
• Types of Natural Language Processing
• Classical vs. Deep Learning Models
• End to End Deep Learning Models
• Seq2Seq Architecture & Training
• Beam Search Decoding

Description

We've talked about, speculated and often seen different applications for Artificial Intelligence - But what about one piece of technology that will not only gather relevant information, better customer service and could even differentiate your business from the crowd?

ChatBots are here, and they came change and shape-shift how we've been conducting online business. Fortunately technology has advanced enough to make this a valuable tool something accessible that almost anybody can learn how to implement.

If you want to learn one of the most attractive, customizable and cutting edge pieces of technology available, then this course is just for you!

Who is the target audience?

• Any students in college who want to start a career in Data Science
• Any Data Science enthusiast
• Anyone interested in creating their own ChatBot
• Anyone interested in Artificial Intelligence, Machine Learning or Deep Learning and its applications

Biology 101: Transport, Immune and Respiratory System

Biology 101: Transport, Immune and Respiratory System, Learn the basics of Biology

• Created by Muhamad Nabeel Uddin
•  English
• 1 hour on-demand video
• 2 articles
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

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What you'll learn

• Understand how our body defends itself against diseases

Description

Biology is multidisciplinary area of study that encompasses all aspects of living organism and their environment. This short course is one in a series that Introduces basic Biology concepts. In this course, you will understand how our blood moves around the body, the basic components of the blood and how the heart functions. You will also learn how our Respiratory system brings Oxygen towards our lungs, and how our lungs remove gases that are not needed by our cells. Finally, you will learn the basics of how our body will defend itself against diseases.

This is a course for those who have little or no prior knowledge- It is an Introductory course. The contents are presented in a manner that suits the O level Examinations that are written in different countries around the world.  

Who is the target audience?

• High School students
• Anyone who wants to learn specific concepts in Biology

Machine Learning and Earth Observation Big Data

Machine Learning and Earth Observation Big Data, Learn to apply machine learning algorithms to classify satellite data on the cloud, Created by Spatial eLearning LLC, Alemayehu Midekisa, PhD

• 1 hour on-demand video
• 6 articles
• 5 downloadable resources
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion
• Have a coupon?

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What you'll learn

• Learn to learn applying machine learning algorithms using satellite data.
• Learn processing analyzing large volume of remotely sensed satellite data with the Earth Engine API.
• Learn to collect reference training data for image classification.
• Learn to remove clouds from Landsat imageries.
• Learn to calculate multi-spectral indices with satellite bands.
• Learn to assess the accuracy of classification.

Description

Do you want to learn how to access, process and analyze remote sensing data using open source cloud-based platforms?

Do you want to master machine learning algorithms to predict Earth Observation big data?

Do you want to start a spatial data scientist career in the geospatial industry?

Enroll in my new course to master Machine Learning and Earth Observation Big Data.

I will provide you with hands-on training with example data, sample scripts, and real-world applications.  

By taking this course, you will take your geospatial data science skills to the next level by gaining proficiency in applying machine learning algorithms to predict satellite data using an open source big data analytics tool, Earth Engine API, a cloud-based Earth observation data visualization analysis by powered by Google.

What makes me qualified to teach you?

I am Dr. Alemayehu Midekisa, PhD and I am a lecturer and research scientist at the University of California. I have over 10 years of experience in processing and analyzing real big Earth observation data from various sources including Landsat, MODIS, Sentinel-2, SRTM and other remote sensing products.

I am also the recipient of one the prestigious NASA Earth and Space Science Fellowship. I teach over 10,000 students on Udemy.

In this Machine Learning and Earth Observation Big Data course, I will help you get up and running on the Google Earth Engine cloud platform. Then you will apply various machine learning algorithms including linear regression, clustering, CART, and random forests. We will use Landsat satellite data to predict land use land cover classification. All sample data and script will be provided to you as an added bonus throughout the course.

Jump in right now to enroll. To get started click the enroll button.

Best,

Alemayehu + The Spatial eLearning Team

Who is the target audience?

• Anyone who want to understand the application of various machine learning techniques using satellite data.
• Anyone who wants to learn big Earth observation data analysis on the cloud.
• Anyone who wants to start a career in spatial data science.

