Machine Learning

Course Link: https://www.coursera.org/learn/machine-learning

Machine Learning in Finance
We all have heard about “automate” and “crowdsource” – these are old ways of saying “machine learning” that are beginning to be applied to finance. What is “machine learning” and how is it different from traditional methods of prediction? Learn about these new “machine learning” techniques and applications in finance.

This course will cover the “machine learning” and “crowdsourced” methods of investing, derivatives and portfolio optimization, and how these techniques can help us make more informed decisions. We will focus on the algorithms used to train and use on GPUs, the performance of these techniques and how they can help us make more informed decisions.GPUs
GPUs for trading
Crowdsourced optimization
Machine learning
Mining Digital Debris with Sporadic Dataflow
This course introduces you to the distributed algorithms used to mine digital debris (DM) for metals, gold, and other non-conventional resources in the real world. You learn how to use the common dataflow techniques to accelerate the process of mining DM, how to manage your company’s infrastructure to keep your business secure, and how to make sure that your engineers know how to work with data.

This course is the extension of the previous course in the specialization entitled Mining Debris with Hadoop and Spark. In this course, we will learn how to use the distributed algorithms developed for that course to mine and handle a much larger dataset than in the previous course. We will practice various distributed algorithms and techniques to mine a much larger dataset and to mine with a reasonable latency. We will use the common dataflow techniques to speed up the process of mining, manage your company’s infrastructure, and ensure that your engineers know how to work with data.

This course requires the use of a Windows version of Python 3.

What you’ll learn:

The full picture can be seen in the “Who this class is for” section. We will see that it is for everyone. In addition, because this course requires the use of a Windows version of Python, we will also see that it is suitable for all skill levels.

At the end of this course, you will be able to:
1. Explain the class and the course material.
2. Leverage the full power of the distributed file system to mine and handle a much larger dataset than in the previous course.
3. Mine and mine with a reasonable latency.
4. Mine with a reasonable scalability using multiplexing.
5. Leverage the power of Hadoop and Spark on your own for mining, analyzing, and/or simulation.Digitally-Manipulated Data (DM)
Data Coordination
Optimization
Crowdsourced Dataflow
<|start Course Link: https://www.coursera.org/learn/machine-learning

Machine Learning: Classification

Course Link: https://www.coursera.org/learn/ml-classification

Machine Learning: Classification and Training
Machine Learning is the application of ML to classify and predict data. The course focuses on the design of machine learning models using linear model. The focus is on the design of such a model as it is the most common and flexible tool of ML algorithms. The course is suitable for engineers who want to design and tune their ML algorithms for specific use-cases.

The course assumes prior completion of Introduction to ML and Machine Learning.Machine Learning principles
Linear model, normal distribution, and feature extraction
Machine learning algorithms
Classification and training: classification, regularization, and regularization
Measuring Success in Business
This course is aimed at helping you measure your business success. Drawing on the insights you’ve gained through the different course disciplines, we’ll take a closer look at what really matters in measuring success, so that you can focus on what really matters. We’ll do so by looking at five key areas:

– Your brand. How well is your brand positioned and positioned to take advantage of the vast array of services and opportunities the Internet provides?
– Your product or service. How has your product or service evolved over time to position you to advantage in the varied and everchanging marketplace?
– Your customers. How have your customers responded to your changes and additions over the years?
– Your competition. How are they? What has their response been to your changes and additions?
– Your market. How has your market positioned over time to take advantage of the various opportunities the Internet provides?
– Your market. How has your market positioned to take advantage of the various opportunities the Internet provides?
– Your market. How has your market positioned to take advantage of the various opportunities the Internet provides?
– Your competition. How have your competitors responded to your changes and additions over the years?
– Your market. How has your market positioned to take advantage of the various opportunities the Internet provides?
– Your competitors. How have your competitors responded to your changes and additions over the years?
– Your market. How has your market positioned to take advantage of the various opportunities the Internet provides?
– Your market. How has your market positioned to take advantage of the various opportunities the Internet provides?
– Your market. How has your market positioned to take advantage of the various opportunities the Internet provides?
– Your competition. How have your competitors responded to your changes and additions over the years?
– Your business. How has your business positioned over time to take advantage of the variety the Internet provides?
– Your competitors. How have your competitors responded to your changes and additions over the years?
– Your market. How has your market positioned to take advantage of the variety the Internet provides?
– Your competition. How have your competitors responded to your changes and additions over the years?

