Setting Up a Machine Learning Development Environment on Mac

Setting up a proper development environment is the first step toward machine learning proficiency. This guide covers installing Python, setting up a virtual environment, and configuring essential libraries for machine learning on a MacBook Air M4. Additionally, it includes instructions for running Jupyter Notebook and a simple linear regression experiment to verify the setup. By the end of this guide, you will have a fully functional environment to begin your machine learning projects.

Goal: Set up a Python development environment for machine learning on a MacBook Air M4 and run the first machine learning experiment in Jupyter Notebook.

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1. Installing Python and Development Tools

Install Homebrew (Mac Package Manager)

Homebrew helps manage packages easily on macOS.

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

Verify the installation:

brew --version

Install Python (Using Pyenv)

macOS comes with Python pre-installed, but managing multiple versions is easier with pyenv.

brew install pyenv
pyenv install 3.11.11
pyenv global 3.11.11

Verify the installation:

python --version

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2. Setting Up the Machine Learning Environment

Create and Activate a Virtual Environment

To keep projects isolated, use venv.

python -m venv ml_env
source ml_env/bin/activate  # For Windows: ml_env\Scripts\activate

Check if the virtual environment is activated:

which python

If the output points to ml_env/bin/python, the environment is set up correctly.

Install Essential Machine Learning Packages

Install the necessary libraries for machine learning.

pip install numpy pandas matplotlib seaborn scikit-learn jupyter

Verify the installation:

pip list

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3. Running Jupyter Notebook

After setting up the environment, launch Jupyter Notebook.

jupyter notebook

Once running, open the browser and go to http://localhost:8888/tree.

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4. First Machine Learning Experiment: Linear Regression

To verify that everything is working, implement a simple linear regression model.

Python Code:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LinearRegression

# Generate data
x = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)
y = np.array([2, 4, 6, 8, 10])

# Train a linear regression model
model = LinearRegression()
model.fit(x, y)

# Predict and visualize results
y_pred = model.predict(x)
plt.scatter(x, y, color='blue', label='Actual')
plt.plot(x, y_pred, color='red', label='Predicted')
plt.legend()
plt.show()

Output:

• Blue dots (Actual) represent real data points

• Red line (Predicted) represents the model’s predictions

• The regression model successfully fits the data

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5. Summary and Next Steps

Key Takeaways

• Installed Python 3.11.11 and set up the development environment

• Created a virtual environment and installed necessary machine learning libraries

• Ran Jupyter Notebook and executed the first machine learning model

Next Goal: Start the First Portfolio Project

• House Price Prediction Project (Using Canadian Real Estate Data)

• Collect and preprocess data

• Build and evaluate a machine learning model

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Additional Notes

Jupyter Notebook Shortcuts

• Shift + Enter → Run the current cell and move to the next

• Esc + B → Add a new cell

• Esc + M → Convert a cell to Markdown mode (for writing explanations)

This marks the first step toward becoming a machine learning engineer. I will continue learning and building portfolio projects to prepare for job applications.