What is mean by Machine Learning?- A complete guide

Let’s try hands on machine learning- a special branch of Artificial Intelligence called Machine Learning that focused on data analysis and processing. Now you can learn machine learning from scratch if you are aware about the concept. The data science machine learning are the trending and revolutionary technologies that booming in the modern days. The Arduino tiny machine learning kit is also a fresh concept associated with the similar one that we are goring to discuss here. For now, let’s get introduce with these cutting-edge techniques and its benefits through a simple story.

Mithila’s housekeeper was very intelligent and hardworking. She was working for Mithila for last 5 years. She did all the work one day, but forgot to fold the clothes. Then one day she forgot to put the clothes in the washing machine. Why is Mithila doing this errand? Mithila decided one day and asked her maid , “Why didn’t you fold the clothes that day? And yesterday, you didn’t put the clothes in the washing machine?”

On this, she said “I didn’t notice.” Then Mithila said that “You have been working with me for so many years, you should remember all these things and work.” Then her maid replied with the question that “Am I any already built machine that can recall every minute thing in advance and perform accordingly in a consistent way?”.

Mithila said, “No, you are not. But one learns only through experience, right? You have 5 years of experience. How come you don’t notice the work?” Her maid replied, “But machines have no experience.”

Mithila wondered if there would really be such a thing where machines would do all the work themselves. Is there any such machine?

Stay tuned to know more….`

The bell rang and Chinu came. Chinu Mithila’s childhood friend. Chinu was an engineer. Mithila explained this idea of turning human into machines for household work to her. On this, Chinu said that such machines may be introduced soon which will do all the work by itself. And it never has to be told what to do.

Mithila was surprised and said “What are you saying? How is this possible?”

“Everything is possible now with data science machine learning and AI,” said Chinu.

You must have got topic of the blog.

Yes…

Today, I am goanna discuss Machine Learning with you all.

Mithila asked “Chinu, I am getting super curious to understand Machine Learning. Can you please explain it to me?”.

Chinu said “Off course. Let’s dive in.”

Now Chinu is explaining about the very interesting concept of data science machine learning.

Introduction of Machine Learning

Machin Learning (ML) is a branch of Artificial Intelligence. Technically, the concept is entirely involved with the process of data stored in the computers, manipulating it, and work without any human instructions.

Let’s take an example that if we want to make a computer calculation, we need to give command or instruction to it. Then computer uses its application and process for making calculations. It’s a conventional way of functioning computers. But in machine learning, computers are programmed in a way that they need not have to receive any kind of instructions. Depend on the data stored inside it, computer will analyse the data, expected responses on its own and then show it to the users. It is just an overview. Now let’s move ahead with it’s detailed techniques.

What is mean by Machine Learning?

We all know that robots are the product of Artificial Intelligence. They are also machines. But we need to instruct them. In machine learning you will find the involvement of algorithms and statistical models. Development of these algorithms enable computers to perform a particular task without explicit instructions. Instead, this sort of systems learns from the data collected and stored in it. Also, make predictions or decisions based on that data. The core idea is to permit the machine to learn from experience and improve its performance over time. Now let’s understand the key points of machine learning.

Key concepts in machine learning includes following points:

1. Data: is the foundation of machine learning, data is used to train models. It can be in various forms like text, images, or numerical values.

2. Algorithms: are the methods used to process data and learn from it. Neural nets, decision trees, and linear regression are a few examples.

3. Training: is the process where the model learns from the data. During training, the model adjusts its parameters to minimize the error in its predictions.

4. Testing: After training, the model is evaluated on a separate set of data to assess its performance and generalization capability.

5. Features: are the individual measurable properties or characteristics of the data being used for analysis.

6. Model: is the result of training an algorithm on data. It can be used to make predictions or decisions based on new data.

Understood?

What are the basics can one learn to understand this concept?

To learn machine learning from scratch, you need to adapt knowledge regarding the types of machine learning: 3 types of machine learning have introduced are as-

1. Supervised Learning: The model is trained on a labeled dataset, which means the input data is paired with the correct output. Examples include classification and regression tasks.

2. Unsupervised Learning: The model is trained on an unlabeled dataset and tries to find patterns and relationships in the data. Examples include clustering and dimensionality reduction.

3. Learning with Semi-Supervision: For training, it combines labelled and unlabelled data. It can improve learning accuracy when labeled data is scarce.

2. Reinforcement Learning: The model learns by interacting with an environment and receiving rewards or penalties based on its actions. It is frequently utilised in video games and robotics.

4. Reinforcement Learning: The model learns by interacting with an environment and receiving rewards or penalties based on its actions. It is frequently utilised in video games and robotics.

6. Deep Learning: A subfield of machine learning that uses neural networks with many layers (deep neural networks) to model complex patterns in data.

Where we use machine learning technology?

Now, in the modern digital world, where every sector is using computers, machine learning is also getting used across various industries for different purpose. Let’s understand it’s applications.

A. Healthcare:

• Medical Diagnosis: Identifying diseases and conditions from medical images, lab results, and patient data.
• Personalized Medicine: Tailoring treatment plans based on individual patient data and predicted responses.
• Predictive Analytics: Predicting patient outcomes, such as the likelihood of readmission or disease progression.

B. Finance:

• Fraud Detection: Identifying fraudulent transactions and activities.
• Algorithmic Trading: Developing models that can execute trades based on market conditions.
• Credit Scoring: Assessing the creditworthiness of loan applicants.

C. Transportation:

• Self-Driving Cars: Enabling autonomous vehicles to navigate and make decisions.
• Route Optimization: Finding the most efficient routes for delivery and transportation services.
• Predictive Maintenance: Predicting vehicle maintenance needs to prevent breakdowns.

