Cooking up Artificial Intelligence with Python
By Fernando Borras Rocher | PhD in Statistical Sciences and Techniques
15 day refund guarantee
This course includes:
āŖļø 18h duration on demand
āŖļø 230 lessons
āŖļø 9 downloadable resources
āŖļø Available on mobile devices
āŖļø Access forever
āŖļø Language:
- Spanish
āŖļø Unlimited consultations
Supplementary materials
ā Certificate of completion
What you will learn
What you will learn
š§ Discover Jupyter Notebooks and the Google Colab environment for programming in Python.
š§ Discover other features of a Colab notebook and its connection to Google Drive.
š§ Effectively manage the on-screen display of the desired results.
š§ Create and manipulate boolean data.
š§ Program code that is executed depending on different conditions (one alternative with else and several alternatives with elif).
š§ Learn how to save the results of a loop in an empty list and with the append function.
š§ Learn how to apply a specific format to strings and numbers when displaying messages on the screen.
š§ Learn common functions in the random module with random simulators.
š§ Learn how to define and use functions with default arguments to operate by default.
Course content
Course content
U1: Python's Kitchen
- Python's Kitchen!
- What are Jupyter Notebooks?
- Jupyter Notebooks Components
- Google Colab's rivals
- The Colaboratory Web Application free Jupyter Notebook environment
- Colaboratory Runtime Environments
- Colaboratory Documents
- Text cells
- Text colors and sizes
- Code Cells
- Adding and moving cells
- Enriched and interactive outputs
- Example of a graph with formulas
- Interactive chart example
- Example of an interactive cell with hidden code
- Binomial Distribution
- Web page embedded in code cell
- Embedded Web Page
- YouTube video embedded inside a code cell
- Embedded YouTube video
- Integration with Drive
- Comments in a cell
- Installing the "open in colab" extension
- Open In Colab Badge
- A gallery of interesting Jupyter notebooks
- Hands in the dough!
U2: Python's Kitchen Utensils
- Python's Kitchen Utensils
- Hands in the dough 1!
- Solution: Hands in the dough 1!
- Python as an advanced calculator
- Operator precedence
- Assignment operator
- Hands in the dough 2!
- Solution: Hands in the dough 2!
- "Print" function
- What we have learned
- String manipulation
- Hands in the dough 3!
- Solution: Hands in the dough 3!
- String indexing
- Hands in the dough 4!
- Solution: Hands in the dough 4!
- String slicing
- Hands in the dough 5!
- Solution: Hands in the dough 5!
- Hands in the dough 6!
- Solution: Red-handed 6!
- What we have learned
U3: The Python's Pantry
- The Python's Pantry
- Lists
- Hands in the dough 1!
- Solution: Hands in the dough 1!
- The append method in list management
- Hands in the dough 2!
- Solution: Hands in the dough 2!
- Text strings are immutable
- The remove() method in list management
- Hands in the dough 3!
- Solution: Hands in the dough 3!
- Concatenation of lists
- The sort() method in list management
- Nested lists
- Hands in the dough 4!
- Solution: Hands in the dough 4!
- What we have learned
- Dictionaries
- Hands in the dough 5!
- Solution: Hands in the dough 5!
- The keys() and values() methods in dictionaries
- Tuples and Sets
- What we have learned
U4: Python Cooking Processes
- Python Cooking Processes
- Simple conditions
- if, elif and else
- Code block indentation
- Hands in the dough 1!
- Solution: Hands in the dough 1!
- Operators and, or and not
- Hands in the dough 2!
- Solution: Hands in the dough 2!
- What we have learned
- Loops
- Hands in the dough 3!
- Solution: Hands in the dough 3!
- Hands in the dough 4!
- Solution: Hands in the dough 4!
- What we have learned
U5: Iterative cooking processes in Python
- Iterative cooking processes in Python
- The enumerate() function
- Hands in the dough 1!
- Solution: Hands in the dough 1!
