"100 Days of Machine Learning Code Challenge" proposed by Siraj Raval

Spanish

Progress: It starts and progresses in the course of Machine Learning Applied with Python by Platzi. The following chapters are finalized:

1. Introduction
2. How to define a Machine Learning problem
3. The Machine Learning engineering cycle
4. Set up a Pydata enviroment
5. Data preparation
6. Modeling and evaluation

Thoughts:

1. The concepts of preprocessing and dataset features visualization are more clear.
2. Functionalities of the libraries numpy, pandas, matplotlib, seaborn and scikit-learn are known.
3. The Lasso regression model is known and applied.

Link(s) to work: Platzi-ML-IMDB

Progress:

1. I finished Feature Engineering chapter from Machine Learning Aplicado con Python course by Platzi.
2. I watched ML.NET sessions from Microsoft DotNetConf 2018 conference.

Thoughts:

1. I learned feature engineering techniques to apply to datasets like: reduction, transformation, scaling and binary encoding..
2. I can look to ML.NET like a development framework growing fast and in a midterm it can help me develop business apps combined with ML. I like this idea...I have to start trying it.

Link(s) to work:

1. Platzi-ML-IMDB
2. Machine Learning in .NET (ML.NET)
3. Artificial Intelligence and Machine Learning for Every .NET Developer

Progress:

1. I finished Machine Learning Aplicado con Python course by Platzi.
2. The basic concepts of ML are reviewed again by reading blogs.

Details:

1. Validation methods (Cross validation, validation curves and learning curves) and model evaluations for Decision trees with con Ensembles and Grid Search are studied.
2. When studying again the basic concepts of ML like its applications and algorithms, it is always good to help to understand in a better way the different concepts in which it has been deepening.

Link(s) to work:

1. Commit: Platzi-ML-IMDB
2. Blog Aprende Machine Learning: http://www.aprendemachinelearning.com
3. Blog Aprende sobre Machine Learning: http://ligdigonzalez.com

Progress:

I assisted to a local event in my city about Data Science & Artificial Neural Networks hosted by Python Cali

Progress:

1. I did exercises to practice ML algorithms: Exercises of Iris flowers classification and survival predictions on the Titanic are done using Regression Logistic, KNN, SVM and Decision trees models.
2. The first 2 chapters of the Curso de Redes Neuronales y Backpropagation by Platzi are studied.

Link(s) to work:

Commit: ML practices.

Progress:

Working on a introductory presentation about Machine Learning to share with a group of friends.

Link(s) to work:

Commit: ML Intro

Progress:

1. Done introductory presentation about Machine Learning to share with a group of friends.
2. I hosted a "mini meetup" with a group of friends to share my knowledge about ML.

Link(s) al trabajo:

Commit: ML Intro

Progress:

1. I finished Redes Neuronales y Backpropagation course by Platzi.
2. I started Move 37 course by School of AI.

Progress:

1. I watched Artificial Intelligence for the impatient developer video from .NET Conf CO v2017.
2. Making progress with Move 37 course by School of AI.

Progress:

Practice OpenAI Gym with the environment CartPole to implement and learn the basic RL methods of Random Search and Hill-Climbing.

Source: http://kvfrans.com/simple-algoritms-for-solving-cartpole/

Link(s) to work:

Commit: OpenAI Gym-CartPole

Progress:

1. I watched Machine Learning 101 video from .NET Conf CO v2017.
2. I watched Google Dopamine video from Move 37 course by School of AI.

Progress:

1. I watched Welcome to the age of conversational interfaces video from .NET Conf CO v2017.
2. I started Introducción a Deep Learning course by Platzi.

Progress:

I watched the next videos about AI:

1. How Smart Agents will shape the future from .NET Conf CO v2017.
2. Inteligencia artificial y SQL Server 2017 from .NET Conf CO v2017.
3. Servicios Cognitivos en la nube (visión, voz, traducción) from .NET Conf CO v2017.
4. Conversational UI for Bots from Xamarin Show.

Progress:

1. I watched AI for Every Developer video from .NET Conf 2018.
2. I finished Introducción a Deep Learning course by Platzi.

