Learn Python and AI: A 12-Week Beginner’s Roadmap

📌 Phase 1: Foundation Building (Weeks 1-4)

Goal: Learn programming basics + AI fundamentals.

---

1. Python Basics (Weeks 1-2)

Why? Python is the #1 language for AI/ML.

Day 1-3: Variables & Data Types

• Variable: A container for storing data (e.g., name = "Alice").
• Data Types:
  • int: Integer (e.g., 10).
  • float: Decimal number (e.g., 3.14).
  • str: Text (e.g., "Hello, World!").

Day 4-7: Control Flow

python

Copy

# If-else statement
if temperature > 30:
    print("It's hot!")
else:
    print("It's cool!")

# For loop
for i in range(3):  # Prints 0, 1, 2
    print(i)

Day 8-10: Functions & Classes

• Function: Reusable code block (e.g., a recipe).

python

Copy

def multiply(a, b):
    return a * b

Day 11-14: Libraries & File Handling

• Install packages: pip install numpy (for math operations).
• Read/write files:

python

Copy

with open("data.txt", "r") as file:
    content = file.read()

---

2. Setup Development Environment (3 Days)

Tools You Need:

1. VSCode: A code editor (like a supercharged Notepad).
   • Install extensions: Python, GitLens.
2. Git: Version control (like a "time machine" for your code).
   • Basic commands:

 

1. Virtual Environment: Isolate project dependencies.
   • Create: python -m venv myenv
   • Activate: source myenv/bin/activate (Linux/Mac) or myenv\Scripts\activate (Windows).

---

3. AI Core Concepts (Week 4)

Key Terms:

• Machine Learning (ML): Teaching computers to learn patterns from data (e.g., spam detection).
• Deep Learning (DL): Advanced ML using neural networks (e.g., facial recognition).

Neural Network Basics

mermaid

Copy

graph LR
A[Input Layer] --> B[Hidden Layer] --> C[Output Layer]

• Input Layer: Receives data (e.g., pixels of an image).
• Hidden Layer: Processes data (like a brain’s neurons).
• Output Layer: Produces results (e.g., "cat" or "dog").

Training Process

1. Feed data → Model makes predictions.
2. Compare predictions with actual answers → Calculate loss (error).
3. Adjust model using backpropagation (fixing mistakes).

---

📌 Phase 2: Skill Development (Weeks 5-8)

Goal: Master tools to build AI models.

---

1. PyTorch Basics (Weeks 5-6)

What is a Tensor?

• A multi-dimensional array (e.g., images are 3D tensors: height × width × color channels).

python

Copy

import torch
tensor = torch.tensor([[1, 2], [3, 4]])  # 2x2 matrix

Build a Simple Model

python

Copy

# Linear regression model
model = torch.nn.Linear(1, 1)  # 1 input → 1 output
criterion = torch.nn.MSELoss()  # Loss function
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)  # Learning rate

---

2. Explore DeepSeek (Weeks 7-8)

How to Start:

1. Clone the repository:

Bash

 

git clone https://github.com/deepseek-ai/DeepSeek.git

  2. Run example code:

Python

 

python examples/sample.py

 3. Modify configurations:

Change settings like batch_size in the config.yaml file.

 

Example Customization:

python

Copy

# Original code
learning_rate = 0.001
# Your edit
learning_rate = 0.005  # Train faster (but risk overshooting)

---

3. Collaboration Tools (Week 8)

GitHub Basics:

• Repository: Project folder (hosted online).
• Branch: Workspace for experiments (e.g., feature/image-classifier).
• Pull Request: Propose changes to the main code.

---

📌 Phase 3: Build Your Project (Weeks 9-12)

Goal: Create a working AI application.

---

1. Define Your Project (3 Days)

Template:

markdown

Copy

**Problem**: Classify movie reviews as positive/negative.  
**Data**: IMDB review dataset (50k samples).  
**Model**: Fine-tune DeepSeek-NLP.  
**Output**: "👍 Positive" or "👎 Negative".  

---

2. Develop a Prototype (Weeks 9-11)

Data Preprocessing Example:

python

Copy

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/llm-base")
inputs = tokenizer("This movie is amazing!", return_tensors="pt")  # Convert text to numbers

Train Your Model:

python

Copy

# Load pre-trained DeepSeek model
from deepseek import DeepSeekModel
model = DeepSeekModel.from_pretrained("deepseek-ai/llm-base")

# Fine-tuning loop
for epoch in range(5):
    train_model_one_epoch(model, data_loader)

---

3. Deploy Your Model (Week 12)

Simple Web Interface with Streamlit:

python

Copy

import streamlit as st

text = st.text_input("Enter your review:")
if st.button("Analyze"):
    prediction = model.predict(text)
    st.write(f"Result: {prediction}")

Deploy to Cloud (AWS Lightsail):

1. Package your code into a Docker container.
2. Upload to AWS Lightsail (one-click deployment).

---

📚 Glossary for Beginners

TermExplanation

Epoch	One full pass through the training data.
Loss	How wrong the model’s predictions are (lower = better).
API	A way for programs to communicate (e.g., ChatGPT API).

---

🚀 Pro Tips

1. Google Errors: Copy-paste error messages into Google + add "stackoverflow".
2. Start Small: Build a "Hello AI" program before complex projects.
3. Ask for Help: Join communities like Reddit’s r/learnmachinelearning.

External Authority Links

• Python Documentation: https://docs.python.org/3/
• PyTorch Documentation: https://pytorch.org/docs/stable/index.html
• GitHub Guide: https://guides.github.com/

FAQ (Google Snippet Optimization)

Q1. How long does it take to learn Python and AI?
👉 Python basics (4 weeks) + AI concepts (1 week) + PyTorch hands-on (2 weeks) + project development (4 weeks) = Total 12 weeks.

Q2. What’s the difference between machine learning and deep learning?
👉 Machine learning learns patterns from data, while deep learning uses a neural network structure inspired by the human brain.

Q3. How do I build an AI model using PyTorch?
👉 Understanding tensors → Preparing a dataset → Designing a model → Training and evaluation → Optimization.