Today, the amount of data produced in the world is enormous. Not only do humans generate this data, but so do smartphones, computers, and other electronic devices. Based on the type of data available and the motive, a programmer will undoubtedly choose how to train an algorithm using a specific learning model. Machine Learning is a branch of computer science in which a system's efficiency improves by repeating tasks with data rather than explicitly programming them by programmers. Let us now distinguish between the three Machine Learning techniques: supervised learning, unsupervised learning, and reinforcement learning.

Moreover, all the tutorial will be done using Tensorflow and Keras, you can see how to install tensorflow and keras here. And i will share my all machine learning algorithms that i have done to practice code in here. In each section of the machine learning tutorial that i will condacted, i also learn from scikit-learn: Machine Learning in Python. See the figure below:

Supervised Learning

Consider yourself a student sitting in a classroom where your teacher is overseeing you, "how you can solve the problem" or "if you are doing it right or not." Similarly, with Supervised Learning, input is presented as a labelled dataset, and a model may readily learn from it to deliver the issue outcome. Supervised Learning deals with two types of problem- classification problems and regression problems.

Classification Problem

This approach improves in the prediction of a discrete value. It is possible to consider the input data as a member of a specific class or group. Taking the fruit dataset as an example, each shot has been labeled as a mango, an apple, and so on. In this case, the algorithm must assign the new photos to one of these categories. For instances: Naive Bayes Classifier, Decision Trees, Support Vector Machines, etc.

Regression problems

For continuous data, these issues are applied. Predicting the price of a piece of land in a city, for example, given the acreage, location, number of rooms, and so on. The input is subsequently given to the computer, which uses past examples to calculate the price of the land. Examples: Linear Regression, Support Vector Regression Decision Tree Regression, etc.

Unsupervised Learning

This algorithm is diametrically opposed to Supervised Learning. In unsupervised learning, there is no such thing as a comprehensive and clean labelled dataset. Self-organization is a type of unsupervised learning. Its primary goal is to investigate the underlying patterns and anticipate the outcome. In this case, we just provide the machine data and ask it to hunt for hidden characteristics and cluster the data in a logical fashion. Example: K-means Clustering, Mean-shift Clustering, DBSCAN Clustering

Reinforcement Learning

It is not based on either supervised or uncontrolled learning. Furthermore, in this case, the algorithms learn to react to their surroundings on their own. It is quickly expanding and creating a wide range of learning algorithms. These algorithms are useful in robotics, gaming, and other fields. There is always a start state and an end state for a learning agent. However, there may be an alternative path to the final state. An agent attempts to affect the environment in the Reinforcement Learning Problem. The agent moves from one state to the next. The agent receives a reward (appreciation) for success but receives no reward or appreciation for failure. In this approach, the agent learns from its surroundings.

Key Differences Between Supervised vs Unsupervised Learning vs Reinforcement Learning

1. Regression and classification are the two fundamental goals of Supervised Learning. Unsupervised Learning is used to solve clustering and associative rule mining issues. Whereas Reinforcement Learning is concerned with exploitation or exploration, Markov's decision processes, Policy Learning, Deep Learning, and value learning are concerned with learning.

2. Supervised Learning works with labeled data, and the algorithm is aware of the output data patterns. Unsupervised learning, on the other hand, works with unlabeled data and produces results based on a collection of impressions. In contrast, in the Markov Decision Process of Reinforcement Learning, the agent interacts with the environment in discrete stages.

3. Supervised Learning, as the name implies, is heavily supervised. Unsupervised Learning, on the other hand, is not supervised. In contrast, Reinforcement Learning is less supervised and relies on the agent to determine the output.

4. In Supervised Learning, the input data is labeled data. Unsupervised Learning, on the other hand, uses unlabeled data. In Reinforcement Learning, the data is not preset.

5. Supervised Learning makes predictions depending on the kind of class. Unsupervised Learning identifies hidden patterns. The learning agent also serves as a reward and action mechanism in Reinforcement Learning.

6. Labeled data is mapped to known output using supervised learning. Unsupervised Learning, on the other hand, looks for patterns and predicts the outcome. Reinforcement Learning is based on trial and error.

7. To summarize, the purpose of Supervised Learning is to build formulas based on input and output data. We identify a relationship between input values and group them in Unsupervised Learning. In Reinforcement Learning, an agent learns by interacting with the environment and receiving delayed feedback.

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