Zara  Bryant

Zara Bryant

1631673576

Introduction to Machine Learning

Foundations of Data Science for Machine Learning - Introduction to Machine Learning

A high-level overview of machine learning for people with little or no knowledge of computer science and statistics. You’ll be introduced to some essential concepts, explore data, and interactively go through the machine learning life-cycle - using Python to train, save, and use a machine learning model like we would in the real world. In this episode, you will:

In this episode, you will:
Explore how machine learning differs from traditional software.
Create and test a machine learning model.
Load a model and use it with new datasets.

#machinelearning #datascience

Introduction to Machine Learning
Dylan  Iqbal

Dylan Iqbal

1631432928

Learn Data Science - Full Course for Beginners

Learn Data Science is this full tutorial course for absolute beginners. Data science is considered the "sexiest job of the 21st century." You'll learn the important elements of data science. You'll be introduced to the principles, practices, and tools that make data science the powerful medium for critical insight in business and research. You'll have a solid foundation for future learning and applications in your work. With data science, you can do what you want to do, and do it better. This course covers the foundations of data science, data sourcing, coding, mathematics, and statistics.

⭐️ Course Contents ⭐️
⌨️ Part 1: Data Science: An Introduction: Foundations of Data Science
- Welcome (1.1)
- Demand for Data Science (2.1)
- The Data Science Venn Diagram (2.2)
- The Data Science Pathway (2.3)
- Roles in Data Science (2.4)
- Teams in Data Science (2.5)
- Big Data (3.1)
- Coding (3.2)
- Statistics (3.3)
- Business Intelligence (3.4)
- Do No Harm (4.1)
- Methods Overview (5.1)
- Sourcing Overview (5.2)
- Coding Overview (5.3)
- Math Overview (5.4)
- Statistics Overview (5.5)
- Machine Learning Overview (5.6)
- Interpretability (6.1)
- Actionable Insights (6.2)
- Presentation Graphics (6.3)
- Reproducible Research (6.4)
- Next Steps (7.1)
  
⌨️ Part 2: Data Sourcing: Foundations of Data Science (1:39:46)
- Welcome (1.1) 
- Metrics (2.1)
- Accuracy (2.2)
- Social Context of Measurement (2.3)
- Existing Data (3.1)
- APIs (3.2)
- Scraping (3.3)
- New Data (4.1)
- Interviews (4.2)
- Surveys (4.3)
- Card Sorting (4.4)
- Lab Experiments (4.5)
- A/B Testing (4.6)
- Next Steps (5.1)

⌨️ Part 3: Coding (2:32:42)
- Welcome (1.1)
- Spreadsheets (2.1)
- Tableau Public (2.2)
- SPSS (2.3)
- JASP (2.4)
- Other Software (2.5)
- HTML (3.1)
- XML (3.2)
- JSON (3.3)
- R (4.1)
- Python (4.2)
- SQL (4.3)
- C, C++, & Java (4.4)
- Bash (4.5)
- Regex (5.1)
- Next Steps (6.1)
       
⌨️ Part 4: Mathematics (4:01:09)
- Welcome (1.1)
- Elementary Algebra (2.1)
- Linear Algebra (2.2)
- Systems of Linear Equations (2.3)
- Calculus (2.4)
- Calculus & Optimization (2.5)
- Big O (3.1)
- Probability (3.2)
        
⌨️ Part 5: Statistics (4:44:03)
- Welcome (1.1)
- Exploration Overview (2.1)
- Exploratory Graphics (2.2)
- Exploratory Statistics (2.3)
- Descriptive Statistics (2.4)
- Inferential Statistics (3.1)
- Hypothesis Testing (3.2)
- Estimation (3.3)
- Estimators (4.1)
- Measures of Fit (4.2)
- Feature Selection (4.3)
- Problems in Modeling (4.4)
- Model Validation (4.5)
- DIY (4.6)
- Next Step (5.1)

#datascience #machinelearning #developer #python

 

Learn Data Science - Full Course for Beginners
Edward Jackson

Edward Jackson

1631283115

Introduction to R Programming

R is one of the best programming languages specifically designed for statistics and graphics. Programming in R is a fast and effective way to perform advanced data analyses and manipulations. In this course, you will learn how to use R and utilize the many data analysis techniques, methods, and functions it has to offer to the professional data scientist.

