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Everything you need to Know about Machine Learning !

Machine Learning is a broad field. More broad are doubt and question that people have about it.

We are tired of answering few machine learning related questions that we get on dm of insta page and email.

Here if have put all the common questions that we get and provided all the resource that will help you exploring this field and its limitless possiblities.

After reading this blog you will be clear with what is machine learning, its use cases(examples), future career scope, salary, learning curve for freshers, why you should choose this, how it is different or related with artificial intelligence and many more.

What is machine learning ?

in Machine learning, Machine means “follows the instructions of humans”,learning means “knowing about something”.

Human beings can learn from the past experiences or shared experiences of others but machine can learn from but following instruction of human being.

Here machine is a learner it learns the instruction given the user so that user can say as training machine is traine.

In the traditional programmin user have to give the input and instruction. It gives the output as the “Result”. The term “machine learning” means that training machine.

We just give data, machine itself train itself using algorithms.

Where Machine Learning is Used? Examples.

EXAMPLE: Smart assistants like siri, alexa, suggestions about friends tags on facebook or instagram, online transportation networks, social media, self driving cars, image recognition and many more.

Machine learning also helps in estimating disease break through driving medical information for outcomes research, planning and assisting therapy, and entire patient management.

Along with machine learning, AI in healthcare is also implemented for efficient monitoring.

Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products.

Many of today’s leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations.

Machine learning allows self-driving cars to instantaneously adapt to changing road conditions, while at the same time learning from new road situations.

By continuously parsing through a stream of visual and sensor data, onboard computers can make split-second decisions even faster than well-trained drivers.

See this post to know what project you can build once you learn this skill.

Is machine learning a good career?

Yes, machine learning is a great career path if you’re interested in data, automation, and algorithms and analyzing large amounts of data and implementing and automating it.

If pay is important to you, a career in machine learning has a good base salary as well. In fact, The World Economic Forum stated that “AI, Machine Learning, and automation will power the creation of 97 million new jobs 2025.”

So, we’d say now is a great time to start your career in machine learning is like machine learning engineer, Data Scientist etc…

Forbes stated it Data Science as “The Sexiest Job of the 21st Century”.

credit: harvard bussiness review (hbr.org)

Which job has highest salary in India?

One of the reason of most of the enthusiast in this field is salary. And at the end of the day it is what matters. Nothing to worry about salary if you interest in this field.

Once you get practical, real world skills and experience in this field you have limitless opportunities. This is a list of technical field roles with respect to their salary.

  1. Machine learning engineer
  2. Data Scientist.
  3. Data Analyst.
  4. Blockchain Developer.
  5. Digital Marketer.
  6. Cloud Computing Professional.
  7. Artificial Intelligence and Machine Learning Expert.
  8. Manager (MBA)
  9. Software Developer
  10. Medicine & Nursing Jobs

According to the glassdoor , with average salary of ₹8,93,562 per year in India it is in the top of list.

Can a fresher learn machine learning?

Yes, why not definitely If you’re a newbie to the programming language and how it’s applied in machine learning, you can learn through a machine learning course and its can help you learn how to develop machine learning algorithms using concepts of time series modeling, regression,binary & multi classification.

 Machine learning job if he/she masters the required skills. What are those? Here is a quick guide.

To have a successful career in the machine learning landscape, freshers need to plan on how they can perform well and work closely with people who have considerable experience in the same field or try to create atmosphere similar the your field and platforms like kaggle competitions, create a account in github

Just Remeber don’t get stuck in theoritcal part and terminologies. Just they are technical words to say easy things that we often use.

In machine learning pandas python, matplotlib are necessary libraries that you should know.

What are the top most stressful jobs?

  • Airline pilot: 61.20.
  • Police officer: 51.94.
  • Broadcaster: 51.27.
  • Event coordinator: 51.19.
  • Newspaper reporter: 49.96.
  • Public relations executive: 49.48.
  • Senior corporate executive: 48.97.
  • Taxi driver: 48.17.

What are you looking for? You will not get it there. As it is not.

In the beginning you may find it frustrating and hard to cope up with the course structure, but eventually as you build few projects you will have a clear understanding of what algorithms are used, how to pre-process the data, analyse it and as you get skilled. It becomes easier.

Difference between AI and ml?

Artificial intelligence and machine learning are the part of computer science that are correlated with each other.

These two technologies are the most trending technologies which are used for creating intelligent system although these are two related technologies and sometimes people use them but still both are the two different terms in various cases

Artificial IntelligenceMachine learning
Artificial intelligence is a technology which enables a machine to simulate human behavior.Machine learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly.
The goal of AI is to make a smart computer system like humans to solve complex problems.The goal of ML is to allow machines to learn from data so that they can give accurate output.
In AI, we make intelligent systems to perform any task like a human.In ML, we teach machines with data to perform a particular task and give an accurate result.
Machine learning and deep learning are the two main subsets of AI.Deep learning is a main subset of machine learning.
AI