Machine Learning and Earth Observation Big Data

Machine Learning and Earth Observation Big Data, Learn to apply machine learning algorithms to classify satellite data on the cloud

• 0.0 (0 ratings
• Created by Spatial eLearning LLC, Alemayehu Midekisa, PhD
•  English
•  English [Auto-generated]
• 1 hour on-demand video
• 4 articles
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

Preview This Course - GET COUPON CODE

What you'll learn

• Learn to learn applying machine learning algorithms using satellite data.
• Learn processing analyzing large volume of remotely sensed satellite data with the Earth Engine API.
• Learn to collect reference training data for image classification.
• Learn to remove clouds from Landsat imageries.
• Learn to calculate multi-spectral indices with satellite bands.
• Learn to assess the accuracy of classification.

Description

Do you want to learn how to access, process and analyze remote sensing data using open source cloud-based platforms?

Do you want to master machine learning algorithms to predict Earth Observation big data?

Do you want to start a spatial data scientist career in the geospatial industry?

Enroll in my new course to master Machine Learning and Earth Observation Big Data.

I will provide you with hands-on training with example data, sample scripts, and real-world applications.  

By taking this course, you will take your geospatial data science skills to the next level by gaining proficiency in applying machine learning algorithms to predict satellite data using an open source big data analytics tool, Earth Engine API, a cloud-based Earth observation data visualization analysis by powered by Google.

What makes me qualified to teach you?

I am Dr. Alemayehu Midekisa, PhD and I am a lecturer and research scientist at the University of California. I have over 10 years of experience in processing and analyzing real big Earth observation data from various sources including Landsat, MODIS, Sentinel-2, SRTM and other remote sensing products.

I am also the recipient of one the prestigious NASA Earth and Space Science Fellowship. I teach over 10,000 students on Udemy.

In this Machine Learning and Earth Observation Big Data course, I will help you get up and running on the Google Earth Engine cloud platform. Then you will apply various machine learning algorithms including linear regression, clustering, CART, and random forests. We will use Landsat satellite data to predict land use land cover classification. All sample data and script will be provided to you as an added bonus throughout the course.

Jump in right now to enroll. To get started click the enroll button.

Best,

Alemayehu + The Spatial eLearning Team

Who is the target audience?

• Anyone who want to understand the application of various machine learning techniques using satellite data.
• Anyone who wants to learn big Earth observation data analysis on the cloud.
• Anyone who wants to start a career in spatial data science.

Species Distribution Models with GIS & Machine Learning in R

Species Distribution Models with GIS & Machine Learning in R, Mapping Habitat Suitability for Conservation Using Machine Learning and GIS in R

Created by Minerva Singh

Includes

• 3.5 hours on-demand video
• 2 Articles
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

Description

                Are You an Ecologist or Conservationist Interested in Learning GIS and Machine Learning in R?

• Are you an ecologist/conservationist looking to carry out habitat suitability mapping?
• Are you an ecologist/conservationist looking to get started with R for accessing ecological data and GIS analysis?
• Do you want to implement practical machine learning models in R?
• Then this course is for you! I will take you on an adventure into the amazing of field Machine Learning and GIS for ecological modelling. You will learn how to implement species distribution modelling/map suitable habitats for species in R. 

My name is MINERVA SINGH and i am an Oxford University MPhil (Geography and Environment) graduate. I finished 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 Peninsular Malaysia will be used to give a practical hands-on experience of working with real life spatial data for mapping habitat suitability in conjunction with classical SDM models like MaxEnt and machine learning alternatives such as Random Forests. The underlying motivation for the course is to ensure you can put spatial data and machine learning analysis into practice today. Start ecological data for your own projects, whatever your skill level and IMPRESS your potential employers with an actual examples of your GIS and Machine Learning skills in R.

So Many R based Machine Learning and GIS Courses Out There, Why This One?

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 ecological data in R. Plus, you will gain exposure to working your way through a common ecological modelling technique- species distribution modelling (SDM) using real life data. Students will also gain exposure to implementing some of the most common Geographic Information Systems (GIS) and spatial data analysis techniques in R. Additionally,  students will learn how to access ecological data via R. 

You will learn to harness the power of both GIS and Machine Learning in R for ecological modelling. 

