This being a beginner’s tutorial, I will try to make it as simple as it could be.

Have you ever went for grocery shopping? What do you do before going to the market?

I always prepare a list of ingredients beforehand. Also, I make the decision according to the previous purchasing experience. Then, I go and purchase the items. But, with the rising inflation, it’s not too easy to work in the budget. I have observed that my budget gets **deviated** a lot of times.

This happens because the shopkeeper changes the quantity and price of a product very often. Due to such factors, I have to modify my shopping list. It takes a lot of **effort**, **research** and **time** to **update** the list for **every change**.

This is where **Machine Learning** can come to your rescue. Still confused?

Don’t worry! Read this DataFlair’s latest Machine learning tutorial to get **deep insight** and understand why machine learning is trending.

### What is Machine Learning?

Machine Learning is the most popular technique of **predicting** the **future** or **classifying information** to help people in making necessary decisions.

Machine Learning algorithms are trained over instances or examples through which they learn from **past experiences** and also **analyze** the **historical data**.

Therefore, as it trains over the examples, again and again, it is able to identify patterns in order to make predictions about the future.

### Machine Learning Tutorial: Introduction to Machine Learning

After knowing what machine learning is, let’s take a quick introduction to machine learning and start the tutorial.

With the help of Machine Learning, we can develop **intelligent systems** that are capable of taking **decisions** on an **autonomous basis**. These algorithms learn from the past instances of data through **statistical analysis** and **pattern matching**. Then, based on the learned data, it provides us with the **predicted results**.

**Data** is the core **backbone** of machine learning algorithms. With the help of the historical data, we are able to **create more data** by training these machine learning algorithms.

For example, **Generative Adversarial Networks** are an advanced concept of Machine Learning that learns from the historical images through which they are capable of generating more images. This is also applied towards **speech** and **text synthesis**.

Therefore, Machine Learning has opened up a vast potential for **data science applications**. Machine Learning combines **computer science**, **mathematics**, and **statistics**. **Statistics** is essential for drawing **inferences** from the **data**.

**Mathematics** is useful for developing **machine learning models** and finally, **computer science** is used for **implementing algorithms**.

However, simply building models is not enough. You must also **optimize** and **tune** the **model** appropriately so that it provides you with **accurate results**. Optimization techniques involve tuning the **hyperparameters** to reach an **optimum result**.

Machine Learning is used in **every domain**. It is being used to **impart intelligence** to **static systems**. With the knowledge acquired from the data, it is used to **build intelligent products**.

### Why Machine Learning?

The world today is evolving and so are the **needs** and **requirements** of people. Furthermore, we are witnessing a **fourth industrial revolution** of **data**.

In order to derive **meaningful insights** from this data and **learn** from the way in which people and the **system interface** with the **data**, we need computational algorithms that can churn the data and provide us with results that would benefit us in various ways.

Machine Learning has revolutionized industries like **medicine**, **healthcare**, **manufacturing**, **banking**, and several other industries. Therefore, Machine Learning has become an **essential part** of modern industry.

Data is powerful and in order to harness the power of this data, added by the massive increase in computation power, Machine Learning has added another dimension to the way we perceive information.

Machine Learning is being **utilized** everywhere.

The electronic devices you use, the applications that are part of your everyday life are powered by **powerful machine learning algorithms**.

Machine Learning example – **Google** is able to provide you with appropriate search results based on browsing habits.

Similarly, **Netflix** is capable of recommending the films or shows that you would want to watch based on the machine learning algorithms that perform predictions based on your **watch history**.

Furthermore, machine learning has facilitated the **automation** of redundant tasks that have taken away the need for manual labor. All of this is possible due to the **massive amount** of **data** that you generate on a daily basis.

Machine Learning facilitates several **methodologies** to make sense of this data and provide you with **steadfast** and **accurate results**.

### How does Machine Learning Work?

With an exponential increase in data, there is a need for having a system that can handle this **massive load** of **data**.

Machine Learning models like **Deep Learning** allow the vast majority of data to be handled with an **accurate generation** of **predictions**.

Machine Learning has revolutionized the way we **perceive information** and the** various insights** we can gain out of it.

These machine learning algorithms use the patterns contained in the training data to perform **classification** and **future predictions**. Whenever any new input is introduced to the **ML model**, it applies its learned patterns over the new data to **make future predictions**. Based on the final accuracy, one can **optimize** their models using various **standardized approaches**.

In this way, Machine Learning model learns to adapt to new examples and produce **better results**. Next in Machine Learning tutorial is its types. Have a look –

### Types of Machine Learning

Machine Learning Algorithms can be classified into 3 types as follows –

**Supervised Learning****Unsupervised Learning****Reinforcement Learning**

#### Supervised learning

Supervised learning is that the machine learning task of learning a function that maps an **input** to an **output** supported example input-output pairs.

In Supervised Learning, the dataset on which we train our model is **labeled**. There is a clear and **distinct mapping** of input and output. Based on the example inputs, the model is able to get **trained** in the **instances**.

