Humans acquire knowledge through experience either directly or shared by others. Machines acquire knowledge through experience shared in the form of past data. We have the terms, Knowledge, Skill, and Memory being used to define intelligence. Just because you have good memory, that does not mean you are intelligent.

Machine learning is artificial intelligence. Yet artificial intelligence is not machine learning. This is because machine learning is a subset of artificial intelligence. In addition to machine learning, artificial intelligence comprises such fields as computer vision, robotics, and expert systems.

ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method. The main focus of ML is to allow computer systems learn from experience without being explicitly programmed or human intervention. Ans : D. Explanation: All statement are true about Machine Learning.

While machine learning is based on the idea that machines should be able to learn and adapt through experience, AI refers to a broader idea where machines can execute tasks “smartly.” Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems.

Check out Great Learning’s PG program in AI & ML to upskill in the domain….Difference between AI and Machine Learning.

We have compiled some ideas and basic concepts of Machine Learning to help in its understanding for those who have just landed in this exciting world.

• Supervised and unsupervised machine learning.
• Classification and regression.
• Data mining.
• Learning, training.
• Dataset.
• Instance, sample, record.

Machine Learning Defined Its goal and usage is to build new and/or leverage existing algorithms to learn from data, in order to build generalizable models that give accurate predictions, or to find patterns, particularly with new and unseen similar data./span>

Applications of Machine learning

1. Image Recognition: Image recognition is one of the most common applications of machine learning.
2. Speech Recognition.
3. Traffic prediction:
4. Product recommendations:
5. Self-driving cars:
6. Email Spam and Malware Filtering:
7. Virtual Personal Assistant:
8. Online Fraud Detection:

Here are six real-life examples of how machine learning is being used.

1. Image recognition. Image recognition is a well-known and widespread example of machine learning in the real world.
2. Speech recognition.
3. Medical diagnosis.
4. Statistical arbitrage.
5. Predictive analytics.
6. Extraction.

Why is it Important?

• Learning gives the algorithm experience which can be used to output the predictions for new unseen data.
• Experience also helps in optimizing the performance of the algorithm.
• Real-world computations can also be taken care of by the Supervised Learning algorithms.

There are two main types of supervised learning problems: they are classification that involves predicting a class label and regression that involves predicting a numerical value. Classification: Supervised learning problem that involves predicting a class label./span>

Another great example of supervised learning is text classification problems. In this set of problems, the goal is to predict the class label of a given piece of text. One particularly popular topic in text classification is to predict the sentiment of a piece of text, like a tweet or a product review./span>

Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.

Different Types of Supervised Learning

• Regression. In regression, a single output value is produced using training data.
• Classification. It involves grouping the data into classes.
• Naive Bayesian Model.
• Random Forest Model.
• Neural Networks.
• Support Vector Machines.

two types

The two most common supervised tasks are regression and classification. Common unsupervised tasks include clustering, visualization, dimensionality reduction, and association rule learning.

Supervised learning is the technique of accomplishing a task by providing training, input and output patterns to the systems whereas unsupervised learning is a self-learning technique in which system has to discover the features of the input population by its own and no prior set of categories are used.

Datasets: A collection of instances is a dataset and when working with machine learning methods we typically need a few datasets for different purposes. Training Dataset: A dataset that we feed into our machine learning algorithm to train our model. It may be called the validation dataset./span>

Unsupervised learning is a machine learning technique, where you do not need to supervise the model. Unsupervised machine learning helps you to finds all kind of unknown patterns in data. Clustering and Association are two types of Unsupervised learning./span>

K-means is a clustering algorithm that tries to partition a set of points into K sets (clusters) such that the points in each cluster tend to be near each other. It is unsupervised because the points have no external classification.

A neural net is said to learn supervised, if the desired output is already known. While learning, one of the input patterns is given to the net’s input layer. Neural nets that learn unsupervised have no such target outputs. It can’t be determined what the result of the learning process will look like.