AI Python – AI

AI Python: A 4-Month Self-Study Course for Aspiring AI Developers

Course Description

Welcome to “AI Python,” a comprehensive 4-month (approximately 16-week) self-study course designed to equip motivated beginners and intermediate learners with the fundamental knowledge and practical skills to develop artificial intelligence applications using Python. This course will guide you from the basics of Python programming relevant to AI to advanced topics such as machine learning algorithms, deep learning architectures, natural language processing, and computer vision. Through engaging lessons, clear explanations, hands-on examples, and a culminating final project, you will build a solid foundation in AI and gain the confidence to tackle real-world AI challenges.

Primary Learning Objectives

Upon successful completion of this course, you will be able to:

1. Understand the core concepts of artificial intelligence, machine learning, and deep learning.
2. Proficiently use Python and its key libraries (NumPy, Pandas, Scikit-learn, TensorFlow/Keras, PyTorch, NLTK, OpenCV) for AI development.
3. Implement and evaluate various machine learning algorithms, including supervised and unsupervised learning techniques.
4. Design and train neural networks for deep learning tasks such as image classification and natural language understanding.
5. Apply natural language processing techniques to analyze and process text data.
6. Utilize computer vision techniques for image manipulation and object detection.
7. Develop and deploy AI models for practical applications, culminating in a comprehensive final project.

Necessary Materials

• A computer with a stable internet connection.
• Python 3 installed (Anaconda distribution recommended).
• Access to a code editor (e.g., VS Code, PyCharm, Jupyter Notebooks).
• Required Python libraries: numpy, pandas, matplotlib, seaborn, scikit-learn, tensorflow (or pytorch), keras, nltk, opencv-python, spacy.

Course Content: Weekly Lessons

The course is structured into 14 distinct weekly lessons, allowing for two weeks for the final project and one week for review/catch-up.

Week 1 & 2: Foundations of Python for AI

Lesson 1: Python Crash Course for AI

• Learning Objectives:
  • Understand essential Python syntax and data structures (lists, dictionaries, tuples, sets).
  • Learn about control flow (if/else, for loops, while loops) and functions.
  • Become familiar with object-oriented programming (OOP) concepts in Python.
• Key Vocabulary:
  • Variable: A named storage location in memory.
  • Data Type: Classification of data (e.g., integer, string, float).
  • List: An ordered, mutable collection of items.
  • Dictionary: An unordered, mutable collection of key-value pairs.
  • Function: A block of organized, reusable code.
  • Class: A blueprint for creating objects.
  • Object: An instance of a class.
• Content:Python is the language of choice for AI due to its simplicity, extensive libraries, and large community support. This lesson will quickly review the Python fundamentals necessary to embark on your AI journey. We’ll cover basic syntax, how to work with common data structures like lists and dictionaries, control flow statements for decision-making and iteration, and the basics of defining and using functions. We’ll also touch upon Object-Oriented Programming (OOP) concepts, which are crucial for understanding many AI libraries and building modular code.
• Practical Hands-on Example:Write a Python program that:
  1. Creates a list of numbers.
  2. Uses a for loop to iterate through the list and print whether each number is even or odd.
  3. Defines a function that takes two numbers as input and returns their sum.
  4. Creates a simple class Car with attributes like make, model, and year, and a method to display car information.

Lesson 2: Introduction to NumPy for Numerical Computing

• Learning Objectives:
  • Understand the purpose and advantages of NumPy arrays.
  • Learn how to create and manipulate NumPy arrays.
  • Perform basic array operations and linear algebra.
• Key Vocabulary:
  • NumPy: A fundamental library for numerical computing in Python.
  • ndarray: N-dimensional array object, the core of NumPy.
  • Vectorization: Performing operations on entire arrays at once, rather than element by element.
  • Broadcasting: NumPy’s ability to operate on arrays of different shapes.
• Content:NumPy is the backbone of scientific computing in Python, providing powerful N-dimensional array objects and tools for integrating C/C++ and Fortran code. It’s essential for AI because it allows for efficient numerical operations on large datasets, which are common in machine learning. We will explore how to create NumPy arrays, access and modify elements, and perform common array operations like addition, multiplication, and dot products. We’ll also discuss the benefits of vectorization and broadcasting for performance.
• Practical Hands-on Example:
  1. Create a 2D NumPy array (matrix) of shape (3, 3) with random integers.
  2. Perform element-wise addition and multiplication with another 3×3 array.
  3. Calculate the dot product of two 1D NumPy arrays (vectors).
  4. Calculate the mean and standard deviation of a NumPy array.

Week 3 & 4: Data Handling and Visualization

Lesson 3: Data Manipulation with Pandas

• Learning Objectives:
  • Understand the DataFrame and Series objects in Pandas.
  • Learn to load and save data from various file formats (CSV, Excel).
  • Perform data cleaning, filtering, and aggregation.
• Key Vocabulary:
  • Pandas: A powerful data manipulation and analysis library.
  • DataFrame: A 2-dimensional labeled data structure with columns of potentially different types.
  • Series: A 1-dimensional labeled array.
  • CSV: Comma Separated Values, a common file format for tabular data.
  • Data Cleaning: The process of preparing data for analysis by handling missing values, duplicates, etc.
• Content:Pandas is an indispensable library for data manipulation and analysis in Python. It provides data structures like DataFrames, which are ideal for tabular data, and Series, for single columns. This lesson will cover how to load data from common formats like CSV, perform essential data cleaning tasks such as handling missing values, and filter and group data for analysis. These skills are crucial for preparing your datasets before feeding them into AI models.
• Practical Hands-on Example:
  1. Load a provided CSV file (e.g., a dataset of house prices).
  2. Identify and handle any missing values (e.g., fill with mean or median).
  3. Filter the DataFrame to show only houses above a certain price.
  4. Group the data by a categorical column (e.g., number of bedrooms) and calculate the average price for each group.

Lesson 4: Data Visualization with Matplotlib and Seaborn

• Learning Objectives:
  • Create various types of plots using Matplotlib (line, scatter, bar, histogram).
  • Enhance visualizations using Seaborn for statistical graphics.
  • Interpret insights from visual data representations.
• Key Vocabulary:
  • Matplotlib: A comprehensive library for creating static, animated, and interactive visualizations in Python.
  • Seaborn: A high-level data visualization library based on Matplotlib that provides a more aesthetic and statistical plots.
  • Histogram: A graphical representation of the distribution of numerical data.
  • Scatter Plot: A plot displaying values for two variables as points.
  • Box Plot: A method for graphically depicting groups of numerical data through their quartiles.
• Content:Data visualization is vital for understanding your data, identifying patterns, and communicating insights. Matplotlib is the foundational plotting library in Python, offering extensive customization. Seaborn builds on Matplotlib to provide a higher-level interface for drawing attractive and informative statistical graphics. This lesson will teach you how to create various plots, including histograms, scatter plots, and box plots, and how to use these visualizations to gain insights into your data.
• Practical Hands-on Example:
  1. Using the dataset from Lesson 3, create a histogram of house prices.
  2. Generate a scatter plot showing the relationship between house size and price.
  3. Create a box plot of house prices grouped by the number of bathrooms using Seaborn.
  4. Add appropriate titles and labels to your plots.

Week 5 & 6