- The zip() function
- Hands in the dough 2!
- Solution: Hands in the dough 2!
- The sorted() and reversed() functions
- The min(), max() and sum() functions
- Hands in the dough 3!
- Solution: Hands in the dough 3!
- List Comprehensions
- Hands in the Dough 4!
- Solution: Hands in the Dough 4!
- The while loop
- An infinite loop... (:
- Break sentence
- Sentence continues
- range() function
- Hands in the dough 5!
- Solution: Hands in the dough 5!
- Hands in the dough 6!
- Solution: Red-handed 6!
U6: Cooking text strings in Python
- Cooking strings in Python
- Binary and decimal number systems
- Integer storage and overflow
- Introspection of types
- Implicit type conversion (casting)
- Explicit type conversion
- Hands in the dough 1!
- Solution: Hands in the dough 1!
- Conversion between collections (Tuples, Sets, Lists)
- Hands in the dough 2!
- Solution: Hands in the dough 2!
- String manipulation functions
- The split() method
- Hands in the dough 3!
- Solution: Hands in the dough 3!
- The join() method
- Hands in the dough 4!
- Solution: Hands in the dough 4!
- The find() method
- Hands in the Dough 5!
- Solution: Hands in the Dough 5!
- The replace() method
- Hands in the dough 6!
- Solution: Red-handed 6!
- The strip() method and new lines
- Hands in the dough 7!
- Solution: Red handed 7!
- The lower() and upper() method
- Hands in the dough 8!
- Solution: Red handed 8!
- String formatting functions
- f-string New string interpolation method as of python 3.6
U7: The Python Cookbook
Python Cookbooks
Using standard library functions: math module
Using standard library functions: random module
Using standard library functions: time module
Install external modules
Hands in the dough 1!
Solution: Hands in the dough 1!
Hands in the dough 2!
Solution: Hands in the dough 2!
U8: Python External Pantries
- Python External Pantries
- Google Colab
- Archives
- Shell Commands in Google Colab Jupyter Notebooks
- Google Colab File System
- Upload files from your local file system to Google Colab
- Download files from the internet to Google Collab
- Passing values āāto and from the shell to the Jupyter notebook
- Writing files in Google Colab
- File Object Attributes
- close() method
- Reading and Writing Files
- read() method to read part or all of the file
- readline() method to read one line at a time
- The readlines() method to read the lines as a list
- Using the with statement with loops using read(), readline(), or readlines()
- Position of a file
- Download files from Google Colab to local computer
- Download Google Colab file sets to local machine
- Activate Google Drive locally
- Version using code snippets
- Own version in code cell
- Create a new Drive file with Python data
- Download file sets to local machine in Google Drive
- Upload files from your local file system to Google Drive
- Move files from Google Colab to Google Drive
- Hands in the dough 1!
- Hands in the dough solution 1!
- Caught in the act 2!
- Hands in the dough solution 2!
- Caught in the act 3!
- Hands in the dough solution 3!
- Direct access to Google Drive file without user validation
- Upload file to Google Colab
- Zip file upload to Colab Google
U9: My recipe book.mp4
- My recipe book
- Functions
- Purpose
- Function arguments
- What can be passed as an argument to a function?
- Default arguments
- Return arguments
- Scope
- Scope of variables
- Local variables
- Global variables
- Pass by value or by reference
- Red handed!
- Solution: Red-handed 0!
- Reviewing file reading
- Hands in the dough 1!
- Solution: Hands in the dough 1!
- Writing our first function
- Hands in the dough 2!
- Solution: Hands in the dough 2!
- The count_in_list function
- Hands in the dough 3!
- Solution: Hands in the dough 3!
- A more general counting function
- The counter function (version 1)
- Hands in the dough 4!
- Solution: Hands in the dough 4!
- The counter function (version 2)
- Text cleaning
- Hands in the dough 5!
- Solution: Hands in the dough 5!
- General cleaning function
- Hands in the dough 6!
- Solution: Red-handed 6!
