Arewa Datascience Machine Learning for Beginners

Arewa Datascience presents this 10-week online course, as part of the Arewa Datascience Fellowship. In this curriculum, you will learn about classical machine learning, using pandas and Scikit-learn libraries. The curriculum does not include deep-learning, as it is anticipated to be covered in the future (second cohort).

Getting Started

Prerequisite:

Our fellows have completed the Python-30 Day challenge that we prepared. If you are following this curriculum, we expect you to have a basic understanding of Python.

Fellows, to use this curriculum, fork the entire repo to your own GitHub account and complete the exercises on your own or with a group:

Start with a pre-lecture quiz.
Read the lecture and complete the activities, pausing and reflecting at each knowledge check.
Try to create the projects by comprehending the lessons rather than running the solution code; however that code is available in the /solution folders in each project-oriented lesson.
Take the post-lecture quiz.
Complete the challenge.
Complete the assignment.
After completing a lesson group, visit the Telegram Group and "learn out loud" by writing about the new knowledge you gained. Don't forget to write a medium blog post about what you learn each week.

Pedagogy

We have chosen two pedagogical tenets while building this curriculum: ensuring that it is hands-on project-based and that it includes frequent quizzes. In addition, this curriculum has a common theme to give it cohesion.

By ensuring that the content aligns with projects, the process is made more engaging for students and retention of concepts will be augmented. In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and should be taken in whole. The projects start small and become increasingly complex by the end of the 10-week cycle. This curriculum also includes a postscript on real-world applications of ML, which can be used as extra credit or as a basis for discussion.

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

A note about languages: These lessons are primarily written in Python, but many are also available in R. To complete an R lesson, go to the /solution folder and look for R lessons. They include an .rmd extension that represents an R Markdown file which can be simply defined as an embedding of code chunks (of R or other languages) and a YAML header (that guides how to format outputs such as PDF) in a Markdown document. As such, it serves as an exemplary authoring framework for data science since it allows you to combine your code, its output, and your thoughts by allowing you to write them down in Markdown. Moreover, R Markdown documents can be rendered to output formats such as PDF, HTML, or Word.

Week	Topic	Lesson Grouping	Learning Objectives	Linked Lesson	Mentors
01	Introduction to machine learning	Introduction	Learn the basic concepts behind machine learning	Introduction History Fairness Techniques	Ibrahim Ahmad
02	Working with Data	Working With Data	Introduction to pandas and data preparation	Python libraries lesson	Idris Abdulmumin
03	Data Visualization	Data Visualization	Introduction to Matplotlib, Data Distributions, Proportions and Relationships and Meaningful Visualizations - bird data 🦆	Quantities Distributions Proportions Relationships Meaningful Visualizations	Shamsuddeen Hassan Muhammad Falalu
04	Regression	Regression	Tools, Data Visualization and Regression Models - North American pumpkin prices 🎃	Tools Data preprocessing Linear Regression Logistic Regression
05	Classification	Classification	Data preprocessing, classifiers - Delicious Asian and Indian cuisines 🍜	Introduction Classifiers 1 Classifiers 2 Recommender App
06	Clustering	Clustering	Data preprocessing, clustering - Exploring Nigerian Musical Tastes 🎧	Data Visualization K-Means
07	Natural language processing ☕️	Natural language processing	Learn the basics about NLP by building a simple bot	Introduction Common Tasks Translation	Shamsuddeen Idris
08	Sentiment Analysis	Natural language processing	Sentiment analysis with hotel reviews	Hotel Reviews 1 Hotel Reviews 2	Shamsuddeen Ibrahim
09	Time Series Forecasting	Time series	Introduction, ARIMA, Support Vector Regressor (SVR) - ⚡️ World Power Usage ⚡️	Introduction ARIMA SVR	Samuel Adeoluwa Aduroja
10	Reinforcement Learning	Reinforcement learning	Introduction, Reinforcement learning with Q-Learning and Gym	Q-Learning Gym
Projects	Real-World ML scenarios and applications	ML in the Wild	Interesting and revealing real-world applications of classical ML	Lesson	Team

Offline access

You can run this documentation offline by using Docsify. Fork this repo, install Docsify on your local machine, and then in the root folder of this repo, type docsify serve. The website will be served on port 3000 on your localhost: localhost:3000.

Source

The curriculum for this course were adapted from Microsoft's 'ML-For-Beginners' and 'DS-For Beginners' curricula.

khalidassalafy / ML-4-Beginners-Arewa-Datascience