I have 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. Yes, even non-ecologists can get started with practical machine learning techniques in R while working their way through real data. 

What you will Learn in this Course

This is how the course is structured:

• Introduction – Introduction to SDMs and mapping habitat suitability
• The Basics of GIS for Species Distribution Models (SDMs) – You will learn some of the most common GIS and data analysis tasks related to SDMs including accessing species presence data via R
• Pre-Processing Raster and Spatial Data for SDMs - Your R based GIS training and will continue and you will earn to perform some of the most common GIS techniques on raster and other spatial data
• Classical SDM Techniques - Introduction to the classical models and their implementation in R (MaxENT and Bioclim)
• Machine Learning Models for Habitat Suitability - Implement and interpret common ML techniques to build habitat suitability maps for the birds of Peninsular Malaysia. 

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts . 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! I 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.

Who is the target audience?

• Ecologists interested in improving their quantitative skills
• GIS and remote sensing practitioners interested in mapping suitable habitats
• Students and researchers interested in learning about SDMs
• Students and researchers interested in implementing ecological & GIS techniques in R
• Conservation practitioners interested in mapping suitable habitats for species
• Ecologists, biologists & conservation practitioners interested in applying machine learning to ecological problems
• Geographers and environmental scientists

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Physics - Kinematics (2-D) for High School and Intro College

Physics - Kinematics (2-D) for High School and Intro College

This course contains lessons with simple and clear explanations of Vectors, Projectiles, and Circular Motion. Created by Michael Voth

Description

This course is one of a series of courses designed for algebra-based high school and intro college physics.  In this course, the topics and concepts in two-dimensional kinematics will be covered; including vectors, projectile motion, and circular motion.  The course contain video lessons with note templates, practice assignments, and assessment quizzes.

The highlight of this course is very clear and simplified explanations of the concepts during the video lessons with assignments that reinforce the learning.  This is a great course for any student struggling with the basic concepts of a high school or introductory college physics course or anyone interested in starting to learn about physics.  This course does not cover calculus based physics, but the foundation of the physics concepts still apply to calculus based courses.


Upon completion of this course, students can continue with the other courses in this series which contain the topics covered in a traditional introductory physics course.


Who is the target audience?
• People wanting easy to understand instruction in physics.
• High school or introductory level college physics students.

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Project Based Python Programming For Kids & Beginners

5 hours on-demand video, 4 Articles, Full lifetime access, Access on mobile and TV, Certificate of Completion

Learn Hands-On Python Programming By Creating Games, GUIs and Graphics | by Minerva Singh

Description

Beginners and Kids Can Now Learn Python the Fun and Easy Way 

Teach yourself (and your kids) to code fun games, graphics and GUI in Python, the powerful programming language used at tech companies and in academia. 

Unlike the other courses and books out there, this course provides a rare opportunity to learn the graphics and UX (User Experience) sides of Python – even as a beginner! Unlike the many other Python courses on Udemy, this course introduces you to this computer language by drawing shapes, coding a simple game, and designing GUIs (Graphic User Interfaces), including a functional GUI for a temperature converter app.

Gain a Firm Foundation in Python GUI Programming

• Learn the basics of Python game programming
• Craft elegant and useful Python GUIs
• Create simple and practical applications in Python
• Explore the world of Python graphic design

If you want to learn to code, Python GUIs are the best way to start!

I designed this programming course to be easily understood by absolute beginners and young people. We start with basic Python programming concepts. Reinforce the same by developing games, graphics and GUIs. And finally we will develop a practical temperature converter app using Python.

Why Python?

The Python coding language integrates well with other platforms – and runs on virtually all modern devices. If you’re new to coding, you can easily learn the basics in this fast and powerful coding environment. If you have experience with other computer languages, you’ll find Python simple and straightforward. This OSI-approved open-source language allows free use and distribution – even commercial distribution.

Can You Build a Career with Python?

Absolutely! On average, U.S. Python developers earn $109,000 per year. This powerful and widely-used language could be your or your child's ticket to a better life. With the rigorous grounding you get from this course, you’ll have the knowledge and confidence to step into higher-level Python courses.

How Can You Use Python?

