3.2 Python Basics for AI

Grokking Modern AI Fundamentals

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3.2 Python Basics for AI

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Having a solid grip on Python is a game-changer for beginners in AI.

Python’s syntax is relatively friendly, and it has a vast ecosystem of libraries that make data manipulation, visualization, and machine learning much easier.

In this section, we’ll walk through:

Installing Python and Essential Libraries
Simple Data Manipulation (loading CSV files, cleaning data)
An Overview of Popular AI/ML Frameworks (TensorFlow, PyTorch, scikit-learn)

Let's start with the first point.

1. Installing Python and Essential Libraries

Getting Started:

Download & Install: To download, visit python.org and grab the latest stable version (3.x).
Check the Installation: Open a terminal (or command prompt) and type python --version to verify.

Must-Have Libraries:
1. NumPy:
  Fundamental for handling arrays, vectors, matrices—the building blocks of most AI tasks.
  
  Example usage: importing
```
import numpy as np  
```
  then creating arrays with
```
np.array([1,2,3])  
```
2. Pandas:
  - Ideal for data manipulation and analysis. Think of its DataFrame as an advanced spreadsheet in code form.
  - Lets you load CSVs, Excel files, or SQL database queries seamlessly.
3. matplotlib (and sometimes seaborn):
  - Essential for plotting charts and visualizing data distributions.
  - Quick plots help you spot trends, outliers, and data imbalances.
Tip: You can install libraries in bulk using a virtual environment or a package manager like pip:
```
pip install numpy pandas matplotlib
```

2. Simple Data Manipulation

Once Python and its libraries are in place, you’re ready to work with real data.

Loading a CSV File:

You can load a CSV into a Pandas DataFrame:

Python3

. . . .

data.head() shows the first few rows, giving you a quick snapshot.

a. Basic Data Cleaning

Check for Missing Values:

data.isnull().sum()

This tells you how many NaN (Not a Number) entries each column has.

Fill or Drop Missing Values:

Python3

. . . .

Converting Data Types:
If a column is recognized as text but should be numerical, you can do something like:

data['ColumnB'] = pd.to_numeric(data['ColumnB'], errors='coerce')

b. Data Exploration

Describe Your Data:

data.describe()

Gives count, mean, min, max, etc. for each numeric column.

Quick Plot:

Python3

. . . .

This displays a histogram of ColumnA to see how values are distributed.

Cleaning and exploring data is crucial.

Even the best AI model fails if the input data is messy or mislabeled.

By mastering these basics, you’ll avoid many common pitfalls later on.

3. Overview of Popular AI/ML Frameworks

Python’s strength in AI and ML owes a lot to powerful, open-source frameworks that simplify everything from linear regression to complex deep learning models.

scikit-learn
- Focus: Traditional machine learning (classification, regression, clustering).
- Why Use It: It’s beginner-friendly, with well-documented APIs and excellent tools for data splitting, model evaluation, and pipeline creation.
- Examples of Algorithms: Logistic Regression, Decision Trees, Random Forests, Support Vector Machines.
TensorFlow
- Focus: Deep learning; created by Google.
- Why Use It: Good for building neural networks, from simple feedforward layers to advanced convolutional networks for image tasks or recurrent networks for text.
- High-Level APIS: Keras (a wrapper around TensorFlow) makes it easier to build and train models without dealing with low-level operations.
PyTorch
- Focus: Deep learning; created by Facebook’s AI Research lab.
- Why Use It: Many find it more Pythonic and intuitive for quick experimentation. It is popular among researchers and in academic settings.
- Dynamic Computation Graph: Offers flexible architectures for cutting-edge model designs.

Which One to Choose?

scikit-learn: Start here if you’re dealing with simpler data science tasks or classic ML.
TensorFlow / Keras or PyTorch: Go for these when you need deep neural networks, large-scale projects, or GPU acceleration. They handle more complex architecture definitions and training pipelines.

.....

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