Course Link: https://www.coursera.org/learn/ml-classification

Regression Modeling in Practice

Course Link: https://www.coursera.org/learn/regression-modeling-practice

Regression Modeling in Practice
This course is all about modeling in a regression. In the first part, we discuss linear and logistic regression models, respectively. You will be introduced to the SAS and Python packages that will allow you to model and explore regression coefficients. In the second half of the course, we discuss multiple regression models, and you learn how to fit and evaluate regressions. In the end, you will be able to implement models in Python that allow you to explore causal effects and confounding variables. You’ll also learn to use Excel for multiple regression modeling.

Pre-requisites

You will need to have completed the course “Machine Learning and Probability: Linear Regression and Logistic Regression” (https://www.coursera.org/learn/lrm-linear-regression-logistic-regression.html).

Suggested Readings

You will need about 10 additional readings, each of which can be independent. You can find a complete list of required readings for this course on the course syllabus.

Suggested Readings

If you intend to study at a college in the future, you might want to consult a college librarian’s guide to find out what’s required for a college library to obtain a library card. In addition, you might want to consult an academic textbook or some online resources about statistical modeling.On the `R-Loop’ Model
Parametric Regression (PRL)
Multiple Regression (MR)
Exploring Causal Effects
Resilience in Children Exposed to Trauma, Disaster and War: Global Perspectives
Children are among the most vulnerable members of society to experience trauma, war, natural and man-made disasters, and complex conflict. Understanding the characteristics and risk factors of resilient children and adolescents is important in guiding policy makers and other professionals in their ability to respond to trauma, war, disaster and conflict.

In this course, you will learn the characteristics of resilience and heritabilities in children at risk of experiencing trauma, war, disaster, or conflict. You will learn the frameworks of trauma and heritabilities, and learn the components of a validated assessment for children at risk. You will also learn the tools to evaluate adolescents at risk and what interventions are appropriate.

In the course, you will learn about the process of unique etiology for traumatic brain injury (TBI), traumatic brain injury (TBI) and congenital trauma. You will learn about the pathology and pathophysiology of the child, adolescent and adult brain and the various injuries that can result from concussive trauma, impact cramps and fractures, as well as chronic traumatic encephalopathy (CTE), chronic traumatic encephalopathy (CTE) and cerebral palsy (CPE). You will also learn about the neuropathology of the child, adolescent and adult brain, the different

Course Link: https://www.coursera.org/learn/regression-modeling-practice

Raising Capital: Credit Tech, Coin Offerings, and Crowdfunding

Course Link: https://www.coursera.org/learn/credit-tech

Raising Capital: Credit Tech, Coin Offerings, and Crowdfunding
Credit and capital markets have been the key drivers of economic growth across the globe for several decades. Businesses have always sought to expand credit, but the process has often been complicated by complex collateral and financing arrangements. In this course we will learn about new techniques used in the current credit and capital markets and how these techniques are beginning to disrupt the traditional patterns of capital markets. We will discuss the most recent advances in digital technology, traditional currencies, alt-coins, and smart contracts. We will also discuss the basics of crowdfunding, the crowd and how it is different from traditional fundraising. We’ll also look at new types of crowdfunding and how to approach your fundraising goals. We’ll also look at how crowdfunding is different than traditional fundraising and how to approach your goals differently. Finally, you’ll learn key techniques for assessing performance and assessing the equity of a company. By the end of this course, you’ll understand how companies raise capital and how to raise the most money possible.

