Welcome
IPN-Dharma AI Lab
This is an IPN CIC - DHARMA initiative to provide an Artificial Intelligence Laboratory to motivate researchers, professors and students to take advantage of the courses, resources and tools of the main technology platforms of the industry in the areas of Machine Learning, Data Science, Cloud Computing, Artificial Intelligence and Internet of Things with the purpose of generating a practical experience through a learning model between peers and by objectives.
Level 1: Literacy and Foundations
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).
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
Courses in this program
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".
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".
Estimated effort 12 weeks
English language
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.