• Once you gain a basic knowledge of Python through this course, you can explore a diverse range of programming specialties:
• 
  
• Build Desktop/Laptop GUIs
• Design Exciting and Immersive Games
• Develop Websites and Apps
• Analyze Scientific and Statistical Data
• Create Educational Software
• Access and Organize Databases
• Manage Networks

Who Uses Python?

This course gives you a solid set of skills in one of today’s top programming languages. Today’s biggest companies (and smartest startups) use Python, including Google, Facebook, Instagram, Amazon, IBM, and NASA. Python is increasingly being used for scientific computations and data analysis. 

You Can Start Right Away, Without Prior Programming Experience

Detailed instructions have been provided with regards to Python installation and getting started with Microsoft Visual Code, a powerful programming IDLE that will be a valuable tool for your programming journey. Hands-on coding instructions have been provided in the lecture videos to enable you to follow along. Additionally, working code examples have been provided for you to try and modify. Each video will teach you a new practical programming concept that you can apply in real time and quizzes will reinforce your learning. 

The instructor is an Oxbridge trained researcher and always available to troubleshoot. You'll also receive an industry recognized Certificate of Completion upon finishing the course.

No Risk: Preview videos from the different sections for FREE, and enjoy a 30-day money-back guarantee when you enroll - zero risk, unlimited payoff! 

Sign up for this course today and learn the skills you need to rub shoulders with today’s tech industry giants. Have fun, create and control intriguing and interactive Python GUIs, and enjoy a bright future!

Who is the target audience?

• Anyone who wants to learn to code
• People wanting to program in Python
• People interested in gaining hands-on Python skills and actually wanting to work through real life programming projects
• People interested in building games and GUIs
• Anyone looking to start with Python GUI development
• Programming beginners and children who want to create practical applications

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Complete Data Science Training with Python for Data Analysis

Complete Data Science Training with Python for Data Analysis, Complete Guide to Practical Data Science with Python: Learn Statistics, Visualization, Machine Learning & More

Publisher : Minerva Singh

Course Length : 13 hours

Course Language : English

THIS IS A COMPLETE DATA SCIENCE TRAINING WITH PYTHON FOR DATA ANALYSIS: 

It's A Full 12-Hour Python Data Science BootCamp To Help You Learn Statistical Modelling, Data Visualization, Machine Learning & Basic Deep Learning In Python! 

HERE IS WHY YOU SHOULD TAKE THIS COURSE:

First of all, this course a complete guide to practical data science using Python...

That means, this course covers ALL the aspects of practical data science and if you take this course alone, you can do away with taking other courses or buying books on Python 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. By storing, filtering, managing, and manipulating data in Python, you can give your company a competitive edge & boost your career to the next level!

THIS IS MY PROMISE TO YOU:

COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTHON BASED DATA SCIENCE!

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 student an incomplete knowledge of the subject. This course will give you a robust grounding in all aspects of data science, from statistical modeling to visualization to machine learning.

Unlike other Python instructors, I dig deep into the statistical modeling features of Python and gives you a one-of-a-kind grounding in Python Data Science!

You will go all the way from carrying out simple visualizations and data explorations to statistical analysis to machine learning to finally implementing simple deep learning based models using Python

DISCOVER 12 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF PYTHON DATA SCIENCE (INCLUDING):

• 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 basic analytical tools- Numpy Arrays, Operations, Arithmetic, Equation-solving, Matrices, Vectors, Broadcasting, etc.

• Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data

• How to Pre-Process and “Wrangle” your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc.

• Creating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and more!

• Statistical analysis, statistical inference, and the relationships between variables

• Machine Learning, Supervised Learning, Unsupervised Learning in Python

• You’ll even discover how to create artificial neural networks and deep learning structures...& MUCH MORE!

With this course, you’ll have the keys to the entire Python Data Science kingdom!

NO PRIOR PYTHON OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED:

You’ll start by absorbing the most valuable Python Data Science basics and techniques...

I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python.

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.

You’ll even understand deep concepts like statistical modeling in Python’s Statsmodels package and the difference between statistics and machine learning (including hands-on techniques).

I will even introduce you to deep learning and neural networks using the powerful H2o framework!

With this Powerful All-In-One Python Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and deep learning! 