Progress:

1. I did an exercise to predict the prices of Boston houses using Linear Regression model.
2. I did an executive introductory presentation to ML.
3. I hosted a hangout with a group of friends to explain basics ML exercises of Classification and Regression.

Link(s) to work:

1. Commit: ML practices.
2. Commit: Presentation.

Progress:

I've started Chapter 01 of Building Machine Learning Systems with Python book.

Progress:

1. I watched Getting Started with Visual Studio Tools for AI video from Microsoft Build 2018.
2. I watched Cognitive Services in Xamarin Applications video from Microsoft Build 2018.
3. I've finished chapter 01 and started chapter 02 of Building Machine Learning Systems with Python book.

Progress:

1. I watched Demystifying Machine and Deep Learning for Developers video from Microsoft Build 2018.
2. I've finished Chapter 02 of Building Machine Learning Systems with Python book.

Progress:

1. I've finished chapter 03 of Building Machine Learning Systems with Python book.
2. I've read some articles from DZone AI Zone.

Link(s) to work:

• 11 Deep Learning With Python Libraries and Frameworks.
• Top Machine Learning Algorithms You Should Know to Become a Data Scientist.
• How to Get Started With Conversational AI.
• Introduction to AI for Enterprises.

Progress:

1. I wrote a story on Medium about my personal motivation with AI (Spanish only).
2. I read some articles about comparisons between Fast.ai and Deeplearning.ai courses.

Link(s) to work:

• Motivación personal para iniciar con Inteligencia Artificial.
• Launching fast.ai.
• Meet These Incredible Women Advancing A.I. Research.
• FAST.AI: UP TO SPEED WITH THE BEST OF DEEP LEARNING.
• Fast.ai tips from a complete newbie.
• Ten Techniques Learned From fast.ai.
• Learning Deep Learning — fast.ai vs. deeplearning.ai.

Progress:

I watched the next videos about AI:

1. How to Learn Deep Learning (when you’re not a computer science PhD).
2. Road to Deep Learning.
3. Road to Deep Learning II.

Progress:

1. Video Conceptos básicos de Machine Learning.
2. Video Introducción a los entornos virtuales en Python.
3. Video Road to Deep Learning III Redes convolucionales.
4. I've review chapters 1 and 2 from Building Machine Learning Systems with Python book.
5. Video Error cuadrático en regresión lineal de Khan Academy.

Progress:

1. I've review chapter 3 from Building Machine Learning Systems with Python book.
2. I've review day 1 of Crash Course from PyImageSearch.

Link(s) to work:

• PyImageSearch-CrashCourse-Day1.

Progress:

I've review day 2 of Crash Course from PyImageSearch.

Link(s) to work:

• PyImageSearch-CrashCourse-Day2.

Progress:

Done: Day 3 and 4 of Crash Course from PyImageSearch.

Link(s) to work:

• PyImageSearch-CrashCourse-Day3.
• PyImageSearch-CrashCourse-Day4.

Progress:

• I've listened Computer Vision Explained with PyImageSearch's Adrian Rosebrock podcast from Hanselminutes.
• I've watched Machine Learning en ArcGIS webinar from Esri Colombia.

Progress:

• Techniques like Cross validation, Accuracy score, Confusion matrix and Classification report are applied using a RandomForestClassifier model for Iris y Titanic exercises.
• Techniques like Root Mean Square Error (RMSE), Coefficient of determination (r2_score), L1 and L2 penalties are applied using a ElasticNet model for Boston houses exercise.

Link(s) to work:

• ML-Exercises Repo.

Progress:

• I've watched Netflix documentary AlphaGo.
• I've watched the next videos:
  • Qué necesitas para hacer Inteligencia Artificial from AMP Tech.
  • Drone Flight Controller from Siraj Raval.
  • 7 Ways to Make Money with Machine Learning from Siraj Raval.
• I've assisted to the Google's virtual event Let's Talk AI.
• Done: Day 5 and 6 of Crash Course from PyImageSearch.