Video Timestamps:

00:00 Welcome
00:54 Downloading and installing R and RStudio
04:10 Quick guide to the RStudio user interface
11:40 Changing the appearance of RStudio
13:19 Installing packages and using the library
18:24 Creating an object in R
23:39 Data types in R (Integers and doubles)
28:19 Data types in R (Characters and logicals) 
31:35 Coercion rules in R
34:06 Functions in R
37:24 Functions and arguments
39:55 Building a function in R
48:00 Using the script vs. using the console

#datascience

Introduction to R Programming
Dylan  Iqbal

Dylan Iqbal

1631206440

Machine Learning Algorithms Full Course | Machine Learning Algorithms Explained

In this video you will find comprehensive explanation of many Machine Learning Algorithms. Along the way you will learn how ML Algorithms works under the hood.
You will learn about
- How decision tree works
- How Bayes Theorem works
- How Support Vector Machine works
- How Deep Neural Network works

#machinelearning #algorithms #datascience

 

Machine Learning Algorithms Full Course | Machine Learning Algorithms Explained
Gunjan  Khaitan

Gunjan Khaitan

1631195243

Become Data Science Expert - Full Course

This Data Science Expert Course Video will provide you with a learning path of Data Science in a correct manner. Filled with lots of Practical Examples this Data Science course of 2021 will all the essentials needed to become a Data Science expert.

What is Data Science?
Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.

Why Data Science?
Data science or data-driven science enables better decision making, predictive analysis, and pattern discovery. It lets you:
1. Find the leading cause of a problem by asking the right questions
2. Perform exploratory study on the data
3. Model the data using various algorithms 
4. Communicate and visualize the results via graphs, dashboards, etc.

Why learn Data Science? 
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques.

The Data Science with Python course is recommended for: 
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields

#datascience 

Become Data Science Expert - Full Course

Chih- Yu Lin

1631071585

How to Quickly Automate Data Processing in Python with Mito library

In this video, Jake (co-Founder of Mito) will show us how to quickly automate data processing in Python using the Mito library.

Mito Installation Instructions:
https://docs.trymito.io/getting-started/installing-mito 

#mito #datascience  #machinelearning  

 

How to Quickly Automate Data Processing in Python with Mito library

Spyder: Scientific Python IDE

Overview

Spyder is a powerful scientific environment written in Python, for Python, and designed by and for scientists, engineers and data analysts. It offers a unique combination of the advanced editing, analysis, debugging, and profiling functionality of a comprehensive development tool with the data exploration, interactive execution, deep inspection, and beautiful visualization capabilities of a scientific package.

Beyond its many built-in features, its abilities can be extended even further via its plugin system and API. Furthermore, Spyder can also be used as a PyQt5 extension library, allowing you to build upon its functionality and embed its components, such as the interactive console, in your own software.

For more general information about Spyder and to stay up to date on the latest Spyder news and information, please check out our new website.

Core components

Editor

Work efficiently in a multi-language editor with a function/class browser, real-time code analysis tools (pyflakes, pylint, and pycodestyle), automatic code completion (jedi and rope), horizontal/vertical splitting, and go-to-definition.

Interactive console

Harness the power of as many IPython consoles as you like with full workspace and debugging support, all within the flexibility of a full GUI interface. Instantly run your code by line, cell, or file, and render plots right inline with the output or in interactive windows.

Documentation viewer

Render documentation in real-time with Sphinx for any class or function, whether external or user-created, from either the Editor or a Console.