The underlying motivation for the course is to ensure you can apply Python based data science on real data and put 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.

HERE IS WHAT THIS COURSE WILL DO FOR YOU:

This course is your one shot way of acquiring the knowledge of statistical data analysis skills that I acquired from the rigorous training received at two of the best universities in the world, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One.

This course will:

   (a) 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.

   (b) Equip students to use Python for performing different statistical data analysis and visualization tasks for data modelling.

   (c) Introduce some of the most important statistical and machine learning concepts to students in a practical manner such that students can apply these concepts for practical data analysis and interpretation.

   (d) Students will get a strong background in some of the most important data science techniques.

   (e) Students will be able to decide which data science techniques are best suited to answer their research questions and applicable to their data and interpret the results.

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. 

JOIN THE COURSE NOW!

Who is the target audience?

• Anyone Who Wishes To Learn Practical Data Science Using Python
• Anyone Interested In Learning How To Implement Machine Learning Algorithms Using Python
• People Looking To Get Started In Deep Learning Using Python
• People Looking To Work With Real Life Data In Python
• Anyone With A Prior Knowledge Of Python Looking To Branch Out Into Data Analysis
• Anyone Looking To Become Proficient In Exploratory Data Analysis, Statistical Modelling & Visualizations Using iPython

Preview This Course - GET COUPON CODE

Regression Analysis for Statistics & Machine Learning in R

Coupon Details

Regression Analysis for Statistics & Machine Learning in R, Learn Complete Hands-On Regression Analysis for Practical Statistical Modelling and Machine Learning in R

Created by Minerva Singh

What Will I Learn?

• Implement and infer Ordinary Least Square (OLS) regression using R
• Apply statistical and machine learning based regression models to deals with problems such as multicollinearity
• Carry out variable selection and assess model accuracy using techniques like cross-validation
• Implement and infer Generalized Linear Models (GLMS), including using logistic regression as a binary classifier
• Build machine learning based regression models and test their robustness in R
• Learn when and how machine learning models should be applied
• Compare different different machine learning algorithms for regression modelling

Description

            With so many R Statistics & Machine Learning courses around, why  enroll for this ?

Regression analysis is one of the central aspects of both statistical and machine learning based analysis. This course will teach you regression analysis for both statistical data analysis and machine learning in R in a practical hands-on manner. It explores the relevant concepts  in a practical manner from basic to expert level. This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting or make business forecasting related decisions. All of this while exploring the wisdom of an Oxford and Cambridge educated researcher.

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. This course is based on my years of regression modelling experience and implementing different regression models on real life data.  Most statistics and machine learning courses and books only touch upon the basic aspects of regression analysis. This does not teach the students about all the different regression analysis techniques they can apply to their own data in both academic and business setting, resulting in inaccurate modelling. My course will change this. You will go all the way from implementing and inferring simple OLS (ordinary least square) regression models to dealing with issues of multicollinearity in regression to machine learning based regression models. 

Become a Regression Analysis Expert and Harness the Power of R for Your Analysis

Get started with R and RStudio. Install these on your system, learn to load packages and read in different types of data in R

Carry out data cleaning and data visualization using R

Implement ordinary least square (OLS) regression in R and learn how to interpret the results.

Learn how to deal with multicollinearity both through variable selection and regularization techniques such as ridge regression

Carry out variable and regression model selection using both statistical and machine learning techniques, including using cross-validation methods .

Evaluate regression model accuracy

Implement generalized linear models (GLMs) such as logistic regression and Poisson regression. Use logistic regression as a binary classifier to distinguish between male and female voices.

Use non-parametric techniques such as Generalized Additive Models (GAMs) to work with non-linear and non-parametric data. 

Work with tree-based machine learning models

Implement machine learning methods such as random forest regression and gradient boosting machine regression for improved regression prediction accuracy.

Carry out model selection

Become a Regression Analysis Pro and Apply Your Knowledge on Real-Life Data

This course is your one shot way of acquiring the knowledge of statistical and machine learning analysis that I acquired from the rigorous training received at two of the best universities in the world, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One. Specifically the course will:

   (a) Take the students with a basic level statistical knowledge to performing some of the most common advanced regression analysis based techniques

   (b) Equip students to use R for performing the different statistical and machine learning data analysis and visualization tasks 

   (c) Introduce some of the most important statistical and machine learning concepts to students in a practical manner such that the students can apply these concepts for practical data analysis and interpretation

   (d) Students will get a strong background in some of the most important statistical and machine learning concepts for regression analysis.

