INTRODUCTION TO PROBABILITY

HISTORY OF PROBABILITY

Probability as a branch of mathematics has a long history, dating back over 300 years, when it was first applied to situations concerning games of chance. Many books are dedicated solely to probability, but our goal is to focus on the aspects of the subject that have the greatest immediate influence on statistical inference difficulties.

The present mathematical theory of probability can be traced back to attempts by Gerolamo Cardano in the sixteenth century and Pierre de Fermat and Blaise Pascal in the seventeenth century to examine games of chance (for example the "problem of points"). Their motivation stemmed from an issue regarding games of chance provided by the chevalier de Méré, a notably philosophical gambler. When a game of chance is stopped, De Méré inquires about the right allocation of stakes. Let's say two players, X and Y, are playing a three-point game with 32 pistoles each, and they're interrupted when X has two points and Y has one.

Pascal thought Fermat's solution was too complicated, so he recommended solving the problem in terms of the quantity now known as "expectation," rather than probability.

Games of chance like this one served as model problems for the theory of chances in its early stages, and they are still used in textbooks today. Pascal's posthumous work on the "arithmetic triangle," which is now associated with his name (see binomial theorem), demonstrated how to calculate numbers of combinations and combine them to solve basic gambling difficulties.

Girolamo Cardano, an Italian mathematician, physician, and gambler, estimated chances for games of chance by counting up equally likely occurrences more than a century ago. However, his small work was not published until 1663, by which time the elements of the theory of chances were well known among European mathematicians.

PROBABILITY

Probability is the study of calculating the chances of something happening. At its most basic level, it is concerned with the roll of a dice or the fall of cards in a game. Probability, on the other hand, is critical to both science and everyday life. It's used for a variety of things, like weather forecasting and figuring out how much your insurance premiums would cost. Probability is the scientific study of randomness and uncertainty. The study of probability gives methods for calculating the chances, or likelihoods, of various outcomes in any situation where one of a number of possible outcomes could occur.

In both written and spoken contexts, the language of probability is frequently utilized in an informal manner. For example, “It is likely that the Dow Jones average will increase by the end of the year,” or “It is likely that the Dow Jones average will climb by the end of the year.” “The incumbent has a 50–50 likelihood of seeking reelection,” says the expert. “It's likely that at least one component of that course will be given next year,” says the professor. “The odds favor a rapid resolution of the strike,” and “at least 20,000 concert tickets are expected to be sold.”

SEE YOU IN THE NEXT TOPIC:

SAMPLE SPACE AND EVENTS OF AN EXPERIMENT
AREAS UNDER THE NORMAL CURVE

About the Author:

Ed Neil O. Maratas an instructor of Jose Rizal Memorial State University, Dapitan Campus, Philippines as regular status. He earned his Bachelor of Science in Statistics at Mindanao State University-Tawi-Tawi College of Technology and Oceanography in the year 2003 and finished Master of Arts in Mathematics at Jose Rizal Memorial State University year 2009. He Became a researcher, a data analyst, and engaged to several projects linked to the university as data processor.

Prepared by:ednielmaratas@gmail.com or you can visit the facebook pageStatisticss For Fun or at my fb-group My Statistics & Social Page for more details about statistics.

Introductory Biostatistics

Introductory Biostatistics

A course in introductory biostatistics is often required for professional students in public health, dentistry, nursing, and medicine, and for graduate students in nursing and other biomedical sciences, a requirement that is often considered a roadblock, causing anxiety in many quarters. These feelings are expressed in many ways and in many di¤erent settings, but all lead to the same conclusion: that students need help, in the form of a user-friendly and real data-based text, in order to provide enough motivation to learn a subject that is perceived to be di‰cult and dry. This introductory text is written for professionals and beginning graduate students in human health disciplines who need help to pass and benefit from the basic biostatistics requirement of a one-term course or a full-year sequence of two courses. Our main objective is to avoid the perception that statistics is just a series of formulas that students need to ‘‘get over with,’’ but to present it as a way of thinking—thinking about ways to gather and analyze data so as to benefit from taking the required course. There is no better way to do that than to base a book on real data, so many real data sets in various fields are provided in the form of examples and exercises as aids to learning how to use statistical procedures, still the nuts and bolts of elementary applied statistics.

