Keep scrolling. Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.

Address of the bookmark: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

Live online courses on statistics with R based on this book, led by the author, are offered regularly; see this page for more information and dates.

The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. The aim of Modern Statistics with R is to introduce you to key parts of the modern statistical toolkit. It teaches you:

• Data wrangling - importing, formatting, reshaping, merging, and filtering data in R.
• Exploratory data analysis - using visualisations and multivariate techniques to explore datasets.
• Statistical inference - modern methods for testing hypotheses and computing confidence intervals.
• Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting.
• Simulation - using simulation techniques for sample size computations and evaluations of statistical methods.
• Ethics in statistics - ethical issues and good statistical practice.
• R programming - writing code that is fast, readable, and (hopefully!) free from bugs.

The book includes plenty of examples and more than 200 exercises with worked solutions. The datasets used for the examples and the exercises can be downloaded here.

Address of the bookmark: https://www.modernstatisticswithr.com/

# install.packages('VennDiagram')
library(VennDiagram)

Second step: Load data

Add filepath if “catdoge.csv” is not in working-directory.

d <- read.csv("catdoge.csv")

Address of the bookmark: http://rstudio-pubs-static.s3.amazonaws.com/13301_6641d73cfac741a59c0a851feb99e98b.html

The tutorial is divided in three main parts:

• In the Sequence part, you will see how to look efficiently for a particular protein sequence, how to blast it against the database of your choice to find homologues, how to perform a multiple alignment of the homologues you've selected and how to edit this alignment.
• The Structure part is about molecular visualization, homology modeling and structural domain prediction.
• In the Function part, you will be introduced to you 3 useful servers to investigate the function of a protein. i.e. finding interactors, co-expressed genes, see a phylogenetic profile, easily access papers citing your gene etc ...

During all the three parts, we will use the S. cerevisiae VPS36 protein as an example.

Address of the bookmark: http://www.mrc-lmb.cam.ac.uk/rlw/text/bioinfo_tuto/introduction.html

Python is a very simple language, and has a very straightforward syntax. The simplest directive in Python is the "print" directive - it simply prints out a line (and also includes a newline).

Create a file Hello.py

print("Hello, Python World !.")

Run

python3 Hello.py

To take care of this issue, many ways are opened to the researchers to improve the quality of the algorithms:

1. Usage of new information processing methods like artificial neuronal networks, genetic algorithms, etc. 2. Usage of Data mining  to explore biochemical databases,
3. Usage of Machine learning on the biological examples to solve, for example, the problem of classification in Bioinformatics.

UFR offers in addition a doctoral training in Computer Science and Bioinformatics.

Doctoral module which includes: a Diplôme des Etudes Supérieures Approfondies (DESA)  of two years; and a doctorate studies program with a national Ph.D. certification. Three specializations constitute the teaching trunk of the ENSAT: Computer engineering, Telecom  engineering, and electronic systems engineering.

Research Interest and Activities:

The following are the present areas of research interest:

1. Machine Learning and Profile Gene Expression of Cancer
2. Predicting Protein structure
3. Hidden Markov Models (HMMs) and multiple alignments
4. Transformational Grammar for sequence modelling
5. Physical Mapping: STSs
6. Evolutionary Computation applied to Genomic and Proteomic
7. Predicate Logic and Protein Structure

Web site and links:

http://www.ensat.ac.ma/udiab http://www.pasteur.fr/pasteur/international/annonce_coursBioinfoannonce06_casa.pdf

https://github.com/remicnrd/ml_cheatsheet

Visual AI / ML

https://setosa.io/ev/

Simple and efficient tools for predictive data analysis

https://scikit-learn.org/stable/

In this tutorial we’ll be going through the steps of setting up an Apache server.

What you’ll learn

• How to set up Apache
• Some basic Apache configuration

Address of the bookmark: https://ubuntu.com/tutorials/install-and-configure-apache#3-creating-your-own-website

Address of the bookmark: https://seeing-theory.brown.edu/

https://diytranscriptomics.com

Address of the bookmark: https://diytranscriptomics.com