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
- Best seller English, Spanish
- 99% positive reviews (165)
- 8533 students
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
- 99% positive reviews (165)
- 8533 students
- 29 lessons (4h 22m)
- 34 additional resources (16 files)
- Online and at your own pace
- Available on the app
- Audio: English, Spanish
- Spanish, English, Portuguese, German, French, Italian, Polish, Dutch
- 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
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
Enjoy learning from home without a set schedule and with an easy-to-follow method. You set your own pace.
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Learn from the best professionals
Learn valuable methods and techniques explained by top experts in the creative sector.
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Meet expert teachers
Each expert teaches what they do best, with clear guidelines, true passion, and professional insight in every lesson.
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Certificates
PlusIf you're a Plus member, get a custom certificate signed by your teacher for every course. Share it on your portfolio, social media, or wherever you like.
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Get front-row seats
Videos of the highest quality, so you don't miss a single detail. With unlimited access, you can watch them as many times as you need to perfect your technique.
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Share knowledge and ideas
Ask questions, request feedback, or offer solutions. Share your learning experience with other students in the community who are as passionate about creativity as you are.
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Connect with a global creative community
The community is home to millions of people from around the world who are curious and passionate about exploring and expressing their creativity.
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Watch professionally produced courses
Domestika curates its teacher roster and produces every course in-house to ensure a high-quality online learning experience.
FAQs
Domestika's courses are online classes that provide you with the tools and skills you need to complete a specific project. Every step of the project combines video lessons with complementary instructional material, so you can learn by doing. Domestika's courses also allow you to share your own projects with the teacher and with other students, creating a dynamic course community.
All courses are 100% online, so once they're published, courses start and finish whenever you want. You set the pace of the class. You can go back to review what interests you most and skip what you already know, ask questions, answer questions, share your projects, and more.
The courses are divided into different units. Each one includes lessons, informational text, tasks, and practice exercises to help you carry out your project step by step, with additional complementary resources and downloads. You'll also have access to an exclusive forum where you can interact with the teacher and with other students, as well as share your work and your course project, creating a community around the course.
You can redeem the course you received by accessing the redeeming page and entering your gift code.
rogerubiafatjo
Genial!
jorgedeceballos
Un curso que ha superado mis expectativas. Muy buen instructor.
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jorgemvicente
Very nice course, the contents are well explained. I recommend it!
arguellojulian972
GENIAL
ingsoria
Una explicación muy buena, sigo al profesor desde sus videos de Youtube y me parece que debe haber una linea de cursos sobre IA aplicados a proyectos reales donde se explique como publicar modelos en la nube o si hay alguna forma local de hacerlo, a su vez como aplicarlo con API por ejemplo.
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