🧠 AI Concepts in a Nutshell: Machine Learning

Machine Learning is as of today the AI sub-field that is the most widely implemented in production workloads. It is not what makes all the buzz these days, but that’s why I wanted to start with it.

Ever wondered how machines can predict trends, classify images, or make decisions in complex environments?

🤖 What is Machine Learning? ML is a subset of artificial intelligence that enables computers to learn from data without being explicitly programmed. It’s all about making predictions, drawing insights, and identifying patterns to make better decisions.*

There are 3 types of Machine Learning:

1️⃣ Supervised Learning: In supervised learning, we train a model using labeled data, meaning that the target variable (what we want to predict) is known. The model learns from the features (different pieces of information) and labels in the training data to make predictions on new, unseen data.
Example: Predicting heart disease in patients based on factors like age, cholesterol, and smoking habits.

2️⃣ Unsupervised Learning: Unsupervised learning works with unlabeled data, meaning there are no target variables. The model finds patterns and relationships in the data on its own, which is perfect for tasks like anomaly detection and clustering.
Example: Grouping heart disease patients into categories based on feature similarity to research better treatments.

3️⃣ And the least common – Reinforcement Learning: Reinforcement learning is about training models to make sequential decisions, like a robot navigating a path or deciding its next move in a game. The model learns by interacting with its environment and receiving feedback in the form of rewards or penalties. Example: Teaching a computer to play chess by learning from its past moves and their outcomes.

ML Workflow: the main steps

Let’s dive into the four essential steps of the machine learning workflow.
Our Scenario:
Imagine using Berlin apartment sales data to predict future sale prices. With labeled data on square footage, neighborhood, year built, and sale price, this becomes a supervised learning problem.

📊 Step 1 – Extract Features: Begin by reformatting the dataset and selecting relevant features. In our case, we might consider square feet, neighborhood, and distance to the nearest subway station.

⚖️ Step 2 – Split Dataset: Divide the dataset into two parts: training and testing. This separation ensures an accurate evaluation of the model’s performance.

📈 Step 3 – Train Model: Choose a suitable machine learning model and train it using the training dataset. Models range from simple logistic regression to complex neural networks.

🎯 Step 4 – Evaluate: Assess the model’s performance using the test dataset, also known as “unseen data.” Calculate the average error or the percentage of accurate predictions within a certain margin to determine the model’s effectiveness.

If the model meets your performance threshold, it’s ready for use! If not, return to step 3 and fine-tune the model by adjusting its parameters or features.

Deep Learning

Deep learning is a powerful subset of machine learning and takes inspiration from the human brain’s neural networks to solve complex problems!

🔍 What is Deep Learning? Deep learning uses artificial neural networks to learn hierarchical representations of data, enabling AI to process large, unstructured datasets and tackle intricate tasks like computer vision and natural language processing.

🎥 Predicting Box Office Revenue: A Deep Learning Example Imagine using deep learning to predict a movie’s box office revenue based on factors like production budget, advertising, star power, and release timing. A neural network can automatically discover and map relationships between these variables to generate accurate predictions.

🌟 How Does It Work? Neural networks consist of interconnected neurons (or nodes) that process information in layers. In our box office example, neurons might analyze spend, awareness, and distribution to predict revenue. Deep learning networks can have thousands of neurons, enabling them to compute incredibly complex functions and uncover hidden patterns.

When to Choose Deep Learning: Deep learning is ideal for large datasets and complex problems, but it requires powerful computers and more data for training compared to traditional machine learning. When domain knowledge is lacking or the task involves unstructured data, deep learning excels, as neural networks can automatically discover essential features and relationships.

Deep Learning & Computer Vision 📷

Have you ever wondered how self-driving cars navigate or how facial recognition works? Computer vision is the key, and here is a first high-level overview of its core concepts.It enables computers to see and understand the content of digital images, powering applications like self-driving cars, automatic tumor detection, and more.

🎨 Image Data: Images are made up of pixels containing color and intensity information. So, digital images can actually be seen as a bunch of numbers. These numbers can be used as features for your machine learning model.

🧔 Face Recognition: To build a face recognition system, we input images and use a neural network to identify individuals based on pixel intensities. Neurons in the network learn to detect edges, parts of objects, and eventually whole faces, combining this information to output the person’s identity.

🔧 Training the Neural Network: The magic of neural networks lies in their ability to learn from data without explicit programming. By providing images of faces and their corresponding labels, the learning algorithm figures out what each neuron should compute during training.

📱 Applications: Computer vision powers various applications, including facial recognition, self-driving vehicles, automatic tumor detection in CT scans, and even the creation of realistic images, like deepfakes.