R Basics – R Programming Language Introduction
Learn the essentials of R Programming  R Beginner Level!
Created by RTutorials Training  Data Science Education
Students: 181445, Price: Free
Are you interested in data science?
Do you want to learn R totally from scratch?
Are you looking for an easy step by step approach to get into R?
Do you want to take an easy R course for BEGINNERS?
Well, if your answer is YES to some of these questions, look no further, this course will help you.
I created this course for the total beginner. That means for you: No prior knowledge required! If this is your first computer programming language to use  congratulations, you found your entry level material. If you are new to data science, no problem, you will learn anything you need to to start out with R.
That also means for you: if you are already used to R, you will likely benefit more from an advanced course. I have more than ten intermediate and advanced R courses available on Udemy, which might be more suited towards your needs. Check out the rtutorials instructor profile for more info.
Let’s take a look at the content and how the course is structured:
We will start with installation, the R and RStudio interface, add on packages, how to use the R exercise database and the R help tools.
Then we will learn various ways to import data, first coding steps including basic R functions, functions and loops and we will also take a look at the graphical tools.
The whole course should take approx. 3 to 5 hours, and there are exercises available for you to try out R. You will also get the code I am using for the demos.
Anything is ready for you to enter the world of statistical programming.
What R you waiting for?
Martin
Introduction to Data Science using Python (Module 1/3)
Learn Data science / Machine Learning using Python (Scikit Learn)
Created by Rakesh Gopalakrishnan  Over 260,000 Students
Students: 120642, Price: Free
Are you completely new to Data science?
Have you been hearing these buzz words like Machine learning, Data Science, Data Scientist, Text analytics, Statistics and don't know what this is?
Do you want to start or switch career to Data Science and analytics?
If yes, then I have a new course for you. In this course, I cover the absolute basics of Data Science and Machine learning. This course will not cover indepth algorithms. I have split this course into 3 Modules. This module, takes a 500,000ft. view of what Data science is and how is it used. We will go through commonly used terms and write some code in Python. I spend some time walking you through different career areas in the Business Intelligence Stack, where does Data Science fit in, What is Data Science and what are the tools you will need to get started. I will be using Python and ScikitLearn Package in this course. I am not assuming any prior knowledge in this area. I have given some reading materials, which will help you solidify the concepts that are discussed in this lectures.
This course will the first data science course in a series of courses. Consider this course as a 101 level course, where I don't go too much deep into any particular statistical area, but rather just cover enough to raise your curiosity in the field of Data Science and Analytics.
The other modules will cover more complex concepts.
The Top 5 Machine Learning Libraries in Python
A Gentle Introduction to the Top Python Libraries used in Applied Machine Learning
Created by Mike West  Creator of LogikBot
Students: 86713, Price: Free
Recent Review from Similar Course:
"This was one of the most useful classes I have taken in a long time. Very specific, realworld examples. It covered several instances of 'what is happening', 'what it means' and 'how you fix it'. I was impressed." Steve
Welcome to The Top 5 Machine Learning Libraries in Python. This is an introductory course on the process of building supervised machine learning models and then using libraries in a computer programming language called Python.
What’s the top career in the world? Doctor? Lawyer? Teacher? Nope. None of those.
The top career in the world is the data scientist. Great. What’s a data scientist?
The area of study which involves extracting knowledge from data is called Data Science and people practicing in this field are called as Data Scientists.
Business generate a huge amount of data. The data has tremendous value but there so much of it where do you begin to look for value that is actionable? That’s where the data scientist comes in. The job of the data scientist is to create predictive models that can find hidden patterns in data that will give the business a competitive advantage in their space.
Don’t I need a PhD? Nope. Some data scientists do have PhDs but it’s not a requirement. A similar career to that of the data scientist is the machine learning engineer.
A machine learning engineer is a person who builds predictive models, scores them and then puts them into production so that others in the company can consume or use their model. They are usually skilled programmers that have a solid background in data mining or other data related professions and they have learned predictive modeling.
In the course we are going to take a look at what machine learning engineers do. We are going to learn about the process of building supervised predictive models and build several using the most widely used programming language for machine learning. Python. There are literally hundreds of libraries we can import into Python that are machine learning related.
A library is simply a group of code that lives outside the core language. We “import it” into our work space when we need to use its functionality. We can mix and match these libraries like Lego blocks.
Thanks for your interest in the The Top 5 Machine Learning Libraries in Python and we will see you in the course.
Welcome to Artificial Intelligence !
NON TECHNICAL COURSE specifically created for AI/ML/DL Aspirants, gives insight about Road map to A.I
Created by Vinoth Rathinam  Founder of NXTGEN A.I  Corporate Trainer  Data Scientist
Students: 41682, Price: Free
NON TECHNICAL COURSE specifically created for AI/ML/DL Aspirants, gives insight about Road map to A.I
This course will clear all doubts such as,
1. What are prerequisites for learning AI?
2. What is Road map to start Machine learning project(ML)
3. How to choose the best programming language for AI ?
4. How much Mathematical knowledge needed for AI ?
5. Which is the best AI Engine/Tool/Framework for AI ? and so on...
Each video is created with real time scenario examples in simple language. So that anyone without programming knowledge can understand in depth about Artificial Intelligence and Machine Learning.
The contents were prepared based on maximum queries searched in google or posted in AI forum.
At the end of this course you will get clear clarity on how much effort needed to start your career in Artificial Intelligence or Machine Learning Projects.
Note:
1. Students/Experienced professionals, who expects sample coding can skip this course :) But soon case study with coding course will be launched :)
2. For NonEnglish speaking students, I enabled the Auto Caption now. But still the text won’t 100% correct. So I will be updating the captions manually as soon as possible.
3. All AI prerequisites topics like programming language , Mathematics , Machine Learning Algorithms will be posted soon as free course. Keep following. Happy Learning !!
R, ggplot, and Simple Linear Regression
Begin to use R and ggplot while learning the basics of linear regression
Created by Charles Redmond  Professor at Mercyhurst University
Students: 38675, Price: Free
Data science skills are in much demand today, but it is not just the mathematicians, statisticians, and the computer scientists who can benefit from acquiring them. Data science skills are for everyone!
In this course, I help you to begin using R, one of the most important tools in data science, and the excellent graphics package for R, ggplot2. Along the way, I also show you the basics of simple linear regression.
There are no prerequisites. We begin with installation of R and RStudio, and I introduce R and ggplot skills as they are needed as we progress toward an understanding of linear regression.
Students should be able to complete the course within two weeks, working at an easy pace.
Linear regression is a machine learning technique. I hope to create more courses like this one in the future, teaching machine learning, R, ggplot, dplyr, and programming, all at the same time.
Applied Deep Learning: Build a Chatbot – Theory, Application
Understand the Theory of how Chatbots work and implement them in Python and PyTorch!
Created by Fawaz Sammani  Computer Vision Researcher
Students: 36278, Price: Free
In this course, you'll learn the following:

RNNs and LSTMs

Sequence Modeling

PyTorch

Building a Chatbot in PyTorch
We will first cover the theoretical concepts you need to know for building a Chatbot, which include RNNs, LSTMS and Sequence Models with Attention.
Then we will introduce you to PyTorch, a very powerful and advanced deep learning Library. We will show you how to install it and how to work with it and with PyTorch Tensors.
Then we will build our Chatbot in PyTorch!
Please Note an important thing: If you don't have prior knowledge on Neural Networks and how they work, you won't be able to cope well with this course. Please note that this is not a Deep Learning course, it's an Application of Deep Learning, as the course names implies (Applied Deep Learning: Build a Chatbot). The course level is Intermediate, and not Beginner. So please familiarize yourself with Neural Networks and it's concepts before taking this course. If you are already familiar, then your ready to start this journey!
Introduction to R
Learn the core fundamentals of the R language for interactive use as well as programming
Created by Jagannath Rajagopal  Entrepreneur and Data Scientist
Students: 34525, Price: Free
UPDATE: As of Nov 22, 2018, this course is now free! Many thanks to all my existing students who made it possible for the wider audience to benefit from the course material :)
With "Introduction to R", you will gain a solid grounding of the fundamentals of the R language!
This course has about 90 videos and 140+ exercise questions, over 10 chapters. To begin with, you will learn to Download and Install R (and R studio) on your computer. Then I show you some basic things in your first R session.
From there, you will review topics in increasing order of difficulty, starting with Data/Object Types and Operations, Importing into R, and Loops and Conditions.
Next, you will be introduced to the use of R in Analytics, where you will learn a little about each object type in R and use that in Data Mining/Analytical Operations.
After that, you will learn the use of R in Statistics, where you will see about using R to evaluate Descriptive Statistics, Probability Distributions, Hypothesis Testing, Linear Modeling, Generalized Linear Models, NonLinear Regression, and Trees.
Following that, the next topic will be Graphics, where you will learn to create 2dimensional Univariate and Multivariate plots. You will also learn about formatting various parts of a plot, covering a range of topics like Plot Layout, Region, Points, Lines, Axes, Text, Color and so on.
At that point, the course finishes off with two topics: Exporting out of R, and Creating Functions.
Each chapter is designed to teach you several concepts, and these have been grouped into subsections. A subsection usually has the following:

A Concept Video

An Exercise Sheet

An Exercise Video (with answers)
Why take a course to learn R?
When I look to advancing my R knowledge today, I still face the same sort of situation as when I originally started to use R. Back when I was learning R, my approach was learn by doing. There was a lot of free material out there (and I refer to that early in the course) that gave me a framework, but the wording was highly technical in nature. Even with the R help and the free material, it took me up to a couple of months of experimentation to gain a certain level of proficiency. What I would have liked at that time was a way to learn the fundamentals quicker. I have designed this course with exactly that in mind.
Why my course?
For those of you that are new to R, this course will cover enough breadth/depth in R to give you a solid grounding. I use simple language to explain the concepts. Also, I give you 140+ exercise questions many of which are based on real world data for practice to get you up and running quickly, all in a single package. This course is designed to get you functional with R in little over a week.
For those beginners with some experience that have learnt R through experimentation, this course is designed to complement what you know, and round out your understanding of the same.
Artificial Neural Network for Regression
Build an ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant
Created by Hadelin de Ponteves  AI Entrepreneur
Students: 26897, Price: Free
Are you ready to flex your Deep Learning skills by learning how to build and implement an Artificial Neural Network using Python from scratch?
Testing your skills with practical courses is one of the best and most enjoyable ways to learn data science…and now we’re giving you that chance for FREE.
In this free course, AI expert Hadelin de Ponteves guides you through a case study that shows you how to build an ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant.
The objective is to create a data model that predicts the net hourly electrical energy output (EP) of the plant using available hourly average ambient variables.
Go handson with Hadelin in solving this complex, realworld Deep Learning challenge that covers everything from data preprocessing to building and training an ANN, while utilizing the Machine Learning library, Tensorflow 2.0, and Google Colab, the free, browserbased notebook environment that runs completely in the cloud. It’s a gamechanging interface that will supercharge your Machine Learning toolkit.
Check out what’s in store for you when you enroll:
Part 1: Data Preprocessing

Importing the dataset

Splitting the dataset into the training set and test set
Part 2: Building an ANN

Initializing the ANN

Adding the input layer and the first hidden layer

Adding the output layer

Compiling the ANN
Part 3: Training the ANN

Training the ANN model on the training set

Predicting the results of the test set
More about CombinedCycle Power Plants
A combinedcycle power plant is an electrical power plant in which a Gas Turbine (GT) and a Steam Turbine (ST) are used in combination to produce more electrical energy from the same fuel than that would be possible from a single cycle power plant.
The gas turbine compresses air and mixes it with a fuel heated to a very high temperature. The hot airfuel mixture moves through the blades, making them spin. The fastspinning gas turbine drives a generator to generate electricity. The exhaust (waste) heat escaped through the exhaust stack of the gas turbine is utilized by a Heat Recovery Steam Generator (HSRG) system to produce steam that spins a steam turbine. This steam turbine drives a generator to produce additional electricity. CCCP is assumed to produce 50% more energy than a single power plant.
Deep Learning Prerequisites: The Numpy Stack in Python V2
Numpy, Scipy, Pandas, and Matplotlib: prep for deep learning, machine learning, and artificial intelligence
Created by Lazy Programmer Team  Artificial Intelligence and Machine Learning Engineer
Students: 25862, Price: Free
Welcome! This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python (V2).
The reason I made this course is because there is a huge gap for many students between machine learning "theory" and writing actual code.
As I've always said: "If you can't implement it, then you don't understand it".
Without basic knowledge of data manipulation, vectors, and matrices, students are not able to put their great ideas into working form, on a computer.
This course closes that gap by teaching you all the basic operations you need for implementing machine learning and deep learning algorithms.
The goal is that, after you take this course, you will learn about machine learning algorithms, and implement those algorithms in code using the tools and techniques you learned in this course.
Suggested Prerequisites:

linear algebra

probability

Python programming
Polynomial Regression, R, and ggplot
Learn how to write and graph functions in R and how to fit polynomials to data sets.
Created by Charles Redmond  Professor at Mercyhurst University
Students: 25340, Price: Free
This course is a sequel to my course "R, ggplot, and Simple Linear Regression". Here we take on polynomial regression and learn how to fit polynomials to data sets. Along the way, we will learn how to write our own functions in R and how to graph them with ggplot. At the conclusion of the course, we will learn how to fit a smoothing spline to data sets.
At a relaxed pace, it should take about a week to complete the course. You will need to have R and RStudio installed, and it would be best if you have a background in R and ggplot equivalent to what you would get if you viewed my first course mentioned above.
Essentials of Data Science
Discover what Data Science is all about
Created by Maximilian Schallwig  Data Scientist
Students: 25288, Price: Free
Data Science is growing ever faster as Big Data become an increasingly important part of our lives.
Because data is universal, the applications of Data Science are pretty much endless, all you need is access to the data of the system that you want to study.
Since it's such a new field, there are a lot of questions about what is Data Science, what do Data Scientists do, and what do you need to succeed as a Data Scientist?
This course is designed to give you an overview of the three essential areas of Data Science, the areas that every good data scientist should know, and being proficient in these areas can be the key to your success. After this course you will have a clear understanding of what Data Science is all about, and can make a clear decision of if it's the right field for you. You will also know what areas are important in Data Science, and hence can make informed decisions on what areas to focus on learning.
When you really get into Data Science there are other areas that start coming in too, but all of these can be traced back to one, or multiple, of these three basic foundations.
50 Must Know Concepts,Algorithms in Machine Learning
Introduction to 50 Must know Topics of Machine Learning,Data science. Understand machine Learning Syllabus.
Created by TheMachineLearning.Org .  Machine Learning Engineer
Students: 25274, Price: Free
This course is designed to give you introduction to syllabus of machine learning. If you want to get started with machine learning then this course will help you. It helps you to get ready for an interview with 50 concepts covering varied range of topics. The course is intended not only for candidates with a full understanding of Machine Learning but also for recalling knowledge in data science.
Artificial Intelligence Markup Language (AIML)
Create your own chatbots using the world's most popular chatbot language.
Created by Steve Worswick  Senior AI Developer at Pandorabots.com
Students: 22709, Price: Free
This course is designed for people with absolutely no knowledge of Artificial Intelligence Markup Language (AIML). It guides you step by step and teaches you how to create a chatbot using the world's most popular chatbot language. From the very beginning to more advanced features, take it at your own pace, practice and learn from Steve Worswick, the 5 times holder of the Loebner Prize.
Python AI and Machine Learning for Production & Development
Learn AI & ML using demos
Created by Techlatest .Net  Training videos on latest technologies and trends
Students: 22136, Price: Free
When you want to learn a new technology for professional use, there are two mutually exclusive options, either you learn it yourself or you go for instructor based training.
Self learning is least expensive but lot of time results in wasting time in finding right contents, setting up the environment , troubleshooting issues and may make you give up in the middle.
Instructor based training can be expensive at times and need your time commitment.
This course combines the best of both these options. The course is based on one of the most famous books in the field "Python Machine Learning (2nd Ed.)" by Sebastian Raschka and Vahid Mirjalili and provides you video tutorials on how to understand the AI/ML concepts from the books by providing out of box virtual machine with demo examples for each chapter in the book and complete preinstalled setup to execute the code.
You learn the concepts by self learning and get hands on executing the sample code in the virtual machine.
The demo covers following concepts:

Machine Learning  Giving Computers the Ability to Learn from Data

Training Machine Learning Algorithms for Classification

A Tour of Machine Learning Classifiers Using ScikitLearn

Building Good Training Sets – Data PreProcessing

Compressing Data via Dimensionality Reduction

Learning Best Practices for Model Evaluation & Hyperparameter Optimization

Combining Different Models for Ensemble Learning

Applying Machine Learning to Sentiment Analysis

Embedding a Machine Learning Model into a Web Application

Predicting Continuous Target Variables with Regression Analysis

Working with Unlabeled Data – Clustering Analysis

Implementing a Multilayer Artificial Neural Network from Scratch

Parallelizing Neural Network Training with TensorFlow

Going Deeper: The Mechanics of TensorFlow

Classifying Images with Deep Convolutional Neural Networks

Modeling Sequential Data Using Recurrent Neural Networks
In addition to the preinstalled setup and demos, the VM also comes with:

