Intro to Machine Learning and AI

Course materials adapted from Microsoft's course.

Lesson Number	Topic	Lesson Grouping	Learning Objectives
01	Introduction to machine learning	Introduction	Learn the basic concepts behind machine learning
02	Techniques for machine learning	Introduction	What techniques do ML researchers use to build ML models?
03	Introduction to regression	Regression	Get started with Python and Scikit-learn for regression models
04	North American pumpkin prices 🎃	Regression	Visualize and clean data in preparation for ML
05	North American pumpkin prices 🎃	Regression	Build linear and polynomial regression models
06	North American pumpkin prices 🎃	Regression	Build a logistic regression model
07	A Web App 🔌	Web App	Build a web app to use your trained model
08	Introduction to classification	Classification	Clean, prep, and visualize your data; introduction to classification
09	Delicious Asian and Indian cuisines 🍜	Classification	Introduction to classifiers
10	Delicious Asian and Indian cuisines 🍜	Classification	More classifiers
11	Delicious Asian and Indian cuisines 🍜	Classification	Build a recommender web app using your model
12	Introduction to clustering	Clustering	Clean, prep, and visualize your data; Introduction to clustering
13	Exploring Nigerian Musical Tastes 🎧	Clustering	Explore the K-Means clustering method
14	Introduction to natural language processing ☕️	Natural language processing	Learn the basics about NLP by building a simple bot
15	Common NLP Tasks ☕️	Natural language processing	Deepen your NLP knowledge by understanding common tasks required when dealing with language structures
16	Translation and sentiment analysis ♥️	Natural language processing	Translation and sentiment analysis with Jane Austen
17	Romantic hotels of Europe ♥️	Natural language processing	Sentiment analysis with hotel reviews 1
18	Romantic hotels of Europe ♥️	Natural language processing	Sentiment analysis with hotel reviews 2
19	Neural Networks and Deep Learning	Neural Networks	Introduction to Neural Networks and Deep Learning
20	Anomaly Detection and Autoencoders	Neural Networks	Neural networks for fraud detection and image reconstruction
21	Using Pretrained Deep Learning Models	Neural Networks	Introduction to Neural Networks and Deep Learning

Setup

• You need Python 3.11 installed in your system.
• Clone the repository: git clone https://github.com/pablomdata/intro-ml-ai
• Go to the folder in the command prompt. Once there, use the command python -m venv env to create a virtual environment.
• Activate the virtual environment:
  • In Windows: env\Scripts\activate.bat
  • In Mac/Linux: source env/scripts/activate You should see an (env) next to your prompt.
• Install dependencies: pip install -r requirements.txt
• Launch Jupyter notebook: jupyter notebook