This is the fifth and final course in the Complete Credit & Capital Markets Specialist specialization. If you would like to learn about the complete specialization, please take a look at the Curriculum Vitae or http://www.coursera.org/specializations/credit-tech-coin-offerings-and-crowdfunding.Credit and Capital Markets
Crypto and Crypto-currencies
Ethereum Classic
Blockchain, Decentralization & Crowdfunding
Raising Capital: Fundraising without Bosses
This course gives you full access to the Fundraising Motivation Capability (FMOC) toolkit, which enables you to develop a deep understanding of how to develop and improve your fundraising strategy. You’ll learn how to evaluate proposals and assess fundraising needs and how to develop fundraising plans that bring in the cash you need. You’ll also learn how to evaluate pitch decks and other material to decide on the best fundraising strategy for your company or organization. Learn to appreciate the role of individual donors in determining whether a fundraising effort is successful or not, and how to use the Fundraising Motivation Capability (FMOC) toolkit to develop a deep, meaningful understanding of your fundraising strategy.

After completing this course, you will be able to:

– Describe the Fundraising Motivation Capability (FMOC) toolkit and how to assess funding needs and develop fundraising strategies.
– Learn to evaluate pitches and fundraisers.
– Understand the role of individual donors in determining whether a fundraising effort is successful or not.
– Understand the difference between a pitch deck and a raffle ticket.
– Assess fundraising needs and develop a pitch deck to

Course Link: https://www.coursera.org/learn/credit-tech

Logistic Regression in R for Public Health

Course Link: https://www.coursera.org/learn/logistic-regression-r-public-health

Logistic Regression in R for Public Health
We all know that one of the best ways to increase public health is by improving public health outcomes. But what is the best way to achieve this outcome? How should we build better public health systems and programs? In this course, we will learn the fundamentals of logistic regression and how to use alternative models to inform decision making. We will then focus on the models and models that inform public health decisions in developed and developing countries alike. We will also explore different types of public health programs and services and the different types of decision making that is needed to evaluate and adjust to changes in program structure and funding. We will also explore models that inform the decision making of population groups, local governments, commercialization, and private insurance companies. By the end of the course, you should be able to use logistic regression models to address your data. You should also know how to construct a model and construct alternative models to inform decision making amongst your data. The course should also give you an overview of different types of regression models. We also look at models that inform the decision making of populations in industrialized countries alike. The course should also give you an insight into how data is used in public health. We look at multilevel models and models that inform decision making amongst your data. You will also be introduced to different types of statistical models and how to use and interpret the logistic regression models used in public health. We also look at models that inform the decision making of populations in industrialized countries as well as other industrialized economies. This course should also give you an insight into the challenges and opportunities that face researchers and administrators in the field of public health. The course should also give you an insight into the challenges and opportunities that face researchers and administrators in the field of public health. Modeling Public Health Problems
Alternative Models to Inform Public Health Decisions
Public Health Interventions
Programs and Services: From Epidemiology to Programmes and Services
Learn to program R packages
This is the second course in the specialization about learning how to use and extend R packages to perform additional analysis and interpret commands. The focus in this course is on taking advantage of the R language and tools for additional analysis and interpretation. In particular, we look at how to include interactive functions, how to use functions within a package, how to use package comments, how to organize your R package, and how to evaluate its quality. We will also look at how to reuse code and extend it, and how to use packages to build more sophisticated programs. We will use R packages to accomplish these goals.

Upon completing this course, you will be able to:
1. Read and parse files from/to/extract them
2. Write R packages
3. Include interactive functions
4. Use functions within a package
5. Organize your R packages
6. Evaluate quality of a package
7. Use packages to

Course Link: https://www.coursera.org/learn/logistic-regression-r-public-health

Statistical Analysis with R for Public Health Specialization

Course Link: https://www.coursera.org/specializations/statistical-analysis-r-public-health

Statistical Analysis with R for Public Health Specialization
This course introduces statistical concepts for use in public health research, including exploratory data analysis, hypothesis testing, and confidence intervals. Weekly lecture videos will introduce statistical concepts and help learners to apply these concepts to real data sets. An emphasis is placed on statistical inference and inference of causal effects. A strong emphasis is placed on the use of continuous data in statistical analysis.

