An all-inclusive guide to start Machine Learning
The study of computer algorithms that improve automatically through experience and use of data is called? The well-known answer is Machine learning. An emerging and in-demand domain that has been changing situations and solving complex issues in the blink of time. Learning this and mastering it up is a sea to cover with.
Let us just break it down for you to make the first step.
In machine learning, algorithms are trained to find patterns and features in massive amounts of data to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data.
How does Machine learning work?
1. Select and prepare the data set
Based on the problem set, choose the best data set for your model. A data set is a collection of data containing your independent and dependent variables.
What is an independent variable?
The variable that on computation will yield the result is called independent variable in Machine learning.
What is the dependent variable?
The result or the target is called the dependent variable.
2. Choose a suitable algorithm
Based on the data and some assumptions being satisfied, we choose the algorithm
Types of data:
1.Labeled data- It is a sample of data that comes with a tag (name, number, or a characteristic).
The algorithms that use labeled data are,
- Regression:
Regression analysis is a set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables.
- Decision tree:
It uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.
- Instant-based algorithms:
It uses classification to estimate how likely a data point is to be a member of one group or another based on its proximity to other data points.
What is this data-point?
A data point is a unit of information or a known fact.
1.Unlabeled data — It is a sample of data that comes without tags.
The algorithms that use unlabeled data are,
- Clustering:
It is used to group similar data objects in a model.
- Neutral Networks:
It is an algorithm that takes the concept of the human brain to recognize the relationship between large amounts of data.
- Train and build the model
- Use and improve the model
What are the methods in Machine Learning?
1.Supervised Learning
It is a Machine Learning task of mapping the input-output pair based on the given set of input-output pairs.
2.Unsupervised Learning
It is a Machine Learning task of learning patterns from unlabeled data.
3.Semisupervised Learning
It is a Machine Learning approach that combines labeled data with large amounts of unlabeled data during training.
Where is Machine Learning used in real-life scenarios?
1.Recommendations
Recommending songs, videos, or any content based on your history of viewing the same.
2.Self-Driving Cars
To guide the self-driving car in direction, making decisions based on obstacles, lane-management machine learning plays a big role.
3.Digital Assistants
Alexa, Siri, Google Assistant are powered by NLP which is a machine learning algorithm that enables the computer to process text, voice data and understands human language the way people do.
4.Data Analysis
Analyzing a huge amount of data and coming to a conclusion or suggestion enable organizations to operate wisely.
- Chatbots
- Detecting any fraud or issue
Is there scope in Machine Learning?
Since there is still a long way for this industry to reach its peak, the salary that you make as an ML professional will continue growing every year. All you need to do is keep upskilling and updating yourself.
Let us know step into our every first algorithm
Linear Regression :
Linear Regression is a Supervised learning model. It is used for finding the linear relationship between the dependent variable (target) and the independent variables. It examines two major things,
- Does the set of independent variables give a good target as result?
- Which independent variables contribute predominately to produce the outcome?
Assumptions in Linear Regression:
- Linear Relationship
- No or little auto-correlation among residues.
- No or little multi-collinearity
- Multivariate Normality
- Homoscedasticity of errors
You will get a clear view when we practically implement regression. Scroll down to check
There are two types of Linear Regression:
1.Simple Linear Regression
If a single independent variable is used to predict the value of a dependent variable, then it is Simple Linear Regression.
2.Multiple Linear Regression
If more than one independent variable is used to predict the value of a dependent variable, then it is called Multiple Linear Regression.
Let us have a hands-on Simple Linear Regression.
Every big learning begins with a small step. All the best to explore ahead!
-Manishma Sundararajan