Preface The first five chapters start slowly in a user-friendly style to nurture interest and motivate learning. Sections called ‘‘Brief Notes on the Fundamentals’’ are added here and there to gradually strengthen the background and the concepts. Then the pace is picked up in the remaining seven chapters to make sure that those who take a full-year sequence of two courses learn enough of the nuts and bolts of the subject. Our basic strategy is that most students would need only one course, which would end at about the middle of Chapter 8, after covering simple linear regression; instructors may add a few sections of Chapter 12. For students who take only one course, other chapters would serve as references to supplement class discussions as well as for their future needs. A subgroup of students with a stronger background in mathematics would go on to a second course, and with the help of the brief notes on the fundamentals would be able to handle the remaining chapters. A special feature of the book is the sections ‘‘Notes on Computations’’ at the end of most chapters. These notes cover uses of Microsoft’s Excel, but samples of SAS computer programs are also included at the end of many examples, especially the advanced topics in the last several chapters.

>>>CLICK HERE TO VIEW THE PDF FILE<<<

Preface

The way of thinking called statistics has become important to all professionals: not only those in science or business but also caring people who want to help to make the world a better place. But what is biostatistics, and what can it do? There are popular definitions and perceptions of statistics. We see ‘‘vital statistics’’ in the newspaper: announcements of life events such as births, marriages, and deaths. Motorists are warned to drive carefully, to avoid ‘‘becoming a statistic.’’ Public use of the word is widely varied, most often indicating lists of numbers or data. We have also heard people use the word data to describe a verbal report, a believable anecdote. For this book, especially in the first few chapters, we don’t emphasize statistics as things, but instead, o¤er an active concept of ‘‘doing statistics.’’ The doing of statistics is a way of thinking about numbers (collection, analysis, and presentation), with emphasis on relating their interpretation and meaning to the manner in which they are collected. Formulas are only a part of that thinking, simple tools of the trade; they are needed but not as the only things one needs to know.

>>>CLICK HERE TO VIEW THE PDF FILE<<<

About the Author:

Ed Neil O. Maratas an instructor of Jose Rizal Memorial State University, Dapitan Campus, Philippines as regular status. He earned his Bachelor of Science in Statistics at Mindanao State University-Tawi-Tawi College of Technology and Oceanography in the year 2003 and finished Master of Arts in Mathematics at Jose Rizal Memorial State University year 2009. He Became a researcher, a data analyst, and engaged to several projects linked to the university as data processor.

Prepared by:ednielmaratas@gmail.com or you can visit the facebook pageStatisticss For Funfor more details about statistics.

ShortcutLInk Here:Visit Ad.fly Website Now

"> >>>Short URL link HERE<<< Basically, AdFly is a link shortening service and unlike other link shortening services like bit.ly & goo.gl,

Chi-Square Test

Chi-Square Analysis

Learning Objectives Given the learning materials and activities of this chapter, the students will be able to: Perform Chi-square test for Goodness of Fit and test of independence to test the significance preference and significance of associations between categorical variables. Interpret the results..

A chi-square tests involve comparing the observed frequencies in a one-way or two-way frequency distribution table with the expected frequencies if the null hypothesis were true. These tests play an important role in many other problems where information is obtained by counting rather than measuring. The method we shall describe here applies to two kinds of problems. The first is the Chi-square goodness-of-fit test, and the second is the chi-square test for independence.

Source of the Image Credit: https://www.slideshare.net/Asadgroup/chi-square-test-presentation and for more details

>>>CLICK HERE TO VIEW File<<<

About the Author:

Ed Neil O. Maratas an instructor of Jose Rizal Memorial State University, Dapitan Campus, Philippines as regular status. He earned his Bachelor of Science in Statistics at Mindanao State University-Tawi-Tawi College of Technology and Oceanography in the year 2003 and finished Master of Arts in Mathematics at Jose Rizal Memorial State University year 2009. He Became a researcher, a data analyst, and engaged to several projects linked to the university as data processor.

Prepared by:ednielmaratas@gmail.com or you can visit the facebook pageStatisticss For Funfor more details about statistics.

