This is my experience with the challenge 100 days of ML code

In this age of modern technology, there is one resource that we have in abundance: a large amount of structured and unstructured data. In the second half of the 20th century, machine learning evolved as a subfield of artificial intelligence (AI) involving self-learning algorithms that derive knowledge from data to make predictions.

100 Days of ML Code is a commitment to better your understanding of this powerful tool by dedicating at least 1 hour of your time every day to studying and/or coding machine learning for 100 days.

See the original repository at Siraj Raval repository.

• Make a public pledge to code or study machine learning for minimum 1 hour every day for the next 100 days via your favorite social platform using the #100DaysofMLCode Hashtag.

• Make a public log of your work. Update it daily. Here is a GitHub example template. Another one is here. You can also *make a blog or vlog.

• If you see someone make a post using the #100DaysofMLCode hashtag, encourage them via a 'like', 'share', or comment.

I schedule my learning based on the structure of some online courses found on the internet and consider to be best evaluated by people.

These courses are extensive enough to accompany me during this challenge and with enough information to not have downtime, in addition to always continuing in the right direction in my learning

All resources used (fully or partially) for the challenge:

• Packtpub Workshops - One year of access to any interactive workshop at no charge for registered Packt users.

• Python for Data Science and Machine Learning Bootcamp - This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms.

• Python A-Z™: Python For Data Science With Real Exercises - Programming In Python For Data Analytics And Data Science. Learn Statistical Analysis, Data Mining And Visualization

• The Data Science Course 2020: Complete Data Science Bootcamp - Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning.

• JetBrains Academy - Learn to Program by Creating Working Applications.

• Understanding Machine Learning with Python - Use your data to predict future events with the help of machine learning. This course will walk you through creating a machine learning prediction solution and will introduce Python, the scikit-learn library, and the Jupyter Notebook environment.

• Building Your First Machine Learning Solution - Machine learning is exciting, yet, it may sound more complicated than it is actually is. This course empowers you with the necessary theory and practice to become confident about how machine learning works by building a hands-on solution.

• The Supervised Learning Workshop - The Supervised Learning Workshop focuses on building up your practical skills so that you can deploy and build solutions that leverage key supervised learning algorithms.

• Using Python for Research - This course bridges the gap between introductory and advanced courses in Python. While there are many excellent introductory Python courses available, most typically do not go deep enough for you to apply your Python skills to research projects. In this course, after first reviewing the basics of Python 3, we learn about tools commonly used in research settings. This version of the course includes a new module on statistical learning.

• Building Regression Models with scikit-learn - This course covers important techniques such as ordinary least squares regression, moving on to lasso, ridge, and Elastic Net, and advanced techniques such as Support Vector Regression and Stochastic Gradient Descent Regression.

• r/datascience - A place for data science practitioners and professionals to discuss and debate data science career questions.

• r/MachineLearning - Welcome to MachineLearning

• r/learnmachinelearning - A subreddit dedicated to learning machine learning.

• Machine learning mastery - Excelent site with good content about machine learning for developers.

• FullBrain - Fullbrain is a social learning platform, to share and map all knowledge.

• IBM Machine learning with python - In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed!

• UCI Machine learning repository - The UCI Machine Learning Repository is a database of machine learning problems that you can access for free.

• SF Salaries - One way to understand how a city government works is by looking at who it employs and how its employees are compensated. This data contains the names, job title, and compensation for San Francisco city employees on an annual basis from 2011 to 2014.

• Video Game Sales - This dataset contains a list of video games with sales greater than 100,000 copies. It was generated by a scrape of vgchartz.com.

• Emergency - 911 Calls - This file contains calls to 911 (emergency calls) in Montgomery County Pennsylvania. This is a simple flat, CSV file.

• USA Housing Prices - USA Housing Prices.

• Titanic - Predict survival on the Titanic and get familiar with ML basics

• Lending Club data - Loan repayment prediction

• Iris Dataset - The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository. It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.

• Spotify Dataset 1921-2020 - The file contains more than 160.000 songs collected from Spotify Web API, and also you can find data grouped by artist, year, or genre in the data section.