The purpose of this course is to acquaint the learner with the basic concepts and principles of statistics, including the use of inferential statistics. The course will introduce statistical models and inference techniques, and will help to familiarize the learner with the basic statistical concepts. The course will also introduce random-effects models and random-effects ANOVA. The course will also introduce the use of Cox regression and two-group ANOVA, as well as random-effects modeling of covariance and correlation. It will also introduce the SE method, which is used for continuous data analysis. The course will also introduce the use of random-effects modeling and random-effects ANOVA, as well as random-effects modeling of covariance and correlation. It will also introduce the SE method, which is used for continuous data analysis.

The course uses RStudio as its statistical software and as an IDE. The course is designed to run on Linux or Mac OS X. The course uses an open-source software package repository (https://github.com/statistics-in-r/rstudio) for each dataset, and each package contains a set of pre-built R packages that you can use directly. There are also included open-source statistical software packages (RStudio, R, and RStudio Extension), which you can install using the Software Configuration Utility (SCU) included with the course.Introduction to Stats and Networking
Covariance
Cox Regression
ANOVA
Statistical Model Checking in Practice
This course covers model-checking techniques for statistical inference of non-negative values. We will learn what type of model to use for our data and its goodness-of-fit. We will use statistical inference methods that are appropriate for our data set and some statistical modeling that we may use. We will also introduce a set of techniques for performing robust statistical modeling and inference that are not normally performed using machine learning methods. The course also covers model selection and optimization. We will learn how to implement robust statistical modeling and inference by using multiple data sets and different optimization methods.Intro to Model Checking
Non-negative values and model selection
Probability and conditional independence
Inference for Continuous Data
Statistical Model Checking for Machine Learning
This course covers model checking techniques for machine learning. We will learn the basics of how to evaluate non-negative numbers and how to construct confidence intervals. We will learn how to construct confidence intervals using the formatter and the construct vector calculus and the corresponding non-linear regression model. We will also explain the concept of a normal distribution and how to compute average values. We will also explain the concept of normal distribution over large numbers of variables. We will use machine learning techniques for evaluating large datasets, which will be covered in depth in later courses on supervised learning and reinforcement learning. We will also explain the concepts of a normal distribution over large numbers of variables and the concept of average over large numbers of variables. We will also introduce the tensor product and average over vectors using TensorFlow and explain how to perform average over large numbers of variables. We will then apply these concepts to a case study in machine learning used in the machine learning course of the specialization. We will see how the machine learning technique can be used to detect misclassified data in large datasets. We will see how the machine learning technique can be used to detect training errors even for datasets that have been cleaned and optimized. We will see how the machine learning technique can be used to detect errors even for datasets that have been cleaned and optimized. We will see how classification problems can be addressed with machine learning. We will see how to use the classification models that are most effective in detecting misclassified data. We will see how to evaluate the performance of a model by performing a variety of analysis methods and performing a variety of classification tasks. We will learn how to use the classification models that are most effective in detecting errors even for datasets that have been cleaned and optimized. We will see how to use the classification models that are most effective in detecting misclassified data. We will see how to evaluate the performance of a classifier by performing a variety of analysis methods and performing a variety of classification tasks. We will also introduce the random variables and random variables for non-linear regression models. We will see how to construct confidence intervals and evaluate non-linear regression models. We will learn how to implement the regression models and evaluate the performance of the models. We will also explain the concept

Course Link: https://www.coursera.org/specializations/statistical-analysis-r-public-health

Best Coursera Courses for Machine Learning

Here is a list of best coursera courses for machine learning.