ShortcutLInk Here:Visit Ad.fly Website Now

"> >>>Short URL link HERE<<< Basically, AdFly is a link shortening service and unlike other link shortening services like bit.ly & goo.gl,

Foundations of Data Analysis - Part 1: Statistics Using R

>>>CLICK HERE TO ENROLL<<<

Foundations of Data Analysis - Part 1: Statistics Using R

Overview

In this first part of a two part course, we’ll walk through the basics of statistical thinking – starting with an interesting question. Then, we’ll learn the correct statistical tool to help answer our question of interest – using R and hands-on Labs. Finally, we’ll learn how to interpret our findings and develop a meaningful conclusion. This course will consist of: Instructional videos for statistical concepts broken down into manageable topics Guided questions to help your understanding of the topic Weekly tutorial videos for using R Scaffolded learning with Pre-Labs (using R), followed by Labs where we will answer specific questions using real-world datasets Weekly wrap-up questions challenging both topic and application knowledge We will cover basic Descriptive Statistics – learning about visualizing and summarizing data, followed by a “Modeling” investigation where we’ll learn about linear, exponential, and logistic functions. We will learn how to interpret and use those functions with basic Pre-Calculus. These two “units” will set the learner up nicely for the second part of the course: Inferential Statistics with a multiple regression cap. Both parts of the course are intended to cover the same material as a typical introductory undergraduate statistics course, with an added twist of modeling. This course is also intentionally devised to be sequential, with each new piece building on the previous topics. Once completed, students should feel comfortable using basic statistical techniques to answer their own questions about their own data, using a widely available statistical software package (R). With these new skills, learners will leave the course with the ability to use basic statistical techniques to answer their own questions about their own data, using a widely available statistical software package (R). Learners from all walks of life can use this course to better understand their data, to make valuable informed decisions.

Course Content

Week One: Introduction to Data

Week Two: Univariate Descriptive Statistics

Week Three: Bivariate Distributions

Week Four: Bivariate Distributions (Categorical Data)

Week Five: Linear Functions

Week Six: Exponential and Logistic Function Models

>>>CLICK HERE TO ENROLL<<<

About the Author:

Ed Neil O. Maratas is an instructor of Jose Rizal Memorial State University, Dapitan Campus, Philippines as regular status. He earned his Bachelor of Science in Statistics at Mindanao State University-Tawi-Tawi College of Technology and Oceanography in the year 2003 and finished Master of Arts in Mathematics at Jose Rizal Memorial State University year 2009. He Became a researcher, a data analyst, and engaged to several projects linked to the university as data processor.

Prepared by:ednielmaratas@gmail.com or you can visit the facebook pageStatisticss For Funfor more details about statistics.

ShortcutLInk Here:Visit Ad.fly Website Now

"> >>>Short URL link HERE<<< Basically, AdFly is a link shortening service and unlike other link shortening services like bit.ly & goo.gl,

Statistical Methods

Statistical Methods for the Social Sciences

The purpose of this book is to acquaint the reader with the increasing number of applications of statistics in engineering and the social sciences. It can be used as a textbook for a first course in statistical methods in Universities and Polytechnics. The book can also be used by decision-makers and researchers to either gain a basic understanding or to extend their knowledge of some of the most commonly used statistical methods. The book contains ten Chapters. Chapter 1 deals with the overview of statistics. In Chapter 2, we discuss how to describe data, using graphical and summary statistics. Chapter 3 covers probability while Chapters 4 and 5 cover probability distributions. Chapters 6, 7, 8 and 9 present basic tools of statistical inference; point estimation, interval estimation, hypothesis testing and analysis of variance. Chapter 10 presents linear regression and correlation. Our presentation is distinctly applications-oriented.

>>>CLICK HERE TO VIEW Open AND DOWNLOAD the PDF File<<<

A prominent feature of the book is the inclusion of many examples. Each example is carefully selected to illustrate the application of a particular statistical technique and or interpretation of results. Another feature is that each chapter has an extensive collection of exercises. Many of these exercises are from published sources, including past examination questions from King Saud University (Saudi Arabia) and Methodist University College Ghana. Answers to all the exercises are given at the end of the book.

