Machine Learning (ML) has become one of the hottest topics in technology in recent years. It promises to automate tasks and make predictions by learning patterns in data that would be difficult or impossible for humans. But what exactly happens to my data during Machine Learning? How does it differ from classical programming? And how can you start learning and applying Machine Learning yourself? In this beginner guide, we will answer all these questions and provide you with a clear entry into the world of Machine Learning.
What Happens to My Data During Machine Learning?
Machine Learning is essentially about recognizing patterns and relationships in large amounts of data and using them for predictions or decisions. The data you feed into an ML algorithm is used to train a model. This model can then be applied to new, unknown data to make predictions or support decisions.
To achieve this, the data is used in a so-called “training process.” The model is “fed” with a large amount of sample data where the inputs and corresponding outputs are already known. The algorithm then learns to recognize the relationships between these inputs and outputs in order to make similar predictions for new input data in the future.
A simple example is the classification of emails as “spam” or “not spam.” The algorithm is trained with a variety of emails where it is already known whether they are spam or not. Based on this training data, the model learns which features are typically present in spam emails and can use this information to classify new, unknown emails.
Difference Between Machine Learning and Classical Programming
In classical programming, you give the computer explicit instructions on what to do. You write code that precisely describes what steps the computer must execute to complete a specific task. This means that you as a developer must define all the logic and rules yourself.
In contrast, Machine Learning works differently. Here you don’t give the computer the rules, but let it figure out the rules itself. You provide the algorithm with a large amount of data and leave it to discover logic by recognizing patterns and relationships in this data.
An illustrative example is the development of an image recognition program. In classical programming, you would have to explain exactly to the computer what a dog looks like: size, shape, fur pattern, etc. With Machine Learning, however, you give the algorithm thousands of images of dogs and non-dogs and let it learn for itself which features are typical for dogs.
Data Sources for Machine Learning: Where Do You Get Data?
Without data, there is no Machine Learning. For beginners, the question often arises: where do you get suitable data to start your own ML projects? If you don’t have your own data, you can access a variety of public datasets. One of the most well-known platforms for this is Kaggle.
Kaggle: A Treasure Trove for Data
Kaggle is an online community for Data Science and Machine Learning that offers not only competitions but also an extensive collection of public datasets. Whether you’re interested in predicting real estate prices, analyzing social media data, or image classification – on Kaggle you’ll find a suitable dataset for almost every use case. These datasets are freely accessible and excellent for practice and experimentation.
In addition to data, Kaggle also offers tutorials, competitions, and discussions to help you improve your skills and learn new techniques.
Train/Test/Split: The Key to Model Training
One of the most important steps in Machine Learning is splitting the data into training and test datasets. This step is often called “Train/Test/Split” and is crucial for evaluating your model’s performance.
Why is the Train/Test/Split Important?
If you only train your model with one dataset and then test it with the same data, you might mistakenly believe that your model is perfect because it makes all predictions correctly. In reality, however, the model has only learned to “memorize” this specific data without truly understanding the underlying patterns.
By splitting your data into two parts – one for training and one for testing – you can ensure that your model learns generalizable patterns that can also be applied to new, unknown data. A typical approach is to split the data into 80% training data and 20% test data.
Programming Languages, Frameworks, and Tools for Getting Started
Getting started with Machine Learning requires some basic knowledge of programming and the use of specific tools and frameworks. The absolute favorite among programming languages for Machine Learning is Python. This language is not only easy to learn but also has a huge selection of libraries specifically developed for Machine Learning.
Python: The Foundation for Machine Learning
Python is the preferred language for Machine Learning because it is easy to learn and use. It allows you to focus on the algorithms and data rather than dealing with complicated syntax or lengthy development processes.
Important Libraries and Frameworks
– scikit-learn: This is one of the most well-known and widely used libraries for Machine Learning in Python. Scikit-learn offers simple and efficient tools for data mining and data analysis, and it supports a variety of Machine Learning models, such as linear regression, decision trees, and support vector machines.
– NumPy: A fundamental library for scientific computing in Python. NumPy enables efficient operations on large multidimensional arrays and matrices, which is particularly important for Machine Learning.
– Jupyter Notebook: An interactive tool that allows you to write and execute Python code, visualize data, and take notes – all in a single document. Jupyter Notebooks are particularly useful for Machine Learning as they allow you to conduct your experiments and analyses clearly and structured.
Hardware Requirements
To get started with Machine Learning, you don’t need high-end hardware. If you’re working with small datasets, you can easily work on a normal laptop without a dedicated graphics card. However, for more complex projects and larger amounts of data, a powerful GPU is beneficial.
Next Steps: Deep Learning with PyTorch and TensorFlow
After you understand the basics of Machine Learning, you can take the next step and explore Deep Learning. Deep Learning is a subfield of Machine Learning that focuses on neural networks with many layers to solve more complex tasks.
PyTorch and TensorFlow
For Deep Learning, PyTorch and TensorFlow are the two most popular frameworks. Both offer powerful tools for training deep neural networks and are used by large companies and research institutions worldwide.
– TensorFlow: Originally developed by Google, TensorFlow is an open-source framework for machine learning that is particularly easy to use and abstracts away some elements.
– PyTorch: A framework developed by Facebook that stands out for its flexibility and ease of use. PyTorch is often used for research projects because it doesn’t abstract as much logic and can be better customized for special cases.
At innFactory, we predominantly use PyTorch, but we have also implemented smaller use cases with TensorFlow or TensorFlow Lite on end devices.
Machine Learning as a Service: Using Pre-trained Models
For many use cases, you don’t need to train your own model from scratch. There are numerous providers that offer pre-trained models via APIs. This is called “AI as a Service” and allows you to integrate Machine Learning into your applications without being an expert yourself.
An example of this are Large Language Models (LLMs) like GPT, which are accessible via APIs and can be used for various tasks such as text generation, translation, or sentiment analysis.
Conclusion – AI is Not Magic
Getting started with Machine Learning can seem overwhelming, but with the right resources and a structured approach, anyone can learn the basics. Start by collecting and analyzing data, learn the fundamentals of modeling, and build on these skills to master more complex algorithms and techniques. Use the many available tools and resources to make your learning journey as efficient and effective as possible.
With this solid foundation, you are well-equipped to take the next steps, whether through the use of Deep Learning or the integration of pre-trained models into your applications. Machine Learning is an exciting and rapidly evolving discipline that offers many opportunities – whether for solving business challenges or for personal development.