What is Machine Learning?

Machine learning is an artificial intelligence (AI) technology that gives systems the ability to learn and develop from experience automatically without being programmed specifically. The focus of machine learning is on designing computer programs that can access and use data to learn for themselves.

In order to search for trends in data and make informed choices in the future based on the examples we have, the learning process starts with observations or data, such as examples, direct experience, or guidance. The primary objective is to allow computers to learn automatically and change behavior accordingly without human involvement or assistance.

But text is treated as a series of keywords using the classic algorithms of machine learning; instead, an approach based on semantic interpretation mimics the human ability to interpret the context of a text.

Some techniques of Machine Learning

Algorithms for machine learning are also classified as supervised or unsupervised.

Supervised machine learning algorithms: Using labeled examples to predict potential incidents, you can apply what has been learned in the past to new data. The learning algorithm generates an inferred feature to make predictions about the output values starting from the analysis of a known training dataset. After adequate training, the system is able to provide goals for any new data. In order to adjust the model accordingly, the learning algorithm can also compare its output with the right, expected output and find mistakes.

In addition, where the information used for training is neither identified nor named, unsupervised machine learning algorithms are used. Unsupervised learning explores how systems can infer from unlabeled data a feature to explain a secret structure. The system does not work out the correct output, but it explores the information and can draw data set inferences to explain hidden structures from unlabeled data.

Semi-supervised machine learning algorithms:

Somewhere between supervised and unsupervised learning, they use both labeled and unlabeled information, usually a small amount of labeled information and a large amount of unlabeled information, for instruction. The systems that use this technique are able to increase the precision of learning considerably. Typically, semi-supervised learning is preferred where skilled and relevant resources are needed for the acquired labeled data to train it / learn from it. Otherwise, it typically doesn’t take extra resources to obtain unlabeled data.

Machine learning algorithms for reinforcement are a learning process.

Reinforcement machine learning algorithms: It is a method of learning that communicates with the environment by producing behavior and finding errors or rewards. The most important characteristics of reinforcement learning are trial and error search and delayed reward. In order to optimize its efficiency, this approach enables machines and software agents to automatically evaluate the optimal behavior within a particular context. For the agent to learn which behavior is better, simple reward feedback is required; this is known as the reinforcement signal.

Why it is important to learn machine learning?

It is a method of learning that communicates with the environment by producing behavior and finding errors or rewards. The most important characteristics of reinforcement learning are trial and error search and delayed reward. In order to optimize its efficiency, this approach enables machines and software agents to automatically evaluate the optimal behavior within a particular context. For the agent to learn which behavior is better, simple reward feedback is required; this is known as the reinforcement signal.

Machine  Learning vs Artificial Intelligence 

AI is a broader term for the development of intelligent machines that can mimic the potential and actions of human thought, while machine learning is an application or subset of AI that enables machines to learn from data without being specifically programmed.

 

Types of Machine Learning Algorithms

  1. Supervised Machine Learning:This algorithm consists of a target/outcome variable to be predicted from a given set of predictors (or dependent variable) (independent variables). We generate a function using these variable sets that maps inputs to desired outputs. The training process continues until a desired degree of accuracy is reached on the training data by the model. Supervised learning examples include: Regression, Decision Tree, Random Forest, KNN, Logistic Regression, etc.
  2. Unsupervised Learning: We do not have a goal or result variable to predict / estimate in this algorithm. It is used for population clustering in different communities, which is commonly used for basic interference to segment consumers into different groups. Examples of learning that is not supervised: Algorithm Apriori, K means.
  3. Reinforcement Learning:We do not have a goal or result variable to predict / estimate in this algorithm. It is used for population clustering in different communities, which is commonly used for basic interference to segment consumers into different groups. Examples of learning that is not supervised: Algorithm Apriori, Kmean.

 

Common Machine Learning Algorithms

  1. Linear Regression
  2. Logistic Regression
  3. Decison Tree
  4. SVM
  5. KNN
  6. Random Forest
  7. Gradient Boosting Algorithms
  8. GBM
  9. XGBoost

Linear Regression

It is used to predict real values based on continuous variable values (house expenses, number of calls, overall sales, etc) (s). Here, by fitting the best line, we create a relationship between independent and dependent variables. This best fit line is referred to as a line of regression and is represented by a linear equation Y= a*X+b.

The easiest way to view linear regression is to relive this childhood experience. Let’s say, you ask a fifth-grader to coordinate individuals in his class by increasing the order of weight, without asking them for their weights! What do you believe the child is going to do? He/she will possibly look at the height and build of individuals (visually analyse) and organise them using a combination of these observable parameters. This is real-life linear regression! The child has actually found out that a relationship, which looks like the above equation, will equate height and construction with weight.

In this equation:

  • Dependent Varible

a-Slope

X-Independent Varible

b-intercept

Logistic Regression

Do not get confused by the name of it! It is not a regression algorithm, but a classification. It is used to predict discrete values based on a given set of independent variable values (binary values such as 0/1, yes/no, true/false) (s). In simple words, by fitting data to a logit function, it predicts the likelihood of occurrence of an event. It is, thus, often referred to as logit regression. Since the likelihood is estimated, its performance values are between 0 and 1 because (as expected).

Don’t get confused by its name! It is a classification, not a regression algorithm. Based on a given set of independent variable values (binary values like 0/1, yes/no, true/false), it is used to estimate discrete values (s). In simple words, it calculates the probability of occurrence of an event by fitting data to a logit function. Therefore, it is often referred to as logit regression. Since the probability is calculated, its efficiency values are between 0 and 1 since (as expected).

Decision Tree

This is one of my favoured algorithms, and I use it very often. It is a type of algorithm for supervised learning that is often used for problems with classification. Surprisingly, for both categorical and constant dependent variables, it works. We divide the population into two or more homogeneous sets in this algorithm. This is achieved on the basis of the most relevant attributes/independent variables to render as different classes as possible.

SVM(Support Vector Machine)

It is a technique for classification. We plot each data item in this algorithm as a point in n-dimensional space (where n is the number of characteristics you have) with the value of a particular coordinate being the value of each characteristic.

For example, if we had only two characteristics, such as an individual’s height and hair length, we would first plot these two variables in a two-dimensional space where each point has two coordinates (these co-ordinates are known as Support Vectors)

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