D. Manufacturing:

• Quality Control: Identifying defects in products using image recognition and other data.
• Predictive Maintenance: Predicting when equipment will fail to schedule timely maintenance.
• Supply Chain Optimization: Improving the efficiency of supply chains through demand forecasting and inventory management.

E. Energy:

• Smart Grids: Managing electricity distribution more efficiently.
• Predictive Maintenance: Monitoring equipment for potential failures.
• Energy Consumption Forecasting: Predicting energy demand to optimize production and distribution.

F. Telecommunications:

• Network Optimization: Managing network traffic and resources to improve service quality.
• Churn Prediction: Predicting which customers are likely to leave a service.
• Fraud Detection: Identifying fraudulent activities such as unauthorized access or usage.

G. Entertainment:

• Content Recommendation: Suggesting movies, music, or articles based on user preferences.
• Content Creation: Generating new music, art, or writing using generative models.
• Audience Analysis: Understanding viewer or listener behavior to tailor content.

H. Natural Language Processing (NLP):

• Language Translation: Translating text from one language to another (e.g., Google Translate).
• Speech Recognition: Converting spoken language into text (e.g., Siri, Alexa).
• Chatbots and Virtual Assistants: Providing automated customer service and support.

I. Cybersecurity:

• Threat Detection: Identifying and mitigating potential security threats.
• Anomaly Detection: Detecting unusual patterns that may indicate security breaches.
• Spam Filtering: Identifying and filtering out spam emails.

J. Agriculture:

• Crop Monitoring: Using drones and sensors to monitor crop health and growth.
• Yield Prediction: Predicting crop yields based on various factors such as weather and soil conditions.
• Pest and Disease Detection: Identifying pests and diseases affecting crops.

In a nutshell, there are applications made with the use of machine learning concept. All these applications don’t need to be instructed for showing the correct results or finding ambiguities. Applications work on the basis of proper information only, according to the purpose for which they are designed. These applications demonstrate the versatility and potential of machine learning to drive innovation and efficiency across different fields.

Mithila was astonished when she got the concept. But still she was meeting with some confusion. She asked Chinu, “Where do we see this concept implemented in our daily lives?”.

Chinu said, “Do you watch movies on Netflix?” Mithila replied “Yes, I do.”

Then Chinu continued.

You must have seen recommended movies and shows on Netflix. Why does it show recommendations? is seen in Netflix’s attempt to foster user engagement by showing recommendations that viewers are likely to find entertaining based on their viewing preferences and history. Now let’s understand, how does Network understood the user’s taste and how does it work?

Following are the basic steps that process of machine learning includes.

1. Data Gathering:

• User Interactions: Netflix gathers data on the basis of what users regularly watch, how long they watch it, when they pause, and what they rate content.
• Content Information: Metadata about movies and TV shows, including genres, cast, director, release year, and more.

2. Data Processing:

• The gathered data is processed and cleaned to ensure it is suitable for training machine learning models. It covers handling missing values, normalizing data, and feature extraction.

3. Model Training:

• Collaborative Filtering: It is a technique identifies patterns in user behavior by finding similarities between users and items. For instance, if User A and User B have similar viewing histories, the system will recommend content watched by User A to User B and vice versa.
• Content-Based Filtering: This technique recommends content based on the features of the items themselves. If a user likes a particular genre or director, the system will recommend similar content.
• Hybrid Models: Netflix often combines collaborative and content-based filtering to improve the accuracy of its recommendations.

4. Model Deployment:

• The trained models are deployed into the production environment. When a user logs into Netflix, the system quickly analyzes their profile and viewing history to generate personalized recommendations in real-time.

5. Continuous Improvement:

• Netflix constantly collects new data on user interactions, which is fed back into the system to retrain and refine the models. This ensures that the recommendations stay relevant as user preferences evolve.

Now, let’s take a real-world example -

Let’s say a user named Sachin watches several romantic movies and rates them highly. The recommendation system will take the following steps:

a) Analyze Viewing History:

• Identify that Sachin enjoys romantic movies based on his viewing history and ratings.

b) Find Similar Users and Content:

• Use collaborative filtering to search other users with similar tastes to Sachin.
• Use content-based filtering to find other romantic movies with similar characteristics to those Sachin has enjoyed.

c) Generate Recommendations:

• Combine the results from both filtering techniques to generate a list of recommended movies and TV shows.

d) Present Recommendations:

• Display the recommended content to Sachin on his Netflix homepage, making it easy for him to find something new to watch.

What is it’s the impact?

• User Engagement: Personalized recommendations keep users engaged and reduce churn rates.
• Increased Viewing Time: By suggesting content that users are likely to enjoy, Netflix increases the time users spend on the platform.
• Revenue Growth: Enhanced user satisfaction leads to higher subscription retention rates and overall revenue growth. So, it is a kind of strategy to grow business.

I certainly can believe that you comprehended the notion after I delivered a real-world example of how machine learning is used to design a system that benefits both the user and the service provider.

Wrapping Up

From this point when Mithila come to know about this concept, she started saying that she will buy a machine if exists for doing her household stuff.

On her thought, Chinu said, “Don’t do this ever. Because of these AI tools we have made loss in almost all the industries. The majority of people working in the service industry are having difficulty earning money. How will one earn if machines substitute human?

You cannot abandon someone’s earning opportunities.” So, yes, there are some advantages of Machine Learning implementation and disadvantages also.

Mithila got upset and said, so machine learning is of no use for me. Chinu laughed at her and said, “ If you feel that your maid should work on her own without any instructions, then you can definitely increase her salary. It will definitely fruitful to you.”

So, what’s your opinion on this?