   (e) Students will be able to decide which regression analysis techniques are best suited to answer their research questions and applicable to their data and interpret the results

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to both statistical and machine learning regression analysis. 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! I will personally support you and ensure your experience with this course is a success.

Who is the target audience?

• People who have completed my course on Statistical Modeling for Data Analysis in R (or equivalent experience)
• People with basic knowledge of R based statistical modelling
• People with knowledge of linear regression modelling
• People wanting to extend their knowledge of regression modelling for solving real world problems.
• People wanting to learn how to apply machine learning based regression models using R
• Undergraduates and postgraduates seeking to deepen their knowledge of statistical and machine learning analysis
• Academic researchers seeking to learn new techniques for data analysis
• Business data analysts who wish to use regression modelling for predictive analysis

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FATEK PLC & MITSUBISHI PLC & SIEMENS PLC COURSE URDU HINDI

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FATEK PLC & MITSUBISHI PLC & SIEMENS PLC COURSE URDU HINDI, BASIC TRAINING COURSE AUTOMATION ENGINEERING

Created by Nasir Hussain shah

What Will I Learn?

• AM SURE YOU SEE MY ALL VIDEOS AND MY STUDENTS DO BEGINNER LEVEL PROGRAAM

Description

ASSLAM O ALIKUM FRIEND IN THIS COURSE WE SHOW YOU HOW TO DO PLC PROGRAMMING AND HOW MAKES PROJECTS AND BASIC TRAING ON ALL PLC AND HMI AND OTHER MANY EQUIPMENT TRAINING ON,,...

The Instrumentation course employs a large range of industrial current loop, temperature, pressure, level and flow sensors and associated equipment. The course teaches electricians with no previous instrumentation knowledge how to recognise, calibrate and maintain a range of equipment used in real industrial instrumentation systems and then the follow-up course on PID Controllers shows how these would be put into a control loop. These two courses combined together form the Instrumentation & Process Control course..

Who is the target audience?

• MY COURSES ONLY FOR ENGINEER

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Binary Distillation

Academics, Math & Science

Binary Distillation, Model Continuous Distillation Equipment in the Industry as well as Flash & Batch Separation Processes in Binary Systems

Created by Chemical Engineering Guy

What Will I Learn?

• Understand the Principles behind Binary Distillation
• Design & Operate Flashing, and Flash Drums via Flash Distillation of Binary Mixtures
• Understand and apply the principles and methods for simple distillation of binary mixtures
• Understand Continuous Distillation Equipment
• Model Distillations of Binary Systems
• Apply McCabe-Thiele Method for Continuous Distillation Column Design (Number of Stages)
• Simulate several process, specially continuous and batch distillation of binary mixtures
• Get to know the "Recycle Ratio", "Reboiler Duty", "Feed Stage" etc...
• Model Stripping and Enriching Sections of a Distillation Column

Requirements

• Mass Transfer Basics
• Transport Phenomena
• Thermodynamics
• Mass and Energy Balances
• Physical Chemistry

Description

Introduction:

Gas Absorption is one of the very first Mass Transfer Unit Operations studied in early process engineering. It is very important in several Separation Processes, as it is used extensively in the Chemical industry.

Understanding the concept behind Gas-Gas and Gas-Liquid mass transfer interaction will allow you to understand and model Absorbers, Strippers, Scrubbers, Washers, Bubblers, etc...

We will cover:

REVIEW: Of Mass Transfer Basics (Equilibrium VLE Diagrams, Volatility, Raoult's Law, Azeotropes, etc..)