Jupyter notebook for web based interactive development

JupyterHub for multiuser notebook environment to allow multiple users to simultaneously do development

Remote desktop

Visual studio code IDE

Fish Shell
The VM is available on :

Google Cloud Platform

AWS

Microsoft Azure
Data Science 101 Data Analytics Class Python Bootcamp NYC
Data Science 101 Data Analytics Class Python Pandas Bootcamp (Non Programmers & Beginners at Wall Street NYC, New York)
Created by Shivgan Joshi  Free Python Class Bootcamp Big Data Science NYC 312 285 6886
Students: 21478, Price: Free
Data Science 101: Python Pandas Bootcamp Data Analytics Course
This course is based on my classes taken in NYC for introducing basics of Data Analytics in Python using Pandas.
The course is not intended to make your expert in Python Analytics but rather introduce you to simple code and give you basic intro of all topics in Data Analytics in Python to launch you into a career in Data Science.
Topics:

Learn Python for Analytics: Pandas

Pandas Objects are Series, DataFrames and comparison with Excel VBA

Creating DataFrames from scratch using dictionary or list

Data Cleaning & Preparation for Analysis  Missing Values, Data imputation

Aggregation, Wrangling Rearranging and reshaping data : Join, Combine, Pivot, Melt and Reshape

Data Manipulating DataFrames with Pandas

Time Series Data  String to Datetime

Visualizations with Matplotlib
This course is build based on my classes taken in NYC, New York
Tensorflow 2.0  Recurrent Neural Networks, LSTMs, GRUs
Sequence prediction course that covers topics such as: RNN, LSTM, GRU, NLP, Seq2Seq, Attention, Time series prediction
Created by Jad Slim  Developer
Students: 20158, Price: Free
This is a preview to the exciting Recurrent Neural Networks course that will be going live soon. Recurrent Networks are an exciting type of neural network that deal with data that come in the form of a sequence. Sequences are all around us such as sentences, music, videos, and stock market graphs. And dealing with them requires some type of memory element to remember the history of the sequences, this is where Recurrent Neural networks come in.
We will be covering topics such as RNNs, LSTMs, GRUs, NLP, Seq2Seq, attention networks and much much more.
You will also be building projects, such as a Time series Prediction, music generator, language translation, image captioning, spam detection, action recognition and much more.
Building these projects will impress even the most senior machine learning developers; and will prepare you to start tackling your own deep learning projects with real datasets to show off to your colleagues or even potential employers.
Sequential Networks are very exciting to work with and allow for the creation of very intelligent applications. If you’re interested in taking your machine learning skills to the next level, then this course is for you!
Introduction to Artificial Intelligence in Software Testing
Learn the Basic Fundamentals of Artificial Intelligence (AI) in Software Testing in less than 30 minutes!
Created by Sujal Patel  Passionate Test Automation Expert, Consultant and Trainer
Students: 19995, Price: Free
Introduction to Artificial Intelligence in Software Testing course talks about basic fundamentals of Artificial Intelligence (AI) and the future of Automated Testing with AI Machine Learning.
This course is designed for both testers and developers. This course is also great for anyone who want to learn Artificial Intelligence in Software Testing. Once again this is very basic course but if you want to learn more in detail then please see my other course called "Artificial Intelligence (AI) in Software Testing (The Future of Automated Testing with Machine Learning  Implementing Artificial Intelligence (AI) in Test Automation)".
This course will teach you how AIassisted test automation can transform the UI. This course will also teach you Artificial Intelligence (AI) and it's relationship with Machine Learning and Deep Learning.
After you have completed this course you should be able to teach your friends or coworkers the importance of Artificial Intelligence in Software Testing. You should also host the lunch and learn session for your friends or coworkers.
Learn R for Business Analytics from Basics
Know basics of Business Analytics ?! Workout those skills further on Rplatform. Learn R for BA over a weekend !
Created by Analytics 17  Data Science Experts
Students: 19825, Price: Free
Newly Launched Course!
R is the new and fastest growing Business Analytics platform. R shall become (if it hasn't already become) one of the most used Business Analytics tool. It is giving strong competition to giants like SAS, SPSS and other erstwhile business analytics packages.
This course is designed specifically for someone who knows basics of Business Analytics and wants to learn implementation of those skills on R platform.
The course is designed considering the busy schedule of learners. It has power pack content for about 90 mins. If you practice along with learning (which is highly recommended) then you shall take about 12 days to complete the course.
You will learn how to perform all the analytical tasks required to develop a "Losses prediction model in R" from scratch. This means that you will be working on:
 Downloading and Installing R in your machine
 Getting familiar with R environment
 Loading important packages in R
 Start writing your first code in R
 Import Data in R and perform exploration and transformation activities
 Do plots in R to understand data distribution
 Write your own macro functions
 Run correlation and regression in R and analyse model results
By the end of the course you shall be confident and equipped with all the knowledge required to perform analytical activities in R.
If you want to learn Business Analytics or SAS language, then our other course "Business Analytics for Beginners: Using SAS" shall be the best fit for you.
What is Data Science ?
Fundamental Concepts for Beginners
Created by Gopinath Ramakrishnan  Data Science & Machine Learning Enthusiast, Agile Coach
Students: 19599, Price: Free
If you have absolutely no idea what Data Science is and are looking for a very quick nontechnical introduction to Data Science , this course will help you get started on fundamental concepts underlying Data Science.
If you are an experienced Data Science professional, attending this course will give you some idea of how to explain your profession to an absolute lay person.
There are lots of very good technical and programming focused courses available on Data Science in Udemy and elsewhere.
This short course will lay a firm foundation for better understanding and appreciation of what is being taught in advanced Data Science courses.
Logistic Regression Practical Case Study
Breast Cancer detection using Logistic Regression
Created by Hadelin de Ponteves  AI Entrepreneur
Students: 19553, Price: Free
Did you know that approximately 70% of data science problems involve classification and logistic regression is a common solution for binary problems?
Logistic regression has many applications in data science, but in the world of healthcare, it can really drive lifechanging action.
In this SuperDataScience case study course, learn how to detect breast cancer by applying a logistic regression model on a realworld dataset and predict whether a tumor is benign (not breast cancer) or malignant (breast cancer) based off its characteristics.
By the end of the course, you will be able to build a logistic regression model to identify correlations between the following 9 independent variables and the class of the tumor (benign or malignant).

