Today, I am starting to document my journey of learning to become an AI Engineer.

Before this, I mostly knew AI through tools like ChatGPT, chatbots, image generation tools, coding assistants, and search tools. However, I realized that simply using AI tools is not enough. I want to understand how AI works behind the scenes, how Machine Learning is different from Deep Learning, what Generative AI means, and why Python is so important in this field.

That is why I decided to start this series as a learning journal.

The goal is not to write like an expert from the beginning. Instead, I want to record what I learn, how I understand each concept, what mistakes I make, and what small projects I build along the way.

My Goal for Week 1

In the first week, I will focus on two main areas:

1. Understanding the big picture of AI

2. Learning beginner Python for future Data, Machine Learning, and Deep Learning topics

At this stage, I do not need to study complex algorithms or advanced models yet. The most important thing is to build a clear foundation and become comfortable with basic Python programming.

Part 1: Understanding the AI Landscape

Before jumping into code or Machine Learning, I think it is important to understand the bigger picture first.

At the moment, this is how I understand the main AI concepts.

What is AI?

AI, or Artificial Intelligence, is a field that focuses on creating systems that can perform tasks that usually require human intelligence.

For example, AI systems can be used for:

• Answering questions

• Recognizing images

• Recommending products

• Translating languages

• Analyzing data

• Generating text, images, or code

AI is a very broad field. Inside AI, there are smaller areas such as Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, and Generative AI.

What is Machine Learning?

Machine Learning is a branch of AI where machines learn patterns from data instead of being manually programmed with fixed rules.

For example, instead of writing many rules like this:

if email_has_discount_word and email_has_many_links:
    return "spam"

We can give a machine learning model many emails that are already labeled as spam or not spam. The model then learns patterns from those examples and uses them to predict whether a new email is spam.

My simple understanding is:

Machine Learning is a way for machines to learn patterns from data.

What is Deep Learning?

Deep Learning is a smaller branch of Machine Learning. It uses neural networks with many layers to learn from data.

Deep Learning is commonly used in areas such as:

• Image recognition

• Natural language processing

• Speech recognition

• Large language models

• Self-driving cars

My current understanding is that Deep Learning is powerful for large and complex datasets, but it usually requires more data and computing resources compared to traditional Machine Learning models.

What is NLP?

NLP stands for Natural Language Processing. It is a field that helps computers understand and process human language.

Examples of NLP applications include:

• Chatbots

• Machine translation

• Text summarization

• Sentiment analysis

• Smart search

• Question answering over documents

I think NLP is especially important because it is closely related to chatbots, search engines, RAG systems, and many modern Generative AI applications.

What is Generative AI?

Generative AI refers to AI systems that can create new content.

For example, Generative AI can create:

• Text

• Images

• Code

• Audio

• Summaries

• Emails

• Ideas

ChatGPT is a familiar example of Generative AI.

What I find interesting is that Generative AI does not only classify or predict something. It can generate new outputs based on prompts, context, or user instructions.

What is Agentic AI?

Agentic AI is a type of AI that can perform tasks through multiple steps. It can plan, use tools, make decisions, and adjust its actions to reach a goal.

For example, instead of only asking an AI system:

“Summarize this document.”

An AI agent could do multiple steps:

1. Read the document

2. Find important information

3. Summarize the content

4. Check for missing details

5. Create a report

6. Return the final result to the user

For Week 1, I only need to understand Agentic AI at a high level. I do not need to build agents yet.

Part 2: Why Python is Important

Python is widely used in AI because it is easy to read, beginner-friendly, and has a large ecosystem of libraries.

In the future, I will need to learn libraries such as:

• NumPy

• pandas

• Matplotlib

• scikit-learn

• TensorFlow

• PyTorch

• FastAPI

• LangChain

However, before learning those libraries, I need to understand the basics of Python first.

Python Basics I Need to Learn in Week 1

This week, I will focus on the following Python topics:

• Variables

• Numbers

• Strings

• Lists

• Dictionaries

• Sets

• Tuples

• If/else statements

• For loops

• While loops

• Functions

• Lambda functions

• Modules and pip install

• Reading and writing files

• Exception handling

• Classes and objects

My goal is not to master the entire Python language in one week. Instead, I want to learn enough Python to write simple scripts, solve basic problems, and prepare for future topics like data analysis and machine learning.

Simple Python Examples

One simple example is calculating the average score of a student:

scores = [8, 7.5, 9, 6.5]

average = sum(scores) / len(scores)

print("Average score:", average)

This small example helps me practice:

• Lists

• The sum() function

• The len() function

• Variables

• Basic arithmetic

• Printing output

Another useful example is counting how many times each word appears in a text:

text = "ai is changing the world and ai is useful"

words = text.split()

word_count = {}

for word in words:
    if word in word_count:
        word_count[word] += 1
    else:
        word_count[word] = 1

print(word_count)

From this example, I can practice:

• Strings

• The split() method

• Dictionaries

• For loops

• If/else statements

I think this is a very useful exercise for AI and NLP because text processing is an important foundation for many language-related applications.

My Mini Project for Week 1

The first mini project I want to build is:

Text File Analyzer

The idea is simple.

The program will read a .txt file and analyze:

• Number of lines

• Number of words

• Number of characters

• Word frequency

• Top most common words

This is a small project, but it combines many important Python basics:

• Reading files

• Processing strings

• Using dictionaries

• Writing functions

• Handling errors when a file does not exist

A simple project structure could look like this:

text-file-analyzer/
  main.py
  sample.txt
  README.md

I also plan to upload this project to GitHub so I can practice version control and writing a clear README file.

How I Plan to Connect GitHub, Hashnode, and LinkedIn

I want to learn with clear outputs, not just watch videos and forget everything later.

For each small project, I want to follow this process:

Learn a topic
→ Build a small exercise or mini project
→ Upload the code to GitHub
→ Write an explanation on Hashnode
→ Share a short update on LinkedIn

Each platform has a different purpose:

• GitHub shows my code

• Hashnode shows how I understand and explain what I built

• LinkedIn helps me share my learning progress publicly

My goal is not to look professional immediately. My goal is to build the habit of learning, documenting, and improving consistently.

Week 1 Checklist

By the end of this week, I want to be able to:

• Explain AI, Machine Learning, Deep Learning, NLP, Generative AI, and Agentic AI at a basic level

• Write simple Python programs

• Use lists, dictionaries, sets, and tuples

• Write basic functions

• Read and write files

• Handle simple errors using try/except

• Understand classes and objects at a beginner level

• Complete at least one small Python mini project

• Upload my code to GitHub

• Write a short Week 1 recap

What I Realized After Starting

The most important thing I realized is that I should not rush into Machine Learning or Deep Learning before building a strong foundation.

AI is a large field. Without a clear roadmap, it is easy to feel lost. That is why, at the beginning, I want to focus on the basics first:

• Understand the core concepts

• Practice beginner Python

• Build small exercises

• Write notes in my own words

• Slowly create small projects

Conclusion

This is the first post in my AI Engineer learning journey.

I am still at the very beginning, so this post is not meant to be a deep technical article. It is more like a personal learning note to record how I started.

In the next post, I will write about what I learned after finishing Week 1, including my Python exercises and my first mini project.

Hopefully, when I look back at this post in the future, it will remind me that the most important thing is not to start perfectly, but to start clearly and keep going consistently.