# what is data science & machine learning & artificial intelligence. complete guide from beginner to advanced step by step for free

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Let’s start with the basic definition first of what is data science and machine learning and artificial intelligence.

## Data Science:-

• Data Science deals with structured and unstructured data.
• Data science is a deep study of the massive amount of data, which involves extracting meaningful insights from raw, structured, and unstructured data that is processed using the scientific method, different technologies, and algorithms.

Example:
Let suppose we want to travel from point A to point B by road. Now, we will see that which is the best route to reach faster at the location, in which route there will be no traffic jam, and which will be cost-effective. All these decision factors will act as input data, and we will get an appropriate answer from these decisions, so this analysis of data is called the data analysis, which is a part of data science.

• Data science can help in different predictions such as various surveys, elections, flight ticket confirmation, etc.
• With the help of data science technology, we can convert a massive amount of raw and unstructured data into meaningful insights.
• Data science uses machine learning algorithms to solve various problems.

Data science is done in the following steps:-

1. Data Acquisition – web servers, logs, databases, APIs, online repositories.
2. Data Preparation
3. Exploratory Data Analysis
4. Data Modeling with the help of KNN, Naive Bayes, Decision Tree, etc.
5. Data Visualization can be done with the help of Seaborn, Matplotlib.

TOP MACHINE LEARNING ALGORITHMS YOU SHOULD KNOW:-

• Linear Regression
• Logistic Regression
• Linear Discriminant Analysis
• Classification and Regression Trees
• Naive Bayes
• K-Nearest Neighbors (KNN)
• Learning Vector Quantization (LVQ)
• Support Vector Machines (SVM)
• Random Forest

Machine learning in Data Science:- To become a data scientist, one should also be aware of machine learning and its algorithms, as, in data science, there are various machine learning algorithms that are broadly being used. Following are the name of some machine learning algorithms used in data science:

Regression
Decision tree
Clustering
Principal component analysis
Support vector machines
Naive Bayes
Artificial neural network

## Supervised Machine Learning:

Supervised learning is a machine learning method in which models are trained using labeled data.

Supervised learning can be used for two types of problems:

1) Classification

2) Regression.

Example:- suppose you are given a basket filled with different kinds of fruits. Now the first step is to train the machine with all different fruits one by one like this:
If the shape of the object is rounded and has a depression at the top, is red in color, then it will be labeled as –Apple.
If the shape of the object is a long curving cylinder having Green-Yellow color, then it will be labeled as –Banana.

Now suppose after training the data, you have given a new separate fruit, say Banana from the basket, and asked to identify it.

Since the machine has already learned the things from previous data and this time has to use it wisely. It will first classify the fruit with its shape and color and would confirm the fruit name as BANANA and put it in the Banana category. Thus the machine learns the things from training data(basket containing fruits) and then applies the knowledge to test data(new fruit).

in other words:-

Suppose we have an image of different types of fruits. The task of our supervised learning model is to identify the fruits and classify them accordingly. So to identify the image in supervised learning, we will give the input data as well as output for that, which means we will train the model by the shape, size, color, and taste of each fruit. Once the training is completed, we will test the model by giving the new set of fruit. The model will identify the fruit and predict the output using a suitable algorithm.

• Classification:-  problems use an algorithm to accurately assign test data into specific categories, such as separating apples from oranges. Or, in the real world, supervised learning algorithms can be used to classify spam in a separate folder from your inbox. Linear classifiers, support vector machines, decision trees, and random forests are all common types of classification algorithms.
• Classification: A classification problem is when the output variable is a category, such as “Red” or “blue” or “disease” and “no disease”.
• Regression:- is another type of supervised learning method that uses an algorithm to understand the relationship between dependent and independent variables. Regression models are helpful for predicting numerical values based on different data points, such as sales revenue projections for a given business. Some popular regression algorithms are linear regression, logistic regression, and polynomial regression.
• Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”.

Supervised learning deals with or learns with “labeled” data. This implies that some data is already tagged with the correct answer.

Types:-

1. Regression
2. Logistic Regression
3. Classification
4. Naive Bayes Classifiers
5. K-NN (k nearest neighbors)
6. Decision Trees
7. Support Vector Machine

Supervised learning allows collecting data and produces data output from previous experiences.
Helps to optimize performance criteria with the help of experience.
Supervised machine learning helps to solve various types of real-world computation problems.

Classifying big data can be challenging.
Training for supervised learning needs a lot of computation time. So, it requires a lot of time.

## Unsupervised learning

Unsupervised learning is the training of a machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Here the task of the machine is to group unsorted information according to similarities, patterns, and differences without any prior training of data.

Unlike supervised learning, no teacher is provided that means no training will be given to the machine. Therefore the machine is restricted to find the hidden structure in unlabeled data by itself.

• For example, suppose it is given an image having both dogs and cats which it has never seen.  Thus the machine has no idea about the features of dogs and cats so we can’t categorize it as ‘dogs and cats ‘. But it can categorize them according to their similarities, patterns, and differences, i.e., we can easily categorize the above picture into two parts. The first may contain all pics having dogs in them and the second part may contain all pics having cats in them. Here you didn’t learn anything before, which means no training data or examples.
• It allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with unlabelled data.

Unsupervised learning is classified into two categories of algorithms:

1. Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.
2. Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.
• Example-: To understand unsupervised learning, we will use another example. So unlike supervised learning, here we will not provide any supervision to the model. We will just provide the input dataset to the model and allow the model to find the patterns from the data. With the help of a suitable algorithm, the model will train itself and divide the fruits into different groups according to the most similar features between them.
• Supervised machine learning is Highly accurate and Unsupervised machine learning is Less accurate.

## Linear Regression

• Question:- What is linear regression in simple terms?

Answer:- Simple linear regression is a regression model that estimates the relationship between one independent variable and one dependent variable using a straight line.

in other words

• Linear Regression is the process of finding a line that best fits the data points available on the plot, so that we can use it to predict output values for inputs that are not present in the data set we have, with the belief that those outputs would fall on the line.

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