1: [ML Tutorials #1] Grasping the concept of machine learning in just 90 seconds of reading
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Machine learning is a branch of the artificial intelligence domain that encompasses various methods relying on learning from data to solve problems such as prediction, classification, dimensionality reduction, etc. Learning from the data means that machine learning systems can analyze patterns, extract insights, and make informed decisions without being explicitly programmed for a particular task. Instead of adhering to predetermined rules, machine learning methods adapt and improve their performance over time. The process involves training models, validating their accuracy, and testing their generalization to new, unseen data.
Intuitively, we can envision the machine learning model as a student in a classroom. The teacher imparts knowledge to the student during what we refer to as the training step for the machine learning model. After the session, the student undergoes a quiz to solidify the concepts, representing the validation step for the machine learning model. Finally, the student takes a comprehensive final exam to test their understanding of the entire course. All of these stages occur gradually over what is termed as epochs in the context of a machine learning model.
In this analogy, each epoch corresponds to a complete cycle of the training, validation, and testing phases. It's like the student attending multiple class sessions, quizzes, and exams to reinforce and assess their knowledge. With each successive epoch, the machine learning model refines its understanding of the data, enhancing its ability to make accurate predictions or classifications in real-world applications. Just as a student becomes more adept through repeated study sessions, the machine learning model becomes increasingly proficient with each pass through the data.
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[ML Tutorials #1] Grasping the concept of machine learning in just 90 seconds of reading
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2: [ML Tutorials #2] "Understanding Overfitting and Underfitting in a Quick 90-Second Read"
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Overfitting and underfitting represent two common issues in machine learning that affect the performance of a model. In the context of overfitting, the model learns the training data too precisely, capturing noise and fluctuations that are specific to the training set but do not generalize well to new, unseen data. Underfitting, on the other hand, occurs when a model is enabled to capture the underlying patterns in the training data, resulting in poor performance not only on the training set but also on new, unseen data. It indicates a failure to learn the complexities of the data.
**Analogy : **
Intuitively, returning to the example of the student that we presented in the definition of the machine learning concept, we discussed the possibility of considering a machine learning model as a student in a class. After the lecture phase, equivalent to the training step for the model, the student takes an exam or quiz to confirm their understanding of the course material. Now, imagine a student who failed to comprehend anything during the course and did not prepare. On the exam day, this student, having failed to grasp the content, will struggle to answer and will receive a low grade; this represents the case of underfitting in machine learning. On the other hand, let's consider another student who, despite having a limited understanding of the course, mechanically memorized the content and exercises. During the exam, when faced with questions reformulated or presented in a new manner, this student, having learned without true comprehension, will also fail due to the inability to adapt, illustrating the case of overfitting in machine learning.
This analogy between a machine learning model and a student highlights the insightful parallels of underfitting and overfitting. Just as a student can fail by not grasping the course or memorizing without true understanding, a model can suffer from underfitting if it's too simple to capture patterns or overfitting if it memorizes the training data too precisely. Striking the right balance between complexity and generalization is crucial for developing effective machine learning models adaptable to diverse and unknown data. In essence, this educational analogy emphasizes the delicate equilibrium required in the machine learning learning process.
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[ML Tutorials #2] "Understanding Overfitting and Underfitting in a Quick 90-Second Read"
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https://bluwr.com/p/1562527
