AI educator and DeepLearning.AI founder Andrew Ng is on a roll with his new course offerings. In August alone, he launched four new courses focusing on LMMs, coding and AI application building. And the one that stood out was AI Python for Beginners, where users (beginners and developers) can learn coding with the help of an AI assistant.
Meanwhile, here are a few AI-based coding courses that offer a beginner-friendly guide to coding:
- AI Python for Beginners by DeepLearning.AI
- AI Programming with Python for Beginners on FutureLearn
- AI Programming with Python Nanodegree on Udacity
- Artificial Intelligence: Reinforcement Learning in Python on Udemy
- Python for Everybody Specialisation by the University of Michigan on Coursera
AI Python for Beginners
This course is structured into four parts and is designed for absolute beginners. It covers the fundamentals of Python programming and its application in AI. Participants will learn:
- Python Basics: Introduction to programming concepts like variables, functions, loops, and data structures.
- Real-World Applications: How to automate tasks and analyse data using Python, with practical projects such as creating custom recipe generators and smart to-do lists.
- AI Integration: The course includes interaction with AI tools to enhance learning, allowing students to debug code and understand programming concepts through an AI chatbot.
By the end of the course, learners will be able to write Python scripts that interact with large language models and automate various tasks, preparing them for more advanced AI applications.
AI Programming with Python for Beginners on FutureLearn
This course is structured into weekly modules designed for beginners with no prior programming experience. It covers the fundamentals of Python programming and its application in data analysis and visualisation. Participants will learn:
- Python Basics: Introduction to programming concepts like variables, data types, functions and control structures.
- Data Structures: Working with lists, dictionaries and files in Python.
- Data Analysis: Techniques for cleaning, manipulating and analysing data using Python libraries.
- Data Visualization: Creating informative visualisations with the help of Python’s Matplotlib library.
By the end of the course, participants will be able to write Python scripts to automate data-related tasks, analyse datasets and create visualisation of data. This foundation will prepare them for more advanced applications in AI and machine learning.
AI Programming with Python Nanodegree on Udacity
This program is divided into four main projects and is designed for beginners with no prior experience in AI. It covers the essential skills needed for AI programming using coding languages like Python, NumPy and Pandas. Participants will learn:
- Python Programming: Fundamentals of Python syntax, data structures and object-oriented programming.
- NumPy and Pandas: How to use these libraries for efficient data manipulation and analysis.
- Machine Learning Fundamentals: Introduction to supervised and unsupervised learning techniques.
- AI Applications: Building AI applications like a dog breed classifier and a TV script generator.
By completing the program, participants will have built a portfolio of AI projects and gained the necessary skills to start building their own AI applications using Python. The projects are designed to be challenging, providing hands-on experience in AI programming.
Artificial Intelligence: Reinforcement Learning in Python on Udemy
This course is divided into two sections covering various reinforcement learning algorithms and their implementation in Python. Participants are expected to have basic prior knowledge of calculus, probability and Python programming. This course will explore:
- Reinforcement Learning Fundamentals: Key concepts like Markov decision processes, value functions, and policy gradients.
- Classic Reinforcement Learning Algorithms: Q-learning, SARSA, and Monte Carlo methods.
- Advanced Algorithms: Deep Q-Networks, policy gradients, and actor-critic methods.
- Practical Implementation: Hands-on projects using Python libraries like OpenAI Gym, Keras, and TensorFlow.
By the end of the course, participants will have a deep understanding of reinforcement learning and the ability to implement 17 different algorithms using Python. The course projects cover a wide range of applications, from classic control problems to modern game-playing AI.
Python for Everybody Specialisation by the University of Michigan on Coursera
This specialisation consists of five courses and is designed for beginners with no prior programming experience. It provides a comprehensive introduction to Python programming and its applications in data analysis and visualisation. Participants will explore:
- Programming Basics: Variables, expressions, conditional execution and iteration.
- Data Structures: Lists, dictionaries and files in Python.
- Data Retrieval: Accessing data from databases and web APIs.
- Data Analysis: Techniques for cleaning, manipulating and analysing data using Python libraries like Pandas.
- Data Visualization: Creating informative visualisations with Python’s Matplotlib library.
By completing this specialisation, learners will have a strong foundation in Python programming and be prepared to apply their skills in various domains, including AI and machine learning. The capstone project allows learners to showcase their skills by building a data analysis application.
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