Variable explorer

Inspect any variables, functions or objects created during your session. Editing and interaction is supported with many common types, including numeric/strings/bools, Python lists/tuples/dictionaries, dates/timedeltas, Numpy arrays, Pandas index/series/dataframes, PIL/Pillow images, and more.

Development tools

Examine your code with the static analyzer, trace its execution with the interactive debugger, and unleash its performance with the profiler. Keep things organized with project support and a built-in file explorer, and use find in files to search across entire projects with full regex support.

Documentation

You can read the Spyder documentation online on the Spyder Docs website.

Installation

For a detailed guide to installing Spyder, please refer to our installation instructions.

The easiest way to install Spyder on any of our supported platforms is to download it as part of the Anaconda distribution, and use the conda package and environment manager to keep it and your other packages installed and up to date.

If in doubt, you should always install Spyder via this method to avoid unexpected issues we are unable to help you with; it generally has the least likelihood of potential pitfalls for non-experts, and we may be able to provide limited assistance if you do run into trouble.

Other installation options exist, including:

  • The WinPython distribution for Windows
  • The MacPorts project for macOS
  • Your distribution's package manager (i.e. apt-get, yum, etc) on Linux
  • The pip package manager, included with most Python installations

However, we lack the resources to provide individual support for users who install via these methods, and they may be out of date or contain bugs outside our control, so we recommend the Anaconda version instead if you run into issues.

Troubleshooting

Before posting a report, please carefully read our Troubleshooting Guide and search the issue tracker for your error message and problem description, as the great majority of bugs are either duplicates, or can be fixed on the user side with a few easy steps. Thanks!

Contributing and Credits

Spyder was originally created by Pierre Raybaut, and is currently maintained by Carlos Córdoba and an international community of volunteers.

You can join us—everyone is welcome to help with Spyder! Please read our contributing instructions to get started!

Certain source files are distributed under other compatible permissive licenses and/or originally by other authors. The icons for the Spyder 3 theme are derived from Font Awesome 4.7 (© 2016 David Gandy; SIL OFL 1.1). Most Spyder 2 theme icons are sourced from the Crystal Project icon set (© 2006-2007 Everaldo Coelho; LGPL 2.1+). Other Spyder 2 icons are from Yusuke Kamiyamane (© 2013 Yusuke Kamiyamane; CC-BY 3.0), the FamFamFam Silk icon set (© 2006 Mark James; CC-BY 2.5), and the KDE Oxygen icons (© 2007 KDE Artists; LGPL 3.0+).

See NOTICE.txt for full legal information.

Running from a git clone

Please see the instructions in our Contributing guide to learn how to do run Spyder after cloning its repo from Github.

Dependencies

Important Note: Most or all of the dependencies listed below come with Anaconda and other scientific Python distributions, so you don't need to install them separately in those cases.

Build dependencies

When installing Spyder from its source package, the only requirement is to have a Python version equal or greater than 3.6.

Runtime dependencies

The basic dependencies to run Spyder are:

  • Python 3.6+: The core language Spyder is written in and for.
  • PyQt5 5.6+: Python bindings for Qt, used for Spyder's GUI.

The rest our dependencies (both required and optional) are declared in this file.

Download Details:
Author: spyder-ide
The Demo/Documentation: View The Demo/Documentation
Download Link: Download The Source Code
Official Website: https://github.com/spyder-ide/spyder 
License: MIT
 

#python #spyder #datascience

Spyder: Scientific Python IDE
Edureka Fan

Edureka Fan

1631011860

What is Deep Learning?

This Edureka "What is Deep Learning" video will help you to understand the relationship between Deep Learning, Machine Learning and Artificial Intelligence. It will also explain what is Deep learning and how Deep Learning overcame Machine Learning limitations and different real-life applications of Deep Learning.

#deeplearning #machinelearning #artificialintelligence #datascience

What is Deep Learning?