About the Author:

Ed Neil O. Maratas an instructor of Jose Rizal Memorial State University, Dapitan Campus, Philippines as regular status. He earned his Bachelor of Science in Statistics at Mindanao State University-Tawi-Tawi College of Technology and Oceanography in the year 2003 and finished Master of Arts in Mathematics at Jose Rizal Memorial State University year 2009. He Became a researcher, a data analyst, and engaged to several projects linked to the university as data processor.

Prepared by:ednielmaratas@gmail.com or you can visit the facebook pageStatisticss For Funfor more details about statistics.

ShortcutLInk Here:Visit Ad.fly Website Now

"> >>>Short URL link HERE<<< Basically, AdFly is a link shortening service and unlike other link shortening services like bit.ly & goo.gl,

How to Use SPSS in Forecasting?

STEPS ON HOW USE SPSS IN FORECASTING

IBM® SPSS® Forecasting enables analysts to predict trends and develop forecasts quickly and easily -- without being an expert statistician. Reliable forecasts can have a major impact on your organization’s ability to develop and implement successful strategies. Unlike spreadsheet programs, IBM SPSS Forecasting has the advanced statistical techniques needed to work with time-series data regardless of your level of expertise. Analyze historical data and predict trends faster, and deliver information in ways that your organization’s decision-makers can understand and use Automatically determine the best-fitting ARIMA or exponential smoothing model to analyze your historic data Model hundreds of different time series at once, rather than having to run the procedure for one variable at a time Save models to a central file so that forecasts can be updated when data changes, without having to re-set parameters or re-estimate models Write scripts so that models can be updated with new data automatically

>>>CLICK HERE TO VIEW AND DOWNLOAD<<<

Content

Chapter 1. >>>Introduction To Time Series<<<,,,2. >>>Introduction To Time SeriesTime Series Modeler<<<,,,3. >>>Apply Time Series Models<<<,,,4. >>>Seasonal Decomposition<<<,,,5. >>>Spectral Plots<<<,,,6. >>>The goodness of Fit Measures<<<,,,7. >>>Outlier Types<<<,,,8. >>>Guide to ACF/PACF plots<<<

Source of this image:https://qsoft-softitaly500.weebly.com/blog/download-spss-22

>>>CLICK HERE TO VIEW AND DOWNLOAD<<<

About the Researcher...

Ed Neil O. Maratas an instructor of Jose Rizal Memorial State University, Dapitan Campus, Philippines as regular status. He earned his Bachelor of Science in Statistics at Mindanao State University-Tawi-Tawi College of Technology and Oceanography in the year 2003 and finished Master of Arts in Mathematics at Jose Rizal Memorial State University year 2009. He Became a researcher, a data analyst, and engaged to several projects linked to the university as data processor.

Prepared by:Ed Neil or you can visit the facebook pageStatisticss For Funfor

ShortcutLInk Here:Visit Ad.fly Website Now

"> >>>Short URL link HERE<<< Basically, AdFly is a link shortening service and unlike other link shortening services like bit.ly & goo.gl,.

Statistical Modeling

Statistical Modeling in R (Part 1)

>>>CLICK HERE TO ENROLL<<<

Course Description...

Statistical Modeling in R is a multi-part course designed to get you up to speed with the most important and powerful methodologies in statistics. In Part 1, we'll take a look at what modeling is and what it's used for, R tools for constructing models, using models for prediction (and using prediction to test models), and how to account for the combined influences of multiple variables. This course has been written from scratch, specifically for DataCamp users. As you'll see, by using computing and concepts from machine learning, we'll be able to leapfrog many of the marginal and esoteric topics encountered in traditional 'regression' courses.

>>>CLICK HERE<<<

TOPICS

.What is statistical modeling?
.Designing, training, and evaluating models
.Assessing prediction performance
.Exploring data with models
.Covariates and effect size

And lot MORE...

FOR MORE DETAILS >>>CLICK HERE TO ENROLL<<<

Instructors Profile:

Daniel Kaplan DeWitt Wallace Professor at Macalester College

Danny is the DeWitt Wallace Professor of Mathematics, Statistics, and Computer Science at Macalester College in Saint Paul, Minnesota. At Macalester, he has developed the introductory sequence in calculus and statistics as well as an introduction to computing for scientists. He’s co-authored the mosaic R package and written several textbooks: Understanding Nonlinear Dynamics, Introduction to Scientific Computation and Programming, and Statistical Modeling: A Fresh Approach.

