MACHINE LEARNING ALGORITHMS
Now that we are done with two major steps which are retrieving and loading our data and preprocessing our data to remove null values, normalize data and label encoding our data .
Now are data is ready to be trained on a model . But how do we do it?
What is an algorithm ?
To reach from India to the States , there is a predefined path, including various steps that need to be done in a sequential manner .
You first see where you are at present and decide where you want to go . After deciding this you check whether you have a passport or not , if not you apply for passport . After that , you state your purpose for visiting USA and apply for visa . If approved , you book your tickets otherwise you re-apply for visa . What we did here is basically following an algorithm to reach USA from India .
An algorithm is a step-by-step procedure for solving a problem or performing a task . It is a list of instructions that are followed in a specific order to achieve a desired outcome.
What is a Machine Learning Algorithm ?
Machine learning algorithms are mathematical procedures and techniques that allow computers to learn from data without being explicitly programmed.
These algorithms can be used to identify patterns in data, make predictions, and perform tasks automatically.
Let us discuss the above in detail and discuss the three categorizes of machine algorithms .
- Supervised learning: Supervised learning algorithms are trained on a set of labeled data, where each input has a known output. The algorithm learns to predict the output for new inputs based on the patterns it has learned from the training data. Some example of this are email filtering, credit scoring , voice recognition.
- Unsupervised learning: Unsupervised learning algorithms are trained on a set of unlabeled data, where the inputs do not have known outputs. The algorithm learns to identify patterns in the data without any prior knowledge of what to look for. We can use this in anomaly detection .
- Reinforcement learning: Reinforcement learning algorithms learn by trial and error. The algorithm is placed in an environment and rewarded for taking actions that lead to desired outcomes. The algorithm learns to take actions that maximize its rewards over time. It is used in automated robots, natural language processing , marketing and advertising .
Now we shall discuss various algorithms that are used in machine learning in detail .
Regression:
Regression is a supervised learning method for determining the relationship between dependent and independent variables.
There are two types of regression:
- Linear regression: This is used to detect the relationship between two variables and to make future predictions. It is further subdivided according to the number of independent and dependent variables.
- Logistic regression: Logistic regression is used when the dependent variable is categorical or has binary outputs such as ‘yes’ or ‘no’. Since logistic regression is used to solve binary classification problems, it predicts discrete values for variables.
Naive Bayes:
The Naive Bayes algorithm is well-suited for large datasets because each program in the algorithm operates independently, and the presence of one feature has no effect on the other.
Its applications include text classification, and recommendation systems, among others.
Classification:
Classification is a type of supervised learning algorithm which involves the process of accurately assigning data to different categories or classes.
Some of the various algorithms are :
- K-nearest neighbor
- Random forest
- Support vector machines
- Decision trees
- Linear classifiers
Neutral Networks:
Neutral Networks perform the process of grouping or categorizing raw data. Additionally, this algorithm is also employed in the interpretation of sensory data and the identification of patterns.
Random Forest
The random forest algorithm is known as an ensemble method as it combines multiple supervised learning techniques to make a conclusion. Moreover, it uses several decision trees to classify each tree, making it a popular choice in a variety of industries.
In the coming articles we will delve in each classification in detail and understand their functioning and uses in detail .
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