SpacePy - Space Science Library for Python

SpacePy

SpacePy is a package for Python, targeted at the space sciences, that aims to make basic data analysis, modeling and visualization easier. It builds on the capabilities of the well-known NumPy and MatPlotLib packages. Publication quality output direct from analyses is emphasized among other goals:

  • Quickly obtain data
  • Read (and write) data from (and to) data formats like NASA CDF and HDF5
  • Create publications quality plots
  • Perform complicated analysis easily
  • Run common empirical models
  • Change coordinates and time systems effortlessly
  • Harness the power of Python

The SpacePy project seeks to promote accurate and open research standards by providing an open environment for code development. In the space physics community there has long been a significant reliance on proprietary languages that restrict free transfer of data and reproducibility of results. By providing a comprehensive, open-source library of widely-used analysis and visualization tools in a free, modern and intuitive language, we hope that this reliance will be diminished.

To help foster an open and welcoming environment, we have adopted a code of conduct that we encourage members of the SpacePy community to read and follow.

Getting SpacePy

Our latest release version is available through PyPI and can be installed using

pip install spacepy --user

This will also automatically install most dependencies. To permit binary installations without a compiler, this will not install ffnet on Windows. Users needing the LANLstar module can install ffnet separately (requires Fortran compiler); this can be done before or after the SpacePy install.

The latest "bleeding-edge" source code is available from our github repository at https://github.com/spacepy/spacepy and can be installed using the standard

python setup.py install --user

Further installation documentation can be found here Mac-specific information can be found here Full documentation is at https://spacepy.github.io

SpacePy supports both Python 2.7 and 3.x.

Dependencies

SpacePy has a number of well-maintained dependencies, most of which are automatically installed by pip. These include:

  • numpy (>=1.10, !=1.15.0)
  • scipy (>=0.11)
  • matplotlib (>=1.5)
  • h5py

Soft dependencies (that are required only for a very limited part of SpacePy's functionality) are:

  • ffnet
  • NASA CDF

For complete installation, excepting pre-built Windows binaries, SpacePy also requires C and Fortran compilers. We test with GCC compilers but try to maintain support for all major compilers.

NASA CDF

If you wish to use CDF files, download and install the NASA CDF library. The default installation directory is recommended to help SpacePy find the library. Get the package from https://cdf.gsfc.nasa.gov/html/sw_and_docs.html

Attribution

When publishing research which used SpacePy, please provide appropriate credit to the SpacePy team via citation or acknowledgement.

To cite SpacePy in publications, use (BibTeX code):

@INPROCEEDINGS{spacepy11,
author = {{Morley}, S.~K. and {Koller}, J. and {Welling}, D.~T. and {Larsen}, B.~A. and {Henderson}, M.~G. and {Niehof}, J.~T.},
title = "{Spacepy - A Python-based library of tools for the space sciences}",
booktitle = "{Proceedings of the 9th Python in science conference (SciPy 2010)}",
year = 2011,
address = {Austin, TX}
}

Certain modules may provide additional citations in the __citation__ attribute. Contact a module's author before publication or public presentation of analysis performed by that module. This allows the author to validate the analysis and receive appropriate credit for his or her work.

For acknowledging SpacePy, please provide the URL to our github repository. github.com/spacepy/spacepy

Download Details:
Author: spacepy
The Demo/Documentation: View The Demo/Documentation
Download Link: Download The Source Code
Official Website: https://github.com/spacepy/spacepy 
License
 

#python #spacepy #datascience 

SpacePy - Space Science Library for Python
Edward Jackson

Edward Jackson

1630944488

5 Real World Applications of Machine Learning

In this video, we are going to explain the five real world applications of machine learning.

#machinelearning #datascience 

 

5 Real World Applications of Machine Learning
Matt Clarke

Matt Clarke

1630943388

How to create a Python web scraper using Beautiful Soup

Beautiful Soup is one of the most powerful libraries for performing web scraping in Python. Here's a step-by-step guide to using it to scrape a website. #python #datascience #webscraping

https://practicaldatascience.co.uk/data-science/how-to-create-a-python-web-scraper-using-beautiful-soup

How to create a Python web scraper using Beautiful Soup
Matt Clarke

Matt Clarke

1630776661

How to run time-based SEO tests using Python

Learn how to run SEO tests in Python using EcommerceTools to fetch your Google Search Console data and evaluate performance using the Causal Impact model.

