3: [ML Tutorials #3] Four keys to create supervised learning model
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Supervised learning is a strategy in machine learning that enables a model to learn from data without being explicitly programmed. In other words, in supervised learning, the model tries to find the relationship between the "input" X and the "output" Y. Therefore, the first key to creating a supervised learning model is the dataset.
**Key 1 : Dataset**
Having a labeled dataset is essential, including two important types of information: the target variable Y, which is what we want to predict, and the explanatory variable X, which are the factors that help us make predictions. Let's take an example: imagine we want our model to predict the weather (Y) based on factors like temperature, humidity, and wind speed (X). To do this, we gather a dataset with information from the past, where we already know both the weather outcomes (Y) and the corresponding factors (X). This dataset acts like a box of puzzle pieces. Each piece represents one of the factors, and finding the relationship between these pieces defines the weather.
We can represent this relationship as a mathematical equation, like this: Y = F(X), where F represents our model. Therefore, the second key is the Model.
**Key 2: Model**
The fundamental model in supervised machine learning is a linear model expressed as y = ax + b. However, the real world often presents nonlinear problems. In such cases, we explore non-linear models, such as a polynomial of degree two like y = ax² + bx + c, or even of degree three, and beyond. It's crucial to understand that each model has parameters requiring adjustment during training. Consequently, the two remaining critical components are the cost function and the optimization algorithm.
**Key 3 : Cost Function**
In machine learning, a cost function, also called a loss or objective function, quantifies the gap between the target and predicted values, signifying the model's error. The aim is to minimize this error to craft the most effective model.
**Key 4 : Optimizer**
Optimizer forms the core of a machine learning model, representing the strategy to discover parameter values that minimize the cost function. It plays a crucial role in fine-tuning the model for optimal performance.
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4: [ML Tutorials #4] "Supervised and Unsupervised Learning in 90 Seconds of Reading"
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** Brief Definition : **
Supervised and unsupervised learning are two fundamental facets of machine learning, each specifically tailored to handle distinct types of data. In supervised learning, the machine learning algorithm is trained on a labeled dataset, where each data point consists of both input features and corresponding output labels. The goal is for the algorithm to learn the mapping from inputs to outputs based on these labeled examples. In unsupervised learning, the machine learning algorithm is trained on an unlabeled dataset to find hidden patterns, structures, or relationships within the data. Unlike supervised learning, there are no predefined output labels for the algorithm to learn from.
** Intuition 🙂 : **
In supervised learning, envision having a jigsaw puzzle featuring a picture of a dog, where each puzzle piece is labeled with its correct position in the completed picture. The model learns from these labeled examples, figuring out the relationships between the shapes and colors of the pieces and their correct locations.This process, often referred to as the training step, allows the model to internalize the patterns within the labeled data. Subsequently, after training, the model is adept at taking a new puzzle of a dog and precisely assembling it based on the knowledge acquired during the training process.
Now, imagine you have a bag of puzzle pieces without a picture or labels — just a mix of colors and shapes. In unsupervised learning, the model explores the characteristics of the puzzle pieces without any predefined labels or information about the complete picture, identifying groups that share similar colors, shapes, or patterns. The model doesn't know what the complete picture looks like, but it discovers that certain pieces belong together based on shared features. These groups represent clusters of similar puzzle pieces.
In this puzzle analogy, supervised learning entails constructing a model with labeled examples to tackle a specific task, while unsupervised learning involves the model autonomously uncovering patterns or relationships within the data without explicit direction.
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