IPN-Dharma IA Lab

    Bienvenidos
    IPN-Dharma IA Lab

    Es una iniciativa de Laboratorio de Inteligencia Artificial del CIC del IPN con la colaboración de DHARMA para motivar a investigadores, profesores y estudiantes a aprovechar los cursos, recursos y herramientas de las principales plataformas tecnológicas de la industria en las áreas de Aprendizaje Automático, Ciencia de Datos, Computación en la Nube, Inteligencia Artificial e Internet de las Cosas con el propósito de generar una experiencia práctica a través de un modelo de aprendizaje entre pares y por objetivos.

    Nivel 1: Alfabetización y Fundamentos

    Machine Learning for Beginners

    Machine Learning for Beginners is a 24-lesson curriculum, plus a bonus 'postscript' lesson, all about Machine Learning. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment, and more. The project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.

    Travel with us around the world as we apply these classic techniques to data from many areas of the world. Discover North American pumpkin market pricing (Regression), Pan-Asian cuisines (Classification), Nigerian musical tastes (Clustering), European Hotel Reviews (NLP), World electricity usage (Time Series) and the Russian story about Peter and the Wolf (Reinforcement Learning).

    Each lesson includes:

    • Optional sketchnote
    • Optional supplemental video
    • Pre-lecture warmup quiz
    • Written lesson
    • For project-based lessons, step-by-step guides on how to build the project
    • Knowledge checks
    • A challenge
    • Supplemental reading
    • Assignment
    • Post-lecture quiz

    Cursos en este programa

    1) Machine Learning for Beginners

    In this curriculum, you will learn about what is sometimes called classic machine learning, using primarily Scikit-learn as a library.

    The lessons are grouped so that you can deep-dive into various important aspects of classic ML. We start with an introduction to ML concepts, moving to its history, concepts of fairness in machine learning, and discussing the tools and techniques of the trade. We then move on to Regression, Classification, Clustering, Natural Language Processing, Time Series Forecasting, Reinforcement Learning, with two 'applied' lessons demonstrating how to use your models within web apps for inference. We end with a 'postscript' lesson listing "real-world" applications of ML, showing how these techniques are used "in the wild".

    Esfuerzo  Esfuerzo estimado 12 semanas

    Idioma  Idioma inglés

    Link  GitHub

    Introduction
    01
    Introduction to machine learning
    Learn the basic concepts behind machine learning.
    02
    The History of machine learning
    Learn the history underlying this field.
    03
    Fairness and machine learning
    What are the important philosophical issues around fairness that students should consider when building and applying ML models?
    04
    Techniques for machine learning
    What techniques do ML researchers use to build ML models?
    Regression
    05
    Introduction to regression
    Get started with Python and Scikit-learn for regression models.
    06
    North American pumpkin prices
    Visualize and clean data in preparation for ML.
    07
    North American pumpkin prices
    Build linear and polynomial regression models.
    08
    North American pumpkin prices
    Build a logistic regression model.
    Web App
    09
    A Web App
    Build a web app to use your trained model.
    Classification
    10
    Introduction to classification
    Clean, prep, and visualize your data; introduction to classification.
    11
    Delicious Asian and Indian cuisines
    Introduction to classifiers.
    12
    Delicious Asian and Indian cuisines
    More classifiers.
    13
    Delicious Asian and Indian cuisines
    Build a recommender web app using your model.
    Clustering
    14
    Introduction to clustering
    Clean, prep, and visualize your data; Introduction to clustering.
    15
    Exploring Nigerian Musical Tastes
    Explore the K-Means clustering method.
    Natural language processing
    16
    Introduction to natural language processing
    Learn the basics about NLP by building a simple bot.
    17
    Common NLP Tasks
    Deepen your NLP knowledge by understanding common tasks required when dealing with language structures.
    18
    Translation and sentiment analysis
    Translation and sentiment analysis with Jane Austen.
    19
    Romantic hotels of Europe
    Sentiment analysis with hotel reviews 1.
    20
    Romantic hotels of Europe
    Sentiment analysis with hotel reviews 2.
    Time series
    21
    Introduction to time series forecasting
    Introduction to time series forecasting.
    22
    World Power Usage - time series forecasting with ARIMA
    Time series forecasting with ARIMA.
    23
    World Power Usage - time series forecasting with ARIMA
    Time series forecasting with Support Vector Regressor.
    Reinforcement learning
    24
    Introduction to reinforcement learning
    Introduction to reinforcement learning with Q-Learning
    25
    Help Peter avoid the wolf!
    Reinforcement learning Gym.
    ML in the Wild
    Postscript
    Real-World ML scenarios and applications
    Interesting and revealing real-world applications of classical ML.
    © 2015 |Laboratorio de Microtecnología y Sistemas Embebidos | Centro de Investigación en Computación | Instituto Politécnico Nacional