Machine Learning (ML) is a subset of Artificial Intelligence (AI) that focuses on developing algorithms that allow computers to learn from data and make predictions or decisions without being explicitly programmed. AI, on the other hand, is a broader field that includes ML, expert systems, robotics, and rule-based automation.
In Supervised Learning, models are trained on labeled data, meaning the input data has corresponding correct outputs. The algorithm learns by mapping inputs to outputs. In Unsupervised Learning, the model is given unlabeled data and must find patterns, structures, or relationships without explicit instructions.
Answer: Some major challenges include:
Data quality issues – Incomplete, biased, or noisy data can affect model accuracy.
Overfitting – When a model learns the training data too well and performs poorly on new data.
Computational cost – Training complex models requires significant computing power.
Ethical concerns – Bias in training data can lead to unfair or discriminatory outcomes.
Answer: The most popular programming languages for ML include:
Python – The most widely used language due to libraries like TensorFlow, PyTorch, and scikit-learn.
R – Commonly used in statistical modeling and data analysis.
Java – Used in enterprise ML applications and big data processing.
Julia – Gaining popularity for high-performance ML applications.