PREREQUISITES OF THE COURSE:

>>>INTRODUCTION TO R<<<

DESCRIPTION OF THE COURSE:

In this introduction to R, you will master the basics of this beautiful open source language, including factors, lists and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis. With over 2 million users worldwide R is rapidly becoming the leading programming language in statistics and data science. Every year, the number of R users grows by 40% and an increasing number of organizations are using it in their day-to-day activities. Leverage the power of R by completing this R online course today!

>>>Intermediate R<<<

DESCRIPTION OF THE COURSE:

The intermediate R course is the logical next stop on your journey in the R programming language. In this R training you will learn about conditional statements, loops and functions to power your own R scripts. Next, you can make your R code more efficient and readable using the apply functions. Finally, the utilities chapter gets you up to speed with regular expressions in the R programming language, data structure manipulations and times and dates. This R tutorial will allow you to learn R and take the next step in advancing your overall knowledge and capabilities while programming in R.

Prepared by:https://www.facebook.com

About the Author...

Ed Neil O. Maratas an instructor of Jose Rizal Memorial State University, Dapitan Campus, Philippines as regular status. He earned his Bachelor of Science in Statistics at Mindanao State University-Tawi-Tawi College of Technology and Oceanography in the year 2003 and finished Master of Arts in Mathematics at Jose Rizal Memorial State University year 2009. He Became a researcher, a data analyst, and engaged to several projects linked to the university as data processor.

Prepared by:Ed Neil or you can visit the facebook pageStatisticss For Funfor

ShortcutLInk Here:

Visit Ad.fly Website Now

">
>>>Short URL link HERE<<<

Basically, AdFly is a link shortening service and unlike other link shortening services like bit.ly & goo.gl, AdFly lets you earn money from your links shortened using AdFly.

Statistics for Data Science and Business Analysis

Statistics for Data Science and Business Analysis

Statistics you need in the office: Descriptive & Inferential statistics, Hypothesis testing, Regression analysis

Description

Is statistics a driving force in the industry you want to enter? Do you want to work as a Marketing Analyst, a Business Intelligence Analyst, a Data Analyst, or a Data Scientist? Well then, you’ve come to the right place! >>>CLICK HERE TO ENROLL<<<

What you will learn..?

Understand the fundamentals of statistics
Learn how to work with different types of data
How to plot different types of data
Calculate the measures of central tendency, asymmetry, and variability
Calculate correlation and covariance
Distinguish and work with different types of distributions
Estimate confidence intervals
Perform hypothesis testing
Make data driven decisions
Understand the mechanics of regression analysis
Carry out regression analysis
Use and understand dummy variables
Understand the concepts needed for data science even with Python and R!
.

FOR MORE DETAILS >>>CLICK HERE TO ENROLL<<<

Complete Python Bootcamp: Go from zero to hero in Python 3

Learn Python like a Professional! Start from the basics and go all the way to creating your own applications and games!

Description

Become a Python Programmer and learn one of the employer's most requested skills of 2018! This is the most comprehensive, yet straight-forward, course for the Python programming language on Udemy! Whether you have never programmed before, already know basic syntax, or want to learn about the advanced features of Python, this course is for you! In this course, we will teach you Python 3. (Note, we also provide older Python 2 notes in case you need them) With over 100 lectures and more than 20 hours of video, this comprehensive course leaves no stone unturned! This course includes quizzes, tests, and homework assignments as well as 3 major projects to create a Python project portfolio! This course will teach you Python in a practical manner, with every lecture comes to a full coding screencast and a corresponding code notebook! Learn in whatever manner is best for you! We will start by helping you get Python installed on your computer, regardless of your operating system, whether its Linux, MacOS, or Windows, we've got you covered!

>>>CLICK HERE TO ENROLL<<<

What you will learn..?

Learn to use Python professionally, learning both Python 2 and Python 3!
Create games with Python, like Tic Tac Toe and Blackjack!
Learn advanced Python features, like the collections module and how to work with timestamps!
Learn to use Object Oriented Programming with classes!
Understand complex topics, like decorators.
Understand how to use both the Jupyter Notebook and create .py files
Get an understanding of how to create GUIs in the Jupyter Notebook system!
Build a complete understanding of Python from the ground up!
.

