Introduction to AI with Python
A course by Rodrigo Montemayor, Programmer
Learn the basics of artificial intelligence and how to apply it in problem-solving
Thanks to machine learning, all industries are revolutionizing the way they work. In this online course, the expert programmer Rodrigo Montemayor will teach you the fundamentals of machine learning with Python from scratch.
Throughout the lessons, you will learn what it is and how it works, what types exist, and how machine learning can be applied to solve various problems. Get ready to be part of the future of artificial intelligence.
What will you learn in this online course?
29 lessons & 34 downloads
- 98% positive reviews (447)
- 29,040 students
- 29 lessons (4h 22m)
- 34 additional resources (16 files)
- Online and at your own pace
- Available on the app
- Audio: Spanish, English, French, Indonesian, Italian, Portuguese, Romanian, Turkish
- Spanish · English · Portuguese · German · French · Italian · Polish · Dutch · Turkish · Romanian · Indonesian
- Level: Beginner
- Unlimited access forever
What is this course's project?
You will make a machine learning model that serves to classify and detect your own images using neural networks.
Projects by course students
Who is this online course for?
Anyone interested in learning the basics of machine learning.
Requirements and materials
To carry out this course, you will need basic knowledge of Python.
Reviews
Rodrigo Montemayor
A course by Rodrigo Montemayor
Rodrigo Montemayor has been a programmer for more than 20 years. He has worked with many of the largest companies in Mexico and also some international ones in web, mobile, and desktop development. In addition, through his own company, he has created software for all types of clients, such as various technology brands, cybersecurity, automation, and artificial intelligence.
On the other hand, he shares content on the RingaTech Youtube channel with the aim of teaching and informing about technology, cybersecurity, and artificial intelligence. As part of the channel, he has developed some projects and games whose code is available to the public.
Content
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U1
Introduction
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Presentation
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influences
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U2
The basics of machine learning
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What is machine learning?
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Machine learning possibilities
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Configuration of the work environment
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U3
your first models
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Types and examples of machine learning
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Data: the basis of any model
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Python libraries
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Your first model: linear regression
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Linear regression in detail
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U4
Machine learning algorithms
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Sets and data analysis
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Linear regression exercise
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Logistic regression
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Logistic regression exercise
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decision trees
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Decision trees exercise
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U5
More machine learning algorithms
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support vector machines
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Support vector machine exercise
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Clustering with K-means
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Clustering exercise with K-means
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U6
Neural networks and deep learning
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Intuition and how neural networks learn
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Regression neural network
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Classification 1 neural network
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Classification Neural Network 2
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Convolutions and filters
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Convolutional Neural Networks 1
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Convolutional Neural Networks 2
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Learning Transfer 1
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Learning transfer 2
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FP
Final project
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Introduction to AI with Python
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What to expect from a Domestika course
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Learn at your own pace
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Learn from the best professionals
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Meet expert teachers
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Certificates
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Get front-row seats
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Share knowledge and ideas
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Connect with a global creative community
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Watch professionally produced courses
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FAQs
You’ll master key Machine Learning concepts, learn how to apply algorithms to real-world problems, work with computer vision, prepare and analyze data, and build practical projects using Python and popular libraries like pandas, scikit-learn, and TensorFlow.
This is an intermediate-level course. It’s best if you have basic Python programming skills and a general understanding of math to get the most out of the lessons and exercises.
You’ll need Python installed, access to Google Colab or Visual Studio Code, and basic programming knowledge. A stable internet connection and a computer capable of running notebooks and Machine Learning libraries are also recommended.
You’ll work on projects like image and text classification, anomaly detection, customer segmentation, regression and binary classification models, and computer vision applications using real datasets and Python libraries.
The course covers applications such as computer vision, pattern recognition, fraud detection, medical diagnosis, price prediction, customer segmentation, and personalized recommendation systems.
Unlike traditional programming, Machine Learning lets systems learn from data and improve automatically, rather than following explicitly coded rules.
You’ll use pandas for data analysis, scikit-learn for Machine Learning models, TensorFlow and Keras for neural networks, plus Google Colab and Visual Studio Code to code and run your projects.
Un curso increíble y super entendible
Saludos Rodrigo Montemayo, me alegra mucho la forma como explicas me parece muy didáctico y entendible solo recomendaria que vayas un poco mas despacio ya que en mi caso estoy recien aprendiendo a dar contigo mis primeros pasos en el mundo de la informatica.
excelente explicación del profesor, buen curso
Logical and well explained
Es un excelente curso bien explicado y ameno gracias.