1. Machine Learning

As the first machine learning mooc course, this machine learning course provided by Stanford University and taught by Professor Andrew Ng, which is the best machine learning online course for everyone who want to learn machine learning. The content include:

  • Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
  • Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
  • Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

2. Machine Learning Specialization

This Machine Learning specialization provided by University of Washington, which provides a case-based introduction to the exciting, high-demand field of machine learning. Students will learn to analyze large and complex datasets, build applications that can make predictions from data, and create systems that adapt and improve over time. In the final Capstone Project, students will apply their skills to solve an original, real-world problem through implementation of machine learning algorithms. The Machine Learning specialization include 4 sub related courses:

1) Machine Learning Foundations: A Case Study Approach

This is the first course of the Machine learning specialization, which will make students get hands-on experience with machine learning from a series of practical case-studies. By the end of the course, student will be able to:

  • Identify potential applications of machine learning in practice.
  • Describe the core differences in analyses enabled by regression, classification, and clustering.
  • Select the appropriate machine learning task for a potential application.
  • Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
  • Represent your data as features to serve as input to machine learning models.
  • Assess the model quality in terms of relevant error metrics for each task.
  • Utilize a dataset to fit a model to analyze new data.
  • Build an end-to-end application that uses machine learning at its core.
  • Implement these techniques in Python.

2)Machine Learning: Regression

This is the second course of the Machine learning specialization. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data — such as outliers — on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to:

  • Describe the input and output of a regression model.
  • Compare and contrast bias and variance when modeling data.
  • Estimate model parameters using optimization algorithms.
  • Tune parameters with cross validation.
  • Analyze the performance of the model.
  • Describe the notion of sparsity and how LASSO leads to sparse solutions.
  • Deploy methods to select between models.
  • Exploit the model to form predictions.
  • Build a regression model to predict prices using a housing dataset.
  • Implement these techniques in Python.

3) Machine Learning: Classification

This is the third course of the Machine learning specialization. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier.

4) Machine Learning: Clustering & Retrieval

This is the fourth course of the Machine learning specialization. In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce. Learning Outcomes: By the end of this course, you will be able to: -Create a document retrieval system using k-nearest neighbors. -Identify various similarity metrics for text data. -Reduce computations in k-nearest neighbor search by using KD-trees. -Produce approximate nearest neighbors using locality sensitive hashing. -Compare and contrast supervised and unsupervised learning tasks. -Cluster documents by topic using k-means. -Describe how to parallelize k-means using MapReduce. -Examine probabilistic clustering approaches using mixtures models. -Fit a mixture of Gaussian model using expectation maximization (EM). -Perform mixed membership modeling using latent Dirichlet allocation (LDA). -Describe the steps of a Gibbs sampler and how to use its output to draw inferences. -Compare and contrast initialization techniques for non-convex optimization objectives. -Implement these techniques in Python.

3. Mathematics for Machine Learning Specialization

This Mathematics for Machine Learning Specialization provided by Imperial College London, which let students learn about the prerequisite mathematics for applications in data science and machine learning. The Machine Learning specialization include 3 courses:

1) Mathematics for Machine Learning: Linear Algebra

This is the first course of the Mathematics for Machine Learning Specialization. In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets – like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.

2) Mathematics for Machine Learning: Multivariate Calculus

This is the second course of the Mathematics for Machine Learning Specialization, which intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future.

3) Mathematics for Machine Learning: PCA

This is the third course of the Mathematics for Machine Learning Specialization. This course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We’ll cover some basic statistics of data sets, such as mean values and variances, we’ll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we’ll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you’ll be familiar with important mathematical concepts and you can implement PCA all by yourself.

4. Advanced Machine Learning Specialization

This Advanced Machine Learning Specialization provided by National Research University Higher School of Economics and Yandex, which let students Deep Dive Into The Modern AI Techniques, and teach computer to see, draw, read, talk, play games and solve industry problems. The Machine Learning specialization include 7 courses:

1) Introduction to Deep Learning
2) How to Win a Data Science Competition: Learn from Top Kagglers
3) Bayesian Methods for Machine Learning
4) Practical Reinforcement Learning
5) Deep Learning in Computer Vision
6) Natural Language Processing
7) Addressing Large Hadron Collider Challenges by Machine Learning

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