An example of supervised learning is **spam filtering**.

Based on the **labeled data**, the model is able to determine if the data is **spam** or **ham. **This is an easier form of **training**.

Spam filtering is an example of this type of **machine learning algorithm**.

#### Unsupervised Learning

Unsupervised Learning may be a machine learning technique during which the users don’t got to **supervise the model**. Instead, it allows the model to figure on its own to get **patterns** and **knowledge** that was **previously undetected**. It mainly deals with the **unlabeled data**.

In Unsupervised Learning, there is no labeled data. The algorithm identifies the **patterns** within the **dataset** and **learns** them. The algorithm groups the data into **various clusters** based on their **density. **Using it, one can perform **visualization** on **high dimensional data**.

One example of this type of Machine learning algorithm is the **Principle Component Analysis**.

Furthermore,** K-Means Clustering** is another type of Unsupervised Learning where the data is clustered in groups of a similar order. The learning process in Unsupervised Learning is solely on the basis of **finding patterns** in the **data**.

After learning the patterns, the **model** then makes **conclusions**.

#### Reinforcement Learning

Reinforcement learning is one among three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

Reinforcement Learning is an **emerging** and **most popular** type of Machine Learning Algorithm. It is used in various **autonomous systems** like **cars** and **industrial robotics**. The aim of this algorithm is to reach a goal in a **dynamic environment**. It can reach this **goal** based on several rewards that are provided to it by the system.

It is most heavily used in **programming robots **to perform **autonomous actions**. It is also used in making **intelligent self-driving cars**.

Let us consider the case of **robotic navigation**.

Furthermore, the **efficiency** can be improved with further **experimentation** with the agent in its environment. This the main principle behind **reinforcement learning**.

There are similar sequences of action in a reinforcement learning model.

### Machine Learning Algorithms

Let us see some most common machine learning approaches:

#### 1. Regression

Regression models are used **extensively** to **predict values** based on the **variables** that are **dependent** on **several factors**.

The most common example of regression is **Linear Regression** where there is a **linear relationship** or **correlation** between the **predictor variable** and the **response variable**.

There are also other types of regression such as **ARIMA** **regression** that makes use an **auto-correlation regression** model to **forecast continuous values** provided by the **time-series data**.

They are used in **forecasting** the **stock prices** and other values that are based on time.

#### 2. Decision Tree Learning

Decision Trees are a **supervised** **type** of **machine learning** **algorithms**. These trees are mainly used for **predictive modeling**. We **create** a **decision tree** that is able to **take decisions** based on user input.

Decision Trees can be used for **both regressions** as well as **classification**. These trees are used to provide **graphical outputs** to the user based on several **independent variables**.

#### 3. Support Vector Machines

**Support Vector Machines** or **SVMs** are machine learning algorithms that are used to **classify data** into **two categories** or **classes**.

It is a type of supervised learning algorithms that makes use of several types of **kernels** to classify the data. Based on the prediction performed, it can categorize whether it falls into **one class** or any **other class**.

With the help of SVMs, one can perform **both linear** as well as **non-linear** **classification**. An SVM classifier **divides** the **data** into **two classes** using a **hyperplane**.

#### 4. Association Rule Learning

Association Rule Mining is used for finding relationships between several variables that are present in the database. It is a type of data mining technique through which you can discover association between several items. It applied in sale industries mostly to predict if the customer will buy item Y if he has purchased the item X.

#### 5. Artificial Neural Networks (ANN)

An Artificial Neural Network is an **advanced form** of **machine learning technique**. These **neural networks** are modeled after the **human nervous system** and are therefore called **neural networks**.

There is a connection of several **neurons** which **compute **the **information**. These neurons capture the **statistical structure** and are therefore able to **create **a **joint probability distribution** over the **input variables**. These neural networks are **apt** at **finding patterns** over **large datasets**.

Neural Networks can perform **classification** as well as **regression** tasks with **high accuracy**.

Furthermore, they **eliminate** the requirement for doing **heavy statistical tasks** in **pre-processing** as they are **quite adequate** in realizing patterns on their own.

#### 6. Inductive Logic Programming

In this, logic programming forms the core part to produce a **rule-like learning model**.

**Inductive Logic Programming** or **ILP** presents the input information, **hypothesis** as well as the **background contextual knowledge** in the form of several rules that have to be followed with **logic**.

It makes use of **functional programs** to carry out **inductive programming** to **process hypothesis** in part rules.

Training models are quite often used for developing this model which is then used to **forge relationships** between **several variables**.

#### 7. Reinforcement Learning

The aim of Reinforcement Learning is to **direct** the agent towards **maximizing rewards** and **reach its goal**. This takes place in a **dynamic environment** where the agent has to chart its way to the goal through a **series of trials** and **errors**. Each time it takes a **correct route**, its **profit is maximized** and when it **encounters** a **wrong approach**, its **profit is minimized**.

Reinforcement Learning is widely used in** self-driving cars** and **autonomous robotics** that require **self-decision making capability**.