Link(s) to work:

• PyImageSearch-CrashCourse-Day5.
• PyImageSearch-CrashCourse-Day6.

Progress:

Done: Day 7 and 8 of Crash Course from PyImageSearch.

Link(s) to work:

• PyImageSearch-CrashCourse-Day8.

Progress:

I've watched the next videos:

• Time Series Prediction from Siraj Raval.
• Train Machine Learning Models with Azure ML in VS Code from AI Show-Channel 9.
• Taking a Look at Computer Vision’s Object Detection from AI Show-Channel 9.
• Build a Bot in Minutes with QnA Maker from AI Show-Channel 9.

Progress:

• I've watched What's new with Speech Services video from AI Show-Channel 9.
• Done: Day 9 and 10 of Crash Course from PyImageSearch.

Link(s) to work:

• PyImageSearch-CrashCourse-Day9.
• PyImageSearch-CrashCourse-Day10.

Progress:

• Presentations about classification in Machine Learning are adjusted.
• I've watched the following videos from AI Show-Channel 9:
  • Using Cognitive Search to Understand the JFK Documents.
  • Building an Intelligent Bot: Conference Buddy.
• Done: Day 11, 12 and 13 of Crash Course from PyImageSearch.

Link(s) to work:

• PPT-Clasificación I.
• PPT-Clasificación II.
• PyImageSearch-CrashCourse-Day12.
• PyImageSearch-CrashCourse-Day13.

Progress:

• Done: Day 14 and 15 of Crash Course from PyImageSearch.

Link(s) to work:

• PyImageSearch-CrashCourse-Day14.
• PyImageSearch-CrashCourse-Day15.

Progress:

• Crash Course from PyImageSearch DONE.

Link(s) to work:

• PyImageSearch-CrashCourse-Day16.

Progress:

• Done: Lab - Introduction to Custom Vision Service of Learning Path: Custom Vision Service from Microsoft AI School program.

Link(s) to work:

• Custom Vision Service-Lab Introduction to Custom Vision Service.

Progress:

• Microsoft AI School Program - Learning Path: Custom Vision Service: Lab Exporting a Custom Vision model and deploy it to an Android device: In progress.
• PyImageSearch book - DL4CV: Chapter 1: Done and Chapter 2: In progress.

Progress:

• I've read the following articles:
  • Build Your Customized Vision App in 10 Minutes.
  • Building a Classification Model for Android, iOS and Windows ML using CustomVision.ai.
  • Using TensorFlow and Azure to Add Image Classification to Your Android Apps.
• I've watched the following videos:
  • Easier AI allows everyone to build smart apps - David Burela & Azadeh Khojandi.
  • Custom Vision – Machine Learning Made Easy with Jim Bennett.
• Microsoft AI School Program - Learning Path: Custom Vision Service: Lab Exporting a Custom Vision model and deploy it to an Android device: Done.

Link(s) to work:

• Custom Vision Service-Lab Exporting a Custom Vision model and deploy it to an Android device.

Progress:

• I've read the following articles:
  • What is Deep Learning?.
  • A Gentle Introduction to Transfer Learning for Deep Learning.
• PyImageSearch book - DL4CV: Chapter 2: Done.

Progress:

PyImageSearch book - DL4CV: Chapter 3: Done.

Progress:

Microsoft AI School Program - Learning Path: Custom Vision Service: Lab Exporting a Custom Vision model and deploying on iOS: Done.

Link(s) to work:

• Custom Vision Service-Lab Exporting a Custom Vision model and deploying on iOS.

Progress:

PyImageSearch book - DL4CV: Chapter 4: Done.

Progress:

PyImageSearch book - DL4CV: Chapter 5 and 6: Done.

Progress:

• PyImageSearch book - DL4CV: Chapter 7: Done.
• Presentation about Azure Cognitive Services: Done.

Link(s) to work:

• Azure Cognitive Services.

Progress:

PyImageSearch book - DL4CV: Chapter 8: Done + Chapter 9: In progress.

Progress:

PyImageSearch book - DL4CV: Chapter 9: Done.