Distillation Theory

Application of Distillation in the Industry

Counter-Current Operation

Several equipment to Carry Gas-Liquid Operations

Bubble, Spray, Packed and Tray Column equipment

Flash Distillation & Flash Drums Design

Design & Operation of Tray Columns

Number of Ideal Stages: McCabe Thiele Method & Ponchon Savarit Method

Recycle

Condenser types: partial, total

Pressure drop due to trays

Design & Operation of Packed Columns

Pressure drop due to trays

Efficiency of Stages & Murphree's Efficency

Batch Distillation, the Raleigh Equation

Software Simulation for Absorption/Stripping Operations (ASPEN PLUS/HYSYS)

Solved-Problem Approach:

All theory is backed with exercises, solved problems, and proposed problems for homework/individual study.

At the end of the course:

You will be able to understand mass transfer mechanism and processes behind Binary Distillation in Flash, Continuous & Batch Processes. You will be able to continue with a Multi-Component Distillation, Reactive Distillation and Azeotropic Distillation as well as more Mass Transfer Unit Operation Courses and/or Separation Processes Course.

About your instructor:

I majored in Chemical Engineering with a minor in Industrial Engineering back in 2012.

I worked as a Process Design/Operation Engineer in INEOS Koln, mostly on the petrochemical area relating to naphtha treating. There I designed and modeled several processes relating separation of isopentane/pentane mixtures, catalytic reactors and separation processes such as distillation columns, flash separation devices and transportation of tank-trucks of product.

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Psychology 1: How our perception (really) works

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Psychology 1: How our perception (really) works, Learn how our brain constructs our reality and challenge your assumptions (with many examples)

Created by André Klapper, PhD

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What Will I Learn?

• Understand how we perceive and how our brain constructs our reality
• Know about the unconscious processes that govern our perception
• Get rid of false assumptions and get ready to really understand the human mind
• Understand how many visual illusions work
• Be able to see the evidence in your everyday life that our brain constructs our reality
• Understand the basic structure of the visual system in the brain

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Energy and power system optimization in GAMS

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Energy and power system optimization in GAMS, General Algebraic Modeling System

Created by Alireza Soroudi

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What Will I Learn?

• GAMS installation
• Solve Economic dispatch problem
• Solve Dynamic Economic dispatch problem
• Solve Optimal power flow problem
• Solve Dynamic Economic dispatch scheduling and Energy storage systems problem

Number System:Convert Decimal,Binary,Hexa-Decimal,Octal

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Number System:Convert Decimal,Binary

Hexa-Decimal,Octal, Number System Conversion

Created by Sayantan Tarafdar

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What Will I Learn?

• Understanding Decimal Binary Octal and Hexadecimal Number System.
• Convert Decimal Numbers to Binary . Convert Decimal Numbers to Hexadecimal Number System. Convert Decimal Numbers to Octal Number System.
• Convert Binary Numbers to Decimal Number System. Convert Binary Numbers to Hexadecimal Number System. Convert Binary Numbers to Octal Number System.
• Convert Octal Numbers to Decimal Number System. Convert Octal Numbers to Hexadecimal Number System. Convert Octal Numbers to Binary Number System.
• Convert Hexadecimal Numbers to Decimal Number System. Convert Hexadecimal Numbers to Octal Number System. Convert Hexadecimal Numbers to Binary Number System.

Analysis of Accounting Ratios

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Analysis of Accounting Ratios, Liquidity Ratios, Activity or Performance Ratios, Leverage ratios and Profitability Ratios

Created by Saurabh Salil

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What Will I Learn?

• Students will learn how to calculate the different ratios, why these are calculated and how to remember formulas

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Understand how electricity is generated

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Understand how electricity is generated, Story of Fundamental Concept to Mathematical Representation

Created by Dr. Jignesh Makwana

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What Will I Learn?

• What are the different forms of energy? What is Law of Energy Conservation?
• What is faraday’s law? & What is lenz’s law?
• How to generate emf of desired magnitude & desired frequency?
• Why shape of generated waveform is sinusoidal?
• What is magnetic field?
• How practical generator differs from ideal theoretical generator concept?

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Research Proposal Workshop for Dental Students

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Research Proposal Workshop for Dental Students, Develop your research proposal

Created by Pulikkotil Shaju Jacob

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Description

This course takes you on a journey in developing your research proposal. You have ten short lectures that have embedded activities. Completion of the activities helps you learn research proposal development skills. The various sections are introduction, research problem, hypothesis, research question, methodology, variables, data management, ethics, budget and timelines.