FOR MORE DETAILS >>>CLICK HERE TO ENROLL<<<

Tensorflow 2.0: Deep Learning and Artificial Intelligence

Computer Vision, Time Series Forecasting, and More!

Description

Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning. In other words, if you want to do deep learning, you gotta know Tensorflow. This course is for beginner-level students all the way up to expert-level students. How can this be? If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts. Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data). Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses Tensorflow 2.0, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions. This course is designed for students who want to learn fast, but there are also "in-depth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).

>>>CLICK HERE TO ENROLL<<<

What you will learn..?

Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
Image Recognition
Predict Stock Returns
Time Series Forecasting
Computer Vision
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Use Tensorflow Serving to serve your model using a RESTful API
Use Tensorflow's Distribution Strategies to parallelize learning
Low-level Tensorflow, gradient tape, and how to build your own custom models
Demonstrate Moore's Law using Code
.

FOR MORE DETAILS >>>CLICK HERE TO ENROLL<<<

Machine Learning, Data Science and Deep Learning with Python

Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks

Description

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too! If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 13 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned!

>>>CLICK HERE TO ENROLL<<<

What you will learn..?

Build artificial neural networks with Tensorflow and Keras
Classify images, data, and sentiments using deep learning
Make predictions using linear regression, polynomial regression, and multivariate regression
Data Visualization with MatPlotLib and Seaborn
Implement machine learning at massive scale with Apache Spark's MLLib
Understand reinforcement learning - and how to build a Pac-Man bot
Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
Use train/test and K-Fold cross validation to choose and tune your models
Build a movie recommender system using item-based and user-based collaborative filtering
Clean your input data to remove outliers
Design and evaluate A/B tests using T-Tests and P-Values

FOR MORE DETAILS >>>CLICK HERE TO ENROLL<<<

Or You Can Visit their Website

Discover and VISIT Udemy's featured courses!

Python for Data Science and Machine Learning Bootcamp

Python for Data Science and Machine Learning Bootcamp

Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!

>>>CLICK HERE TO ENROLL<<<

What you will learn..?

Use Python for Data Science and Machine Learning
Use Spark for Big Data Analysis
Implement Machine Learning Algorithms
Learn to use NumPy for Numerical Data
Learn to use Pandas for Data Analysis
Learn to use Matplotlib for Python Plotting
Learn to use Seaborn for statistical plots
Use Plotly for interactive dynamic visualizations
Use SciKit-Learn for Machine Learning Tasks
K-Means Clustering
Logistic Regression
Linear Regression
Random Forest and Decision Trees
Natural Language Processing and Spam Filters
Neural Networks
Support Vector Machines
.

FOR MORE DETAILS >>>CLICK HERE TO ENROLL<<<

Statistics for Data Science and Business Analysis

Statistics you need in the office: Descriptive & Inferential statistics, Hypothesis testing, Regression analysis

Description

Is statistics a driving force in the industry you want to enter? Do you want to work as a Marketing Analyst, a Business Intelligence Analyst, a Data Analyst, or a Data Scientist? Well then, you’ve come to the right place! >>>CLICK HERE TO ENROLL<<<

What you will learn..?

Understand the fundamentals of statistics
Learn how to work with different types of data
How to plot different types of data
Calculate the measures of central tendency, asymmetry, and variability
Calculate correlation and covariance
Distinguish and work with different types of distributions
Estimate confidence intervals
Perform hypothesis testing
Make data driven decisions
Understand the mechanics of regression analysis
Carry out regression analysis
Use and understand dummy variables
Understand the concepts needed for data science even with Python and R!
.

FOR MORE DETAILS >>>CLICK HERE TO ENROLL<<<

Complete Python Bootcamp: Go from zero to hero in Python 3

Learn Python like a Professional! Start from the basics and go all the way to creating your own applications and games!

