Data Science and Machine Learning Mathematics and Statistics

Category: Tutorial


Posted on 2020-03-03, by perica123.

Description

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Data Science and Machine Learning Mathematics and Statistics
New | h264, yuv420p, 1280x720 |ENGLISH, aac, 44100 Hz, stereo | 16h 19 mn | 5.94 GB
Instructor: The DataScience Stealth

Learn the Mathematics, Statistics and Probability behind Data Science, Machine Learning, Artificial Intelligence!
What you'll learn

Students will learn Introduction to Machine Learning
They will learn what is Supervised and Unsupervised Learning
They will learn Regression
They will learn Bayesian Decision Theory
They will learn Parametric Methods
They will learn The Bayes' Estimator
They will learn Clustering
They will learn Expectation-Maximization Algorithm and much more!

Requirements

Just some high school math

Description

Do you want to become a Data Scientist? Are you willing to learn Machine Learning? Well you're at the right place!!

The average salary for a Machine Learning Engineer is $138,920 per year in the United States by Indeed.

Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed ~ by Wikipedia.

Machine learning can easily consume unlimited amounts of data with timely analysis and assessment. This method helps review and adjusts your message based on recent customer interactions and behaviors. Once a model is forged from multiple data sources, it has the ability to pinpoint relevant variables. This prevents complicated integrations, while focusing only on precise and concise data feeds.

Machine learning algorithms tend to operate at expedited levels. In fact, the speed at which machine learning consumes data allows it to tap into burgeoning trends and produce real-time data and predictions

1. Churn analysis - it is imperative to detect which customers will soon abandon your brand or business. Not only should you know them in depth - but you must have the answers for questions like "Who are they? How do they behave? Why are They Leaving and What Can I do to keep them with us?"

2. Customer leads and conversion - you must understand the potential loss or gain of any and all customers. In fact, redirect your priorities and distribute business efforts and resources to prevent losses and refortify gains. A great way to do this is by reiterating the value of customers in direct correspondence or via web and mail-based campaigns.

3. Customer defections - make sure to have personalized retention plans in place to reduce or avoid customer migration. This helps increase reaction times, along with anticipating any non-related defections or leaves.

Many hospitals use this data analysis technique to predict admissions rates. Physicians are also able to predict how long patients with fatal diseases can live.

Insurance agencies across the world are also able to do the following:

Predict the types of insurance and coverage plans new customers will purchase.

Predict existing policy updates, coverage changes and the forms of insurance (such as health, life, property, flooding) that will most likely be dominant.

Predict fraudulent insurance claim volumes while establishing new solutions based on actual and artificial intelligence.

Machine learning is proactive and specifically designed for "action and reaction" industries. In fact, systems are able to quickly act upon the outputs of machine learning - making your marketing message more effective across the board.

So in this course Machine Learning, Data Science and Neural Networks + AI we will discover topics:

Introduction

Supervised Learning

Bayesian Decision Theory

Parametric Methods

Multivariate Methods

Dimensionality Reduction

Clustering

Nonparametric Methods

Decision Trees

McNemar's Test

Hypothesis Testing

Bootstrapping

Temporal Difference Learning

Reinforcement Learning

Stacked Generalization

Combining Multiple Learners

d-Separation

Undirected Graphs: Markov Random Fields

Hidden Markov Models

Regression

Kernel Machines

Multiple Kernel Learning

Normalized Basis Functions

The Perceptron

and much more!!

Data science

Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems.

At the core is data. Troves of raw information, streaming in and stored in enterprise data warehouses. Much to learn by mining it. Advanced capabilities we can build with it. Data science is ultimately about using this data in creative ways to generate business value

How do data scientists mine out insights? It starts with data exploration. When given a challenging question, data scientists become detectives. They investigate leads and try to understand patterns or characteristics within the data. This requires a big dose of analytical creativity.

Then as needed, data scientists may apply the quantitative technique in order to get a level deeper - e.g. inferential models, segmentation analysis, time series forecasting, synthetic control experiments, etc. The intent is to scientifically piece together a forensic view of what the data is really saying.

This data-driven insight is central to providing strategic guidance. In this sense, data scientists act as consultants, guiding business stakeholders on how to act on findings.

A common personality trait of data scientists is they are deep thinkers with intense intellectual curiosity. Data science is all about being inquisitive - asking new questions, making new discoveries, and learning new things. Ask data scientists most obsessed with their work what drives them in their job, and they will not say "money". The real motivator is being able to use their creativity and ingenuity to solve hard problems and constantly indulge in their curiosity.

Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.

The five stages of the data science life cycle: Capture, (data acquisition, data entry, signal reception, data extraction); Maintain (data warehousing, data cleansing, data staging, data processing, data architecture); Process (data mining, clustering/classification, data modeling, data summarization); Analyze (exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis); Communicate (data reporting, data visualization, business intelligence, decision making).

Effective data scientists are able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions. These skills are required in almost all industries, causing skilled data scientists to be increasingly valuable to companies.

Machine Learning

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as e x a mples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the e x a mples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Machine learning algorithms are often categorized as supervised or unsupervised.

Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It's a science that's not new - but one that has gained fresh momentum.

Here are a few widely publicized e x a mples of machine learning applications you may be familiar with:

The heavily hyped, self-driving Google car? The essence of machine learning.

Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.

Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.

Fraud detection? One of the more obvious, important uses in our world today.

Machine learning algorithms find natural patterns in data that generate insight and help you make better decisions and predictions. They are used every day to make critical decisions in medical diagnosis, stock trading, energy load forecasting, and more. For e x a mple, media sites rely on machine learning to sift through millions of options to give you song or movie recommendations. Retailers use it to gain insight into their customers' purchasing behavior.

Data Analysis

Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. Whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision. This is nothing but analyzing our past or future and making decisions based on it. For that, we gather memories of our past or dreams of our future. So that is nothing but data analysis. Now same thing analyst does for business purposes, is called Data Analysis.

Data analysis tools make it easier for users to process and manipulate data, analyze the relationships and correlations between data sets, and it also helps to identify patterns and trends for interpretation.

There are several types of data analysis techniques that exist based on business and technology. The major types of data analysis are:

Text Analysis

Statistical Analysis

Diagnostic Analysis

Predictive Analysis

Prescriptive Analysis

Who this course is for:

Anyone who want to learn Machine Learning
Anyone who want to become a Data Scientist
Anyone who is interested in Artificial Intelligence
Anyone who want to start their career in Data Science

Homepage
http://anonymz.com/?https://www.udemy.com/course/data-science-and-machine-learning-mathematics-and-statistics-y/


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