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Practical Python for AI Coding 1

Introduction video: https://youtu.be/TRhwIHvehR0

This course is for a complete novice of Python coding, so no prior knowledge or experience in software coding is required. This course selects, introduces, and explains Python syntaxes, functions, and libraries that were frequently used in AI coding. In addition, this course introduces vital syntaxes, and functions often used in AI coding and explains the complementary relationship among NumPy, Pandas, and TensorFlow, so this course is helpful for even seasoned python users. This course starts with building an AI coding environment without failures on learners’ desktop or notebook computers to enable them to start AI modeling and coding with Scikit-learn, TensorFlow, and Keras upon completing this course. Because learners have an AI coding environment on their computers after taking this course, they can start AI coding and do not need to join or use the cloud-based services.

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What's inside

Syllabus

Preparation for coding : Setting up AI coding environment

Basic concepts and rules of Python coding

Primitive data types

Control statements and iteration

Creating functions

Non-primitive data types: Lists and tuples

Non-primitive data types: Dictionaries and sets

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches fundamental Python syntaxes and functions, making it suitable for beginners

Introduces vital syntaxes and functions often used in AI coding, making it useful for experienced Python users as well

Covers NumPy, Pandas, and TensorFlow, providing a comprehensive understanding of essential AI libraries

Guides learners in setting up an AI coding environment, enabling them to start practical AI modeling and coding

Emphasizes hands-on learning, allowing learners to directly apply their knowledge in real-world AI projects

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Practical Python for AI Coding 1 with these activities:

Review basic programming concepts before starting the course

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Ensures a solid foundation in basic programming principles.

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Review online resources or textbooks on basic programming.
Complete practice exercises to test understanding.
Summarize the key concepts and their relevance to Python coding.

Read 'Head First Python' by Paul Barry

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Establishes a strong foundation in Python concepts and syntax.

View Head First Python: A Learner's Guide to the... on Amazon

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Read each chapter thoroughly, taking notes on key concepts.
Complete the practice exercises at the end of each chapter.
Review and summarize the main concepts covered in each chapter.

Organize and review notes, assignments, and course materials

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Improves retention and understanding by organizing and synthesizing course materials.

Browse courses on Note-Taking

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Create a system for organizing notes, assignments, and other course materials.
Regularly review and summarize the organized materials.
Identify areas where additional clarification or understanding is needed.

Four other activities

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Engage in online discussion forums related to Python and AI

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Facilitates knowledge sharing, peer support, and diverse perspectives on Python and AI.

Browse courses on Python

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Identify and join relevant online discussion forums.
Actively participate in discussions, asking and answering questions.
Review and summarize key takeaways from the discussions.

Practice Python coding exercises on LeetCode

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Reinforces Python coding skills and problem-solving abilities.

Browse courses on Python

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Select a set of LeetCode problems relevant to AI coding.
Attempt to solve the problems independently.
Review solutions and explanations to strengthen understanding.

Follow TensorFlow tutorials for deep learning

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Enhances understanding of deep learning concepts and TensorFlow implementation.

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Select a series of TensorFlow tutorials focused on deep learning.
Work through the tutorials, following the instructions and experimenting with different parameters.
Build a small deep learning project using the knowledge gained from the tutorials.

Write a blog post summarizing the key concepts learned in this course

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Reinforces understanding by requiring students to organize and articulate their knowledge.

Browse courses on Python

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Review the course materials and identify the key concepts.
Organize the concepts into a logical structure.
Write a concise and informative blog post explaining the concepts.
Proofread and publish the blog post.

Career center

Learners who complete Practical Python for AI Coding 1 will develop knowledge and skills that may be useful to these careers:

Actuary

Actuaries use mathematical and statistical techniques to assess risk and make financial decisions. Actuaries typically use Python to develop and implement financial models. This course can help build a foundation for these tasks by teaching the basics of Python coding, such as data types, control statements, and functions.

See salaries and explore the career path for Actuary

Data Scientist

Data scientists use scientific methods to analyze data and extract insights. This course can help build a foundation for these tasks by teaching the basics of Python coding, such as data types, control statements, and functions. Additionally, this course covers topics such as creating functions, non-primitive data types, and setting up an AI coding environment, which can be beneficial for Data Scientists.