- Hands in the dough 7!
- Solution: Red handed 7!
- Writing results to a file
- Hands in the dough 8!
- Solution: Red handed 8!
Course evaluation
- This course contains a final exam
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ā Certificate of completion
- Your personalized digital certificate, a unique badge of your achievements, with international validity, course duration and QR code for instant verification.
__
Downloadable resources:
š Downloadable Guide Set:
āø Python's Kitchen.ipynb
āø Python.ipynb cooking processes
āø The Python Cookbook.ipynb
āø And more...
Description
Description
āļø The president of the American MIT, L. Rafael Reif, states that the goal of his university is to "educate the bilinguals of the future", defining bilinguals as people in fields such as biology, chemistry, politics, history and linguistics, who are also trained in artificial intelligence techniques that can be applied to them.
It is necessary for any university student, regardless of their degree, to know the language of machines (Python programming) and for this we have chosen the Google Colab platform that allows for a soft landing and that in a few years will have a much greater impact than Google Drive has had on our lives.
Companies from all industries invest in the development of their teams with this course from G-Talent and Fernando Borras Rocher
Fernando Borras Rocher
PhD in Statistical Sciences and Techniques
About Fernando Borras Rocher
About Fernando Borras Rocher
PhD in Statistical Sciences and Techniques
With a solid background in Mathematics from the University of Valencia and specializing in Education from the University of Alicante, Fernando BorrƔs Rocher is a Professor in the Department of Statistics, Mathematics and Computer Science at the "Miguel HernƔndez" University of Elche. His extensive teaching experience ranges from Biostatistics in the Degree in Medicine to the subject of Artificial Intelligence Models in Health Sciences in the Master's Degree in Clinical Medicine Research.
Fernando leads the Cyborg Experimental Center and the Nanocursos virtual learning environment, where traditional teaching is combined with innovative learning methodologies. His educational approach is characterized by a unique combination of theory and practice, designed to provide students with the necessary tools to excel in the field of health sciences and research.
Throughout his career, Fernando has focused his research on optimizing the location of hierarchical public services, such as hospitals and health centers, as well as on the strategic placement of mobile emergency services (SAMU) to ensure service equity and efficiency. These works allowed him to obtain his PhD in Statistical Sciences and Techniques. His interest in Efficiency Analysis (DEA) and sampling has also led to important contributions in these fields. Furthermore, the application of artificial intelligence in health sciences has become a key line of research in his career, reflecting his commitment to innovation and the advancement of knowledge in this sector.
What makes Fernando different?
āŖļø Academic and Practical Experience: More than 20 years dedicated to teaching and research in statistics, mathematics and computer science, with an outstanding track record in the application of advanced statistical methods and artificial intelligence models in the field of health.
āŖļø Recognized Research: Fernando is a prolific researcher with significant contributions in areas such as the location of health services, the efficiency in the distribution of emergency resources, and artificial intelligence applied to health sciences.
āŖļø Innovative Approach to Education: As director of the Cyborg Experimental Center and the Nanocursos virtual environment, Fernando promotes active and experimental learning, using the latest technologies to improve the educational experience.
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El foro del curso es muy activo y siempre hay alguien dispuesto a ayudar con las dudas.
Este curso me ha dado las herramientas necesarias para comenzar mi carrera en este campo.
Puedo estudiar a mi propio ritmo y desde cualquier lugar.
Si estƔs empezando en el mundo de la IA, este es el curso ideal.
Es increĆble ver cĆ³mo funciona la IA en la prĆ”ctica.
Es una inversiĆ³n que vale la pena hacer.
El profesor explica los conceptos de manera clara y concisa.
Este curso me ha convencido de que es el lenguaje ideal para trabajar con IA.
Es increĆble pensar en todas las posibilidades que ofrece esta tecnologĆa.
Nunca pensĆ© que entenderĆa algo tan complejo como la IA. Este curso me ha abierto las puertas a un mundo fascinante.
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