Start with the basics and learn AI from the beginning.

Start with the basics and learn AI from the beginning!

Because Artificial Intelligence (AI) is moving forward fast, 2025 is the ideal moment to start learning! No matter if you want to switch professions, increase your current abilities, or become familiar with advanced technology, learning AI from the beginning can be done. It will instruct you through each part of the process.

In the beginning, you should figure out your motive and develop the proper mindset (about 1-2 weeks).

state the reasons you want to master AI. General interest? Which area of software development will you specialize in? A niche can be AI used in healthcare or finance? When you know your reason for quitting, you will stay motivated.

Show an open mind by accepting that AI presents new and tough challenges. There will be problems that you have to handle. Use each challenging situation as a way to learn more. Always remember to stay patient as you continue to practice.

Gain a general idea of what AI is about. What does AI, Machine Learning, Deep Learning, and Data Science represent? Check some short explanation videos about AI (such as those by Kurzgesagt).

During Phase 1, you must prepare yourself with the fundamental mathematical ideas (2-4 months).

AI relies on mathematical concepts. Please don’t miss this part of the article.

Linear Algebra deals with vectors, matrices, transformations, and also eigenvalues and eigenvectors. It is vital for knowing about data representation and algorithms.

You can look at Khan Academy, 3Blue1Brown’s channel on YouTube, or Gilbert Strang’s linear algebra lectures available online.

There are derivatives, integrals, gradients, and chain rule in calculus. It helps optimization methods such as gradient descent do their work.

Useful websites are Khan Academy and 3Blue1Brown's online Calculus overview, known as “The Essence of Calculus.”

This area involves probability distributions, Bayes’ theorem, testing hypotheses, regression, and studying both descriptive and inferential statistics. The use of words that show things are uncertain along with methods to analyze data.

I used Khan Academy and StatQuest with Josh Starmer for my resources.

Step number two: Use Python to learn to code (about two to three months).

Python is the main language of AI.

Basic Python: Using data types, looping through code, making functions, class definitions, reading or writing files.

Official tutorial from Python.org, Codecademy, and "Python Crash Course" by Eric Matthes are the good resources.

Important Libraries for Data Science & AI are:

NumPy is a tool for handling calculations and arrays.

Use pandas when you need to work on and analyze data (DataFrames).

Data visualization can be done with Matplotlib & Seaborn.

The resources I believe are the most useful are official documentation, DataCamp, and other Youtube tutorials.

In phase 3, exploring Machine Learning basics is important (3-5 months).

It is now that you start creating smart systems.

Important ideas: The differences between supervised and unsupervised learning, common evaluation criteria like accuracy, precision, and recall, avoiding problems like overfitting by cross-validating the dataset.

Supervised Learning Algorithms mean that algorithms require labeled data.

You can use Linear Regression or Logistic Regression as options.

Dealing with big data can help you solve many problems, especially if you use algorithms like Decision Trees, Random Forests, and Gradient Boosting Machines (XGBoost, LightGBM).

Support Vector Machines are a kind of learning algorithm.

K-Nearest Neighbors is shorthanded as KNN.

There are algorithms called unsupervised learning algorithms.

Group methods Known as K-Means Clustering and Hierarchical Clustering

Principal Component Analysis is a name for a statistical method.

Actively use the Scikit-learn library to do your data analysis.

Recommended resources are Andrew Ng’s “Machine Learning Specialization” course on Coursera, the book Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow, and StatQuest website.

In the fourth phase, you should look into Deep Learning (Lasting 3-5 Months)

One of the main reasons for recent advancements in AI.

Neural Networks are made up of perceptrons, different activation functions, backpropagation, and gradient descent.

There are various types of Neural Networks.

Artificial Neural Networks can also be known as Multi-Layer Perceptrons (MLPs).

When it comes to images and videos, Convolutional Neural Networks (CNNs) are used.

RNNs, LSTMs, GRUs are used for handling text and time series data sequentially.

The paper, Transformers (Attention is All You Need), has defined how modern NLP works.

Pick a framework and use it well, next try to get familiar with the other ones.

TensorFlow says Keras API, allows for smooth and scalable production.

Many researchers in the field refer to PyTorch because of its Python and flexible nature.

Use sites like DeepLearning.AI Specializations at Coursera, fast.ai courses, and the PyTorch and TensorFlow tutorials from the official sites.

The final phase is to continue focusing on a field of interest.

AI covers many areas. Choose a business area that fascinates you and is consistent with what you hope to achieve.

Natural Language Processing (NLP) is used for text analysis, sentiment analysis, machine translation, chatbots, large language models (LLMs).

Image recognition, object detection, and image segmentation are part of Computer Vision (CV).

In RL, agents are trained to act in various situations (games, using robots).

Generative AI makes new content (images, written text, and music) using models such as GANs, VAEs, and Diffusion Models.

I will talk about AI Ethics & Responsible AI in terms of bias, fairness, transparency, explainability. Anyone working in AI needs to have this skill.

Phase 6: Tools and Top Practices (Always Changing and Adapting)

Working with Git and either GitHub or GitLab allows teams to manage projects, and it is crucial for collaboration.

These IDEs/Notebooks are VS Code, Jupyter Notebooks/JupyterLab, and Google Colab.

Keep yourself informed about AWS SageMaker, Google AI Platform, or Azure Machine Learning.

You should first learn about data pipelines, both SQL and NoSQL databases, and data warehouses.

Phase 7: Keep building more buildings and designs! The process is essential and should be applied all the time.

Theory does not mean much unless you practice it.

Address problems that you find important through your personal projects. That is when the real teaching and learning take place.

By joining Kaggle Competitions, you get to practice and gain useful knowledge from others. Begin with those videos made for new users.

Help out with Open Source: It helps you learn new things and interact with others in the same field.

Highlight your coding projects by putting them on GitHub.

Phase 8: Keep Up-to-Date and Connect with Other Learners (for Life)

AI develops fast and keeps growing.

You should read ArXiv papers when looking for recent research.

Follow important names in AI and pay attention to what they write.

Consider joining (virtual meetings) / Conferences/Webinars/Meetups: (Either online or in person).

Stay in touch with others in the field through networking.

Final Thoughts:

Gaining knowledge in AI takes a lot of time and patience. Be reliable, mark your achievements, and don’t feel shy about inquiring about what’s new. Getting through this journey is not easy, yet it is very fulfilling. I wish you all the best in the year 2025.