See salaries and explore the career path for Data Scientist

Underwriter

Underwriters use data to assess risk and make insurance decisions. Underwriters typically use Python to develop and implement financial models. This course can help build a foundation for these tasks by teaching the basics of Python coding, such as data types, control statements, and functions.

See salaries and explore the career path for Underwriter

Operations Research Analyst

Operations Research Analysts use mathematical and statistical techniques to solve business problems. Operations Research Analysts typically use Python to develop and implement mathematical models. This course can help build a foundation for these tasks by teaching the basics of Python coding, such as data types, control statements, and functions.

See salaries and explore the career path for Operations Research Analyst

Risk Analyst

Risk analysts use data to identify and assess risks. Risk Analysts typically use Python to develop and implement risk models. This course can help build a foundation for these tasks by teaching the basics of Python coding, such as data types, control statements, and functions.

See salaries and explore the career path for Risk Analyst

Financial Analyst

Financial analysts use data to make investment decisions. Financial Analysts typically use Python to develop and implement financial models. This course can help build a foundation for these tasks by teaching the basics of Python coding, such as data types, control statements, and functions.

See salaries and explore the career path for Financial Analyst

Quantitative Analyst

Quantitative analysts use mathematical and statistical techniques to analyze data and make investment decisions. Quantitative Analysts typically use Python to develop and implement financial models. This course can help build a foundation for these tasks by teaching the basics of Python coding, such as data types, control statements, and functions.

See salaries and explore the career path for Quantitative Analyst

Data Engineer

Data engineers design, build, and maintain data pipelines. Data engineers typically use Python to develop and implement data pipelines. This course can help build a foundation for these tasks by teaching the basics of Python coding, such as data types, control statements, and functions.

See salaries and explore the career path for Data Engineer

Machine Learning Engineer

Machine learning engineers apply principles of software programming to design, develop, and maintain machine learning models. Machine Learning Engineers typically use Python to develop and implement machine learning models. This course can help build a foundation for these tasks by teaching the basics of Python coding, such as data types, control statements, and function.

See salaries and explore the career path for Machine Learning Engineer

Statistician

Statisticians collect, analyze, interpret, and present data. Statisticians typically use Python to perform data analysis and create visualizations. This course can help build a foundation for these tasks by teaching the basics of Python coding, such as data types, control statements, and functions.

See salaries and explore the career path for Statistician

Computational Scientist

Computational scientists use computers to solve scientific problems. Computational Scientists typically use Python to develop and implement scientific models. This course can help build a foundation for these tasks by teaching the basics of Python coding, such as data types, control statements, and functions.

See salaries and explore the career path for Computational Scientist

Business Analyst

Business analysts use data to help businesses make better decisions. Business Analysts typically use Python to perform data analysis and create visualizations. This course can help build a foundation for these tasks by teaching the basics of Python coding, such as data types, control statements, and functions.

See salaries and explore the career path for Business Analyst

Software Engineer

Software engineers design, develop, and maintain software applications. Software Engineers use Python to develop and implement software applications. This course can help build a foundation for these tasks by teaching the basics of Python coding, such as data types, control statements, and functions.

See salaries and explore the career path for Software Engineer

Data Analyst

Data analysts examine and interpret data. The tasks of a Data Analyst involve using Python to perform data cleaning and analysis, apply statistical techniques, and create visualizations. This course can help build a foundation for these tasks by teaching the basics of Python coding, such as data types, control statements, and functions.

See salaries and explore the career path for Data Analyst

Research Scientist

Research scientists conduct scientific research to advance knowledge and develop new technologies. Research Scientists typically use Python to analyze data and develop models. This course can help build a foundation for these tasks by teaching the basics of Python coding, such as data types, control statements, and functions.

See salaries and explore the career path for Research Scientist

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Practical Python for AI Coding 1.

Python Crash Course, 2nd Edition

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Comprehensive introduction to Python and programming. It covers all the basics, from data types and variables to control flow and functions. It also includes several projects that give you hands-on experience with Python.

Practical Python for AI Coding 1

Three deals to help you save

What's inside

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

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