Simple Regression Analysis in Public Health

Course Link: https://www.coursera.org/learn/simple-regression-analysis-public-health

Simple Regression Analysis in Public Health
This course provides an introduction to regression in public health and health policy. The course will use linear regression as a model for exploratory data analysis and statistical reasoning.

Learning Outcomes

Upon completion of the course, you will be able to:
1. Describe the process of using regression in public health and health policy
2. Design a regression model for exploratory data analysis
3. Specify the level of significance of your regression model
4. Calculate mean differences using linear regression
5. Interpret regression coefficients
6. Compute means and variances using regression
7. Model a continuous variable

In this course, you will have a very brief introduction to regression and the basic assumptions underlying it. Then we will dive into regression models, linear regression, and the weights and regression discontinuous variable models. We will use linear regression to examine the relationship between variables with a continuous variable and a binary outcome such as using or not using a therapy. We will use regression models to examine the relationship between a continuous variable and a binary outcome such as taking a pill or not taking a pill. We will use regression discontinuity models to examine the relationship between a continuous variable and a binary outcome such as taking multiple pills. We will also use regression discontinuity models to examine the relationship between a continuous variable and a binary outcome such as using multiple pills.
The course is primarily designed for practitioners or students who are interested in basic statistics and regression, but anyone with some mathematical or computer science background should be able to follow the lecture and understand the concepts.

Suggested Readings

The course is designed primarily for people who are interested in basic statistics and regression, but anyone with some mathematical or computer science background should be able to follow the lecture and understand the concepts. In particular, those who want to apply the statistical concepts gained in this course to their own work should complete the Quickstart for this course on how to use regression models. In particular, those who want to apply regression to a large number of variables, be it positive or negative, should complete the remainder of the course expanding their knowledge of regression and then dive into the more in-depth course later in the specialization.

Course Overview video: https://youtu.be/g-oaUoU9wO9uU

Course logo: https://www.flickr.com/photos/publicschools/3468922981/in/set-7215766202599369161240969930918_98626151680378539648_n.jpg

Course cover image by John Carl Dreyer: https://www.flickr.com/photos/publicschools/3468922981/in/set-72157662025993691612409630918_98626151680378539648_o.jpg

Course cover image by John

Course Link: https://www.coursera.org/learn/simple-regression-analysis-public-health

Biostatistics in Public Health Specialization

Course Link: https://www.coursera.org/specializations/biostatistics-public-health

Biostatistics in Public Health Specialization
This specialization is designed for students who aspire to be clinicians and health care administrators who are interested in the unravelling of complex public health problems. The course focuses on the application of the basic principles of biostatistics in public health practice, focusing on the application of the statistical approach known as ‘BInS’. The course also focuses on the application of the ‘Informative Multi-Stakeholder Model’ for decision-making. In this way, students gain exposure to the most important concepts in public health decision-making, while also getting practice in the statistical application of multivariate methods. Students will also have basic understanding of the decision-making process in public health, with the aim of making better decisions in the future.

In this specialization, we will:
* Develop a personal interest in the field of biostatistics and its application in public health decision-making
* Develop a background in the field of public health decision-making and its challenges
* Apply the basic concepts and methods of multivariate analysis
decision-making process in public health
public health decision-making

We hope that this course will give you valuable experience in the field of biostatistics and inform you about the public health decisions that are being made in different parts of the world.Bias and confounding
Informative multi-stakeholder model
Informative decision-making
Informative multi-stakeholder model and multivariate analysis
Biological Principles of Sexual Behavior
The course addresses the dynamic nature of sexual behavior and the factors that influence sexual health and development. We will examine the spectrum of sexual behaviors (including oral, anal, and vaginal) and the different developmental stages at play in these behaviors. The course also addresses issues of obesity and sexual dysfunction in the context of healthy sexual functioning.

The course is designed to address a variety of topics and questions, while at the same time providing a firm foundation for students to become educated and empowered consumers of sexual information.

The main goal of the course is to provide a basic understanding of the biology of sexual behavior and the various developmental stages at play in various sexual behaviors. The course will discuss the dynamic nature of sexual behavior (including oral, anal, and vaginal), its factors, influences, and consequences. The course will also address issues of obesity and sexual dysfunction in the context of healthy sexual functioning.

The course is designed for both males and females, students. Both genders can benefit from the course materials. The course materials will be presented by instructors with an interdisciplinary approach, connecting the many views and viewpoints of the UdK team. The course is for anyone interested in sexual health and sexual behavior; students, teachers, counselors, healthcare providers, epidemiologists, and anyone else who wants to contribute to the conversation about sexual health.Introduction
Sexual Behaviors & Attitudes
Development
Obesity & Sexual Dysfunction
BIOGRAPHY, NAMES, AND CREDIT
By drawing on the rich tapestry of Bethania (Milena) and the centuries of family and cultural traditions in her homeland, this ambitious course invites learners to explore the complex webs of family, kinship, and kinship ties. Through biographical information and nomenclature, we will explore the stories and complex web of relationships that enrich our understanding of the Jewish past and preserve its contemporary dimensions.

In this course, we explore the unique challenges of traveling to visit relatives and learn how to translate experiences and names to modern times. We will also investigate the complex issues of intermarriage and travel, addressing issues such as religious law, customs and traditions, and geographical and cultural backgrounds as they relate to the creation of new families.

The course is a joint effort of the Reinventing Israel Educational Society and Yad Vashem. It was conceived as an open, public, and free for all, combining the talents of both the scholars and the common people. The course will appeal to both advanced and novice learners alike because of the emphasis on the shared history, research, and originality. The course is an open-access, peer-reviewed, cross-disciplinary, and free for all. The course is ideal for students in graduate school, as well as those who do not attend university. It will appeal to all those interested in learning more about the history of the Jewish people and the creation of new Jewish communities in the Middle East.

This course was co-created by Tel Aviv University and Yad Vashem – the World Holocaust Remembrance Center. During the course, you will have the opportunity to interact with a variety of experts

Course Link: https://www.coursera.org/specializations/biostatistics-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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