Description

Become a Python Programmer and learn one of the employer's most requested skills of 2018! This is the most comprehensive, yet straight-forward, course for the Python programming language on Udemy! Whether you have never programmed before, already know basic syntax, or want to learn about the advanced features of Python, this course is for you! In this course, we will teach you Python 3. (Note, we also provide older Python 2 notes in case you need them) With over 100 lectures and more than 20 hours of video, this comprehensive course leaves no stone unturned! This course includes quizzes, tests, and homework assignments as well as 3 major projects to create a Python project portfolio! This course will teach you Python in a practical manner, with every lecture comes to a full coding screencast and a corresponding code notebook! Learn in whatever manner is best for you! We will start by helping you get Python installed on your computer, regardless of your operating system, whether its Linux, MacOS, or Windows, we've got you covered!

>>>CLICK HERE TO ENROLL<<<

What you will learn..?

Learn to use Python professionally, learning both Python 2 and Python 3!
Create games with Python, like Tic Tac Toe and Blackjack!
Learn advanced Python features, like the collections module and how to work with timestamps!
Learn to use Object Oriented Programming with classes!
Understand complex topics, like decorators.
Understand how to use both the Jupyter Notebook and create .py files
Get an understanding of how to create GUIs in the Jupyter Notebook system!
Build a complete understanding of Python from the ground up!
.

FOR MORE DETAILS >>>CLICK HERE TO ENROLL<<<

Tensorflow 2.0: Deep Learning and Artificial Intelligence

Computer Vision, Time Series Forecasting, and More!

Description

Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning. In other words, if you want to do deep learning, you gotta know Tensorflow. This course is for beginner-level students all the way up to expert-level students. How can this be? If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts. Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data). Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses Tensorflow 2.0, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions. This course is designed for students who want to learn fast, but there are also "in-depth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).

>>>CLICK HERE TO ENROLL<<<

What you will learn..?

Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
Image Recognition
Predict Stock Returns
Time Series Forecasting
Computer Vision
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Use Tensorflow Serving to serve your model using a RESTful API
Use Tensorflow's Distribution Strategies to parallelize learning
Low-level Tensorflow, gradient tape, and how to build your own custom models
Demonstrate Moore's Law using Code
.

FOR MORE DETAILS >>>CLICK HERE TO ENROLL<<<

Machine Learning, Data Science and Deep Learning with Python

Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks

Description

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too! If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 13 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned!

>>>CLICK HERE TO ENROLL<<<

What you will learn..?

Build artificial neural networks with Tensorflow and Keras
Classify images, data, and sentiments using deep learning
Make predictions using linear regression, polynomial regression, and multivariate regression
Data Visualization with MatPlotLib and Seaborn
Implement machine learning at massive scale with Apache Spark's MLLib
Understand reinforcement learning - and how to build a Pac-Man bot
Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
Use train/test and K-Fold cross validation to choose and tune your models
Build a movie recommender system using item-based and user-based collaborative filtering
Clean your input data to remove outliers
Design and evaluate A/B tests using T-Tests and P-Values

FOR MORE DETAILS >>>CLICK HERE TO ENROLL<<<

Or You Can Visit their Website

Discover and VISIT Udemy's featured courses!

Statistics Book Downloadable

Statistics

>>>CLICK HERE TO DOWNLOAD<<<
>>>CLICK HERE TO DOWNLOAD<<<

Statistics for people who (think they) hate statistics: Using Microsoft Excel 2016. Neil J. Salkind

>>>CLICK HERE TO DOWNLOAD<<<

Applied Time Series Analysis: A Practical Guide to Modeling and Forecasting by Terence C. Mills

Written for those who need an introduction, Applied Time Series Analysis reviews applications of the popular econometric analysis technique across disciplines. Carefully balancing accessibility with rigor, it spans economics, finance, economic history, climatology, meteorology, and public health. Terence Mills provides a practical, step-by-step approach that emphasizes core theories and results without becoming bogged down by excessive technical details. Including univariate and multivariate techniques, Applied Time Series Analysis provides data sets and program files that support a broad range of multidisciplinary applications, distinguishing this book from others.