 #pandas #python #datascience #seo 

https://practicaldatascience.co.uk/data-science/how-to-run-time-based-seo-tests-using-python

How to run time-based SEO tests using Python
Dylan  Iqbal

Dylan Iqbal

1630736839

Data Science with R Programming - Go from Beginner to Expert

In this video, we go through an introduction to Data Science with R programming. This video takes you from beginner to expert in 2 hours with R programming.

When working in the data science field you will definitely become acquainted with the R language and the role it plays in data analysis. This course introduces you to the basics of the R language such as data types, techniques for manipulation, and how to implement fundamental programming tasks.

You will begin the process of understanding common data structures, programming fundamentals and how to manipulate data all with the help of the R programming language.

The emphasis in this course is hands-on and practical learning . You will write a simple program using RStudio, manipulate data in a data frame or matrix, and complete a final project as a data analyst using Watson Studio and Jupyter notebooks to acquire and analyze data-driven insights.

#datascience #r #programming #developer 

Data Science with R Programming - Go from Beginner to Expert

Chih- Yu Lin

1630570854

Building and Deploying a Simple Image Classification Web App in Python

In this video, we will be building and deploying a simple image classification web app in Python using the Gradio library. In essence, it only takes 3 lines of code to build a web app using Gradio: 1) Import the libraries, 2) Create a custom function of the model and 3) Creating the web app interface by specifying the 3 input arguments (function name, input and output).

👉 Thanks to Gradio for sponsoring this video https://gradio.app/ 
- Gradio Hosted https://gradio.app/introducing-hosted/ 

- Demo of the image classifier app https://gradio.app/g/dataprofessor/imageclassifierapp/ 
- Code of the image classifier app https://github.com/dataprofessor/imageclassifierapp/ 
- Cartoon illustration of model deployment https://twitter.com/thedataprof/status/1431268104978370561?s=20 

#gradio #datascience  #machinelearning  #dataprofessor #python 

Building and Deploying a Simple Image Classification Web App in Python
Gunjan  Khaitan

Gunjan Khaitan

1630544115

Learn Keras For Beginners - Full Course

This Keras full course will helo you understand what is Keras, the working principle of Keras, Keras models, what are neural networks along with hands-on demo. We will have look at a project where we detect whether a person is wearing a mask or not.

What Is Keras?
Keras is a high-level, deep learning API developed by Google for implementing neural networks. It is written in Python and is used to make the implementation of neural networks easy. It also supports multiple backend neural network computation. Keras is relatively easy to learn and work with because it provides a python frontend with a high level of abstraction while having the option of multiple back-ends for computation purposes. This makes Keras slower than other deep learning frameworks, but extremely beginner-friendly.

Why Do We Need Keras?
✅Keras is an API that was made to be easy to learn for people. Keras was made to be simple. It offers consistent & simple APIs, reduces the actions required to implement common code, and explains user error clearly.
✅Prototyping time in Keras is less. This means that your ideas can be implemented and deployed in a shorter time. Keras also provides a variety of deployment options depending on user needs.
✅Languages with a high level of abstraction and inbuilt features are slow and building custom features in then can be hard. But Keras runs on top of TensorFlow and is relatively fast. Keras is also deeply integrated with TensorFlow, so you can create customized workflows with ease.
✅The research community for Keras is vast and highly developed. The documentation and help available are far more extensive than other deep learning frameworks. 
✅Keras is used commercially by many companies like Netflix, Uber, Square, Yelp, etc which have deployed products in the public domain which are built using Keras.

#keras #deeplearning #datascience

Learn Keras For Beginners - Full Course