"Machine Learning" and "Deep Learning" are two incredibly confusing terms in many's minds. The majority thinks that the above two can be used interchangeably. So, what exactly are "Machine Learning" and "Deep Learning"?
Definitions of Machine Learning (ML) and
Deep Learning (DL) Machine learning is an important branch of artificial intelligence (AI). It focuses on imitating the way human learns with the use of data and algorithms. In machine learning, machines can gradually improve their accuracy in decision making, just like humans. To clarify, Deep Learning should not be used interchangeably with Machine Learning. It is because Deep Learning does not precisely equal Machine Learning. Deep learning is a sub-field of Machine Learning. Key difference between machine learning and deep learning The key difference between deep learning and machine learning is how the algorithm learns. In classical machine learning, machines depend more on human intervention to learn. The engineers predefine the set of features for the AIs to classify data. On the other hand, in deep learning, learning can be automated. The algorithm can automatically determine the set of features to distinguish data. The algorithm accepts all sorts of data, regardless of labelled or unlabeled. It can even ingest unstructured data in raw form (e.g. images, videos, texts). The intense learning power of deep learning comes from implementing artificial neural networks, which comprises complicated layers of algorithms. The artificial neural network is inspired and designed based on the biological neural network. To add on, the "deep" in deep learning actually refers to the depth of layers in a neural network. Machine learning methods Machine learning methods can be classified into four major types: supervised, unsupervised, semi-supervised, and reinforcement. 1. Supervised learning Supervised learning uses labelled datasets to train AI models. The labelled datasets consist of inputs and outputs. The goal in supervised learning is to generate functions that can map inputs to the desired outputs. In the end, the algorithms can classify data and predict outcomes. 2. Unsupervised learning Unsupervised learning uses unlabeled datasets to train AI models. The unlabeled datasets consist of inputs only. The AI models are required to analyze and classify the clustered datasets by themselves. The ability to find hidden patterns in datasets makes unsupervised learning particularly ideal in data analysis. 3. Semi-supervised learning Semi-supervised learning is in between supervised learning and unsupervised learning. The datasets that AI models fed with are a mixture of labelled and unlabeled data. The labelled data is used to train the model with the way for classifying the data. Meanwhile, the AI models need to classify the unlabeled data by themselves. Semi-supervised learning is a solution to the lack of labelled data for training. 4. Reinforcement learning Reinforcement learning trains machines using an approach different from the above three. Machines are exposed to a controlled environment to train themselves using trial and error. Through continuous adjustments, the machines can eventually learn the best possible knowledge to make accurate decisions. References: What is Machine Learning? - Hong Kong S.A.R. of China | IBM Deep Learning vs. Machine Learning: What's the difference? (zendesk.com) Analytics Community | Analytics Discussions | Big Data Discussion (analyticsvidhya.com) Supervised and Unsupervised Machine Learning Algorithms (machinelearningmastery.com) 
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