>>>CLICK HERE TO DOWNLOAD<<<

Practical time series analysis: master time series data processing, visualization, and modeling using Python

Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python. The book starts with a descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality, and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python. The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python. What You Will Learn • Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project • Develop an understanding of loading, exploring, and visualizing time-series data • Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series • Take advantage of exponential smoothing to tackle noise in time series data • Learn how to use auto-regressive models to make predictions using time-series data • Build predictive models on time series using techniques based on auto-regressive moving averages • Discover recent advancements in deep learning to build accurate forecasting models for time series • Gain familiarity with the basics of Python as a powerful yet simple to write programming language

>>>CLICK HERE TO DOWNLOAD<<<

MUTLIVARIATE TIME SERIES ANALYSIS by Ruey S. Tsay

Multivariate Time Series Analysis: With R and Financial Applications is the much-anticipated sequel coming from one of the most influential and prominent experts on the topic of time series. Through a fundamental balance of theory and methodology, the book supplies readers with a comprehensible approach to financial econometric models and their applications to real-world empirical research. Differing from the traditional approach to multivariate time series, the book focuses on reader comprehension by emphasizing structural specification, which results in simplified parsimonious VAR MA modeling. Multivariate Time Series Analysis: With R and Financial Applications utilizes the freely available R software package to explore complex data and illustrate related computation and analyses. Featuring the techniques and methodology of multivariate linear time series, stationary VAR models, VAR MA time series and models, unit-root process, factor models, and factor-augmented VAR models, the book includes: • Over 300 examples and exercises to reinforce the presented content • User-friendly R subroutines and research presented throughout to demonstrate modern applications • Numerous datasets and subroutines to provide readers with a deeper understanding of the material Multivariate Time Series Analysis is an ideal textbook for graduate-level courses on time series and quantitative finance and upper-undergraduate level statistics courses in time series. The book is also an indispensable reference for researchers and practitioners in business, finance, and econometrics.

PROBABILITY

>>>CLICK HERE TO DOWNLOAD<<<

Probability For Dummies Deborah Rumsey

Packed with practical tips and techniques for solving probability problemsIncrease your chances of acing that probability exam - or winning at the casino!Whether you're hitting the books for a probability or statistics course or hitting the tables at a casino, working out probabilities can be problematic. This book helps you even the odds. Using easy-to-understand explanations and examples, it demystifies probability - and even offers savvy tips to boost your chances of gambling success!Discover how to* Conquer combinations and permutations* Understand probability models from binomial to exponential* Make good decisions using probability* Play the odds in poker, roulette, and other games

>>>CLICK HERE TO DOWNLOAD<<<

Suhov Y., Kelbert M.

>>>CLICK HERE TO DOWNLOAD<<<

Schaum's Outline of Probability and Statistics, 3rd Ed. (Schaum's Outline Series) John Schiller, R. Alu Srinivasan, Murray Spiegel

Confusing Textbooks? Missed Lectures? Not Enough Time? Fortunately for you, there's Schaum's Outlines. More than 40 million students have trusted Schaum's to help them succeed in the classroom and on exams. Schaum's is the key to faster learning and higher grades in every subject. Each Outline presents all the essential course information in an easy-to-follow, topic-by-topic format. You also get hundreds of examples, solved problems, and practice exercises to test your skills. This Schaum's Outline gives you Practice problems with full explanations that reinforce knowledge Coverage of the most up-to-date developments in your course field In-depth review of practices and applications Fully compatible with your classroom text, Schaum's highlights all the important facts you need to know. Use Schaum's to shorten your study time-and get your best test scores! An enhanced ebook is now available with 20 videos of professors showing you exactly how to solve probability and statistics problems! Select the Kindle Edition with Audio/Video from the available formats. Schaum's Outlines-Problem Solved.

Artificial Intelligence

>>>>CLICK HERE TO DOWNLOAD<<<

Step into the future with AI The term "Artificial Intelligence" has been around since the 1950s, but a lot has changed since then. Today, AI is referenced in the news, books, movies, and TV shows, and the exact definition is often misinterpreted. Artificial Intelligence For Dummies provides a clear introduction to AI and how it's being used today. Inside, you'll get a clear overview of the technology, the common misconceptions surrounding it, and a fascinating look at its applications in everything from self-driving cars and drones to its contributions in the medical field. Learn about what AI has contributed to society Explore uses for AI in computer applications Discover the limits of what AI can do Find out about the history of AI The world of AI is fascinating--and this hands-on guide makes it more accessible than ever!