Reinforcement Learnings are experimental in nature and through a **series of trials** are able to reach their goals with **maximum accuracy (or rewards)**.

#### 8. Clustering

In** clustering**, the observations are **divided into groups** or **clusters**. These clusters are formed based on **similar data** and have **similar criteria**. These criteria can be **density** or **similar structure** of the **data**.

There are several clustering techniques that make use of **different criteria** to **cluster the data**.

For instance, the **distance between the data**, the **density of the data** and **graph connectivity** are some of the criteria that **define techniques** for **clustering** in **machine learning**.

Since there are **no labeled data** or **input-output mapping**, this type of technique is an **unsupervised machine learning **procedure.

#### 9. Similarity and Metric Learning

Similarity determination is one of the **key functions** of machine learning. In this form of learning, the **ML model** is provided a **mix of similar** as well as **dissimilar data objects**.

The machine learning model **learns to map** similar objects together and learns a similarity function that allows it to group similar objects together in the future.

#### 10. Bayesian Networks

A Bayesian Network is an **acyclic directed graphical model**. This model is also called **DAG** which represents the probability of several **independent conditioned variables**.

One can illustrate the relationship between **disease** and **symptoms**. It can be used to **compute** the **probabilities** of **various diseases**. They can be used to find the **diagnosis** of **several diseases** through a **calculated approach** of **listing probabilities** of various **factors** that could have contributed towards it.

More advanced forms of **Bayesian Networks** are **Deep Bayesian Networks**.

The basic principle behind the Bayesian Network is the **Bayes theorem** which is the most important part of the **probability theory**. With the help of Bayes Theorem, we determine the **conditional probability** of an **event**. This **conditional probability** is of a **known event**.

The conditional probability itself is the **hypothesis**. And, we calculate this **probability** based on the previous evidence.

**P (A/B) = P (B/A)*P (A)/P (B)**

Using a **well-defined network** of a **connected graph**, a user can make a **DAG** to **model conditional dependencies**

#### 11. Representation Learning

In order to represent the data in a **more structured format**, we make use of **representation learning**. This formats the **data efficiently** so that the **model** can **train better** to **provide accurate results**.

The representation of data is one of the key factors that can affect the **performance** of the machine learning method. This allows the algorithm to **learn better** from the **data**.

Using representation learning, algorithms are able to **preserve** the **input data** and **essential information**. Therefore, a model is able to **capture** most of the **information** during **pre-processing**.

Furthermore, the inputs present in pre-processing are able to gather **data generating** a defined **distribution**.

#### 12. Sparse Dictionary Learning

In the method of Sparse Dictionary, a **linear combination** of **basis functions** as well as **sparse coefficients** are assumed.

The elements of a sparse dictionary are called **atoms**. These atoms altogether compose a **dictionary**. It is an **extension** of representation learning. It is used most widely in **compressed sensing** and **signal recovery**.

In this method, we represent a datum as a **linear combination** of **basis functions** and then **assume the coefficients** to be **sparse**.

So, this was all in the latest Machine learning tutorial for beginners. Many of you might find the umbrella terms** Machine learning**, **Deep learning**, and **AI confusing**.

So, here is some additional help; below is the difference between **machine learning**, **deep learning**, and **AI** in simple terms.

### Machine Learning vs Deep Learning vs AI

#### Machine Learning

Machine learning may be a method of **knowledge analysis** that automates **analytical** **model building**. It’s a branch of AI supported the thought that systems can learn from **data**, **identify patterns** and **make decisions** with **minimal human intervention**.

Machine Learning is a part of Artificial Intelligence that involves **implementing algorithms** that are able to learn from the **data** or **previous instances** and are able to **perform tasks** **without explicit instructions**.

The procedure for learning from the **data** involves **statistical recognition** of **patterns** and **fitting** the **model** so as to evaluate the **data** more **accurately** and provide us with **precise results**.

#### Deep Learning

Deep learning is a **component** of a **broader family** of **machine learning methods** supported **artificial neural networks** with **representation learning**.

Learning is often **supervised**, **semi-supervised** or **unsupervised. **Deep Learning is a part of Machine Learning that involves the **usage** of **artificial neural networks**.

Deep Learning machine learning algorithms are the most **popular** choice in many industries due to the **ability of neural networks** to learn from **large data** more **accurately** and provide **steadfast** **results** to the user.

#### Artificial Intelligence

AI is the **greater pool** that contains an **amalgamation** of all the above-discussed **technologies**. Artificial Intelligence is still **under research** and involves **imparting sentient intelligence** to the machines.

However, Artificial General Intelligence is still far fetched and will require years of **research** before we can have even a basic version of it.

### Summary

In this machine learning tutorial, we went through the basics of **machine learning** and how **computing power** has evolved over time to accommodate **advanced machine learning algorithms**.

Computers are **gaining intelligence** owing to the **data** that is generated in a **vast amount**.

We went through the **different types** of **machine learning algorithms** and further took a brief look at some of the **popular ML algorithms**.

We hope that you are now **well acquainted** with **machine learning**.

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