Clump thickness

Uniformity of cell size

Uniformity of cell shape

Marginal adhesion

Single epithelial cell

Bare Nuclei

Bland chromatin

Normal nucleoli

Mitoses
Logistic regression can identify important predictors of breast cancer using odds ratios and generate confidence intervals that provide additional information for decisionmaking. Model performance depends on the ability of the radiologists to accurately identify findings on mammograms.
Join AI expert Hadelin de Ponteves as you code the solution along with him in this 1hour, 3part case study:
Part 1: Data Preprocessing

Importing the dataset

Splitting the dataset into a training set and test set
Part 2: Training and Inference

Training the logistic regression model on the training set

Predicting the test set results
Part 3: Evaluating the Model

Making the confusion matrix

Computing the accuracy with kFold crossvalidation
Testing your skills with practical courses is one of the best and most enjoyable ways to learn data science…and now we’re giving you that chance for FREE.
Plus, you’ll do it all using Google’s Colab free, browserbased notebook environment that runs completely in the cloud. It’s a gamechanging interface that will save you time and supercharge your data science toolkit.
Click the ‘Enroll Now’ button to join Hadelin’s class today!
More about logistic regression:
Logistic regression is a method of statistical analysis used to predict a data value based on prior observations of a dataset. A logistic regression model predicts the value of a dependent variable by analyzing the relationship between one or more existing independent variables.
In data science, logistic regression is a Machine Learning algorithm used for classification problems and predictive analysis.
More realworld applications of logistical regression include:

Bankruptcy predictions

Credit scoring

Consumer behavior

Customer retention

Spam detection
Bootcamp for KNIME Analytics Platform
For users new to KNIME and data science, or experienced users of other data science tools.
Created by KNIME Inc  Data Science and Evangelism
Students: 19142, Price: Free
If you've never used KNIME Analytics Platform before, this is the course for you. You can use KNIME Analytics Platform to create visual workflows with an intuitive, drag and drop style graphical interface, without the need for coding.
We'll start with installation and setup of the software, and present detailed materials on its features. We'll move on to some practical application of data blending from different sources, and use real datasets to show you all the different way you can transform, clean, and aggregate information. Finally, we'll introduce some machine learning algorithms for classification, and show you how to build your own models.
More than 50 videos are provided, along with some exercises for you to work on independently. By the end of the course, we want you to feel comfortable with the interface of KNIME Analytics Platform, be able to perform common processing tasks with your own data, and start putting predictive analytics into practice.
Training Sets, Test Sets, R, and ggplot
How to evaluate regression model performance in R
Created by Charles Redmond  Professor at Mercyhurst University
Students: 18258, Price: Free
In this course, I show you how to evaluate the performance of a regression model using training sets and test sets. We will use R and ggplot as our tools. Along the way, we will learn how to rowslice data frames, use the predict function in R, and add titles and labels to our plots. We will also work on our programming skills by learning how to write for loops and functions of two variables.
Students should have the background in R, ggplot, and regression equivalent to what one would have after viewing my two Udemy courses on linear and polynomial regression. At a relaxed pace, it should take about two weeks to complete the course.
Natural Language Processing (NLP) with BERT
Movies reviews Semantic analysis using BERT
Created by Hadelin de Ponteves  AI Entrepreneur
Students: 18173, Price: Free
Are you ready to dive right into one of the most exciting developments in data science right now: Google’s breakthrough NLP algorithm, BERT!
Testing your skills with practical courses is one of the best and most enjoyable ways to learn data science…and now we’re giving you that chance for FREE.
Our new case study course: Natural Language Processing (NLP) with BERT shows you how to perform semantic analysis on movie reviews using data from one of the most visited websites in the world: IMDB!
Perform semantic analysis on a large dataset of movie reviews using the lowcode Python library, Ktrain.
But, why is BERT so revolutionary?
Not only is it a framework that has been pretrained with the biggest data set ever used, it is also remarkably easy to adapt to different NLP applications, by adding additional output layers. This allows users to create sophisticated and precise models to carry out a wide variety of NLP tasks.
AI expert Hadelin de Ponteves guides you through some basic components of Natural Language Processing, how to implement the BERT model and sentiment analysis, and finally, Python coding in Google Colab.
Here’s how this 1hour case study course will unfold:
Part 1: Data Preprocessing

Loading the IMDB dataset

Creating the training and test sets
Part 2: Building the BERT model
Part 3: Training and evaluating the BERT model

Getting the learner instance

Training and evaluating the BERT model
Plus, you’ll do it all using Google’s Colab free, browserbased notebook environment that runs completely in the cloud. It’s a gamechanging interface that will save you time and supercharge your data science toolkit.
If you’ve been waiting for a chance to put your NLP skills to the test then this is the opportunity you've been waiting for. Click the ‘Enroll Now’ button and see you inside!
Meeshkan: Machine Learning the GitHub API
Learn how to plan, deploy and run a Machine Learning problem on AWS and Meeshkan
Created by Mike Solomon  C.E.O. Meeshkan Machine Learning
Students: 17470, Price: Free
In this course, Meeshkan C.E.O. Mike Solomon will teach you how to do Machine Learning on Meeshkan.
Meeshkan is an easy and inexpensive platform where people can explore ideas in AI, Machine Learning and Deep Learning.
This course starts with a simple AI question: can a machine predict if a GitHub project will be successful by analyzing only the first few commits of that project?
The first section of the course will run the Machine Learning project on Meeshkan. You'll see how quick and easy it is to do Machine Learning on Meeshkan.
The second section of the course will delve into each step of the process in detail, covering data collection, data egress, infrastructure deployment, model design, model executing and result analysis.
By the end of the course, you will be able to adapt the course materials to design, run, and explore your own Machine Learning models using public APIs and the Meeshkan Machine Learning service.
Build a Web Application with Python, Flask and NLP
Share the joy of famous quotes with a cloudbased web app using natural language processing to hit the right mood!
Created by Manuel Amunategui  Data Scientist & Quantitative Developer
Students: 16705, Price: Free
Let's share the wonderful joy of famous quotes to the world with a quoting machine web application that uses natural language sentiment to tailor the right quote for the user.
The class will teach you how to take your Python ideas and extend them to the web into real Web Applications so the world can enjoy your work.
In this class, we will:

develop our ideas in a local Jupyter notebook

gather data (famous quotes)

use the Vader NLP sentiment algorithm

tune our models and dispensing mechanisms locally

design the look and feel

get graphics

extend responsive HTML templates

port to the web using PythonAnywhere

enjoy great quotes in tune with our moods 24/7
Above all, you will understand how you can port your own Python ideas to the web into fully interactive web applications so the world can enjoy your work!
Introduction to AI for Business
Amplifying Human Ingenuity with Intelligent Technology
Created by Zigurat Innovation & Technology Business School  We are specialists in advanced education
Students: 15972, Price: Free
Peter Maynard, Director Program Management at Microsoft, explores what AI really is, why and how it will transform every business in every industry. Peter also uncovers how Microsoft technology is at the forefront of this transformation and show some scenarios, both present and future with respect to how this is helping business embrace digital transformation.
The purpose of the course is to highlight how underlying Digital Transformation in a number of enterprises is simply an algorithm. This algorithm will determine the success of how that company will leverage its data in the future and if it will ultimately survive. Moving on from that as background, there will be then explored the types of steps that a company can take to win in the algorithm wars and things that they should be conscious of. In the course, there will be presented a range of examples of companies that are winning in Digital Transformation through AI.
Introduction to Big Data – an overview of the 10 V’s
An overview of the Dimensions and Forms of Big Data.
Created by Taimur Zahid  Machine Learning Engineer
Students: 14970, Price: Free
This course is designed to be an indepth overview of the field of Data Science. It teaches the students various Characteristics of Big Data as well as discuss a few types of Data that exists. After completing this course, you will have the knowledge that can be applied later on in your journey into this field when you're selecting an Algorithm, a Tool, a Framework, or even while making a Blueprint of how to deal with the current problem at hand.
Introduction to Natural Language Processing
Learn basics of Natural Language Processing (NLP), Regular Expressions and Text Preprocessing using Python
Created by Analytics Vidhya  Data Science Community
Students: 14780, Price: Free
More than 80% of the data in this world is unstructured in nature, which includes text. You need text mining and Natural Language processing (NLP) to make sense out of this data. Natural Language Processing (NLP) helps you extract insights from emails of customers, their tweets, text messages. Natural Language Processing (NLP) can power many applications, such as language translation, question answering systems, chatbots and document summarisers.
What would you learn in Introduction to Natural Language Processing (NLP) with Python course?

Reading and working with text data using Python

Learn to use Regular Expressions to extract patterns from text

Text preprocessing

Text classification
Natural Language Processing (NLP) for Beginners Using NLTK
Your journey to NLP mastery starts here
Created by Harshal Samant  Engineer and a Machine Learning Enthusiast
Students: 13312, Price: Free
In this video series, we will start with in introduction to corpus we have at our disposal through NLTK. Once we download the corpus and learn different tricks to access it, we will move on to very useful feature in NLP called frequency distribution. In this section, we will see how calculate, tabulate and plot frequency distribution of words. In the next section, we will start learning NLP specific techniques that include:
1. Stemming
2. Lemmatization
3. Tokenization
Data Visualization in Python Masterclass™ for Data Scientist
Matplotlib for Data Visualization and analysis with Python 2021 Edition
Created by Abbosjon Madiev  Computer Scientist
Students: 12318, Price: Free
The only way to truly learn how to use Matplotlib for Data Visualization with Python is by actually getting your hands dirty and trying out the features yourself. That’s where this course comes in!
The hourlong course starts off with an introduction to Matplotlib, including how to install and import it in Python. We will then move on to learn how you can create and customize basic 2D charts in order to best tell your story. Furthermore, you will also learn what subplots are and how you can create as well as customize them with the help of the Matplotlib library.
We will explore the full spectrum of interactive and explorable graphic representations including various plots such as Scatter, Line, Bar, Stacked Bar, Histogram, Pie, and much more. The course also walks you through the basics of creating a 3D plot in Matplotlib and how you can start plotting images using the Python visualization library.
And, once you are done with this course, you will be able to create almost any kind of plot that you need with Matplotlib and Python.
Why you should take this course?

Updated 2021 course content: All our course content is updated as per the latest version of the Matplotlib library.

Practical handson knowledge: This course is oriented to providing a stepbystep implementation guide for making amazing data visualization plots rather than just sticking to the theory.

Guided support: We are always there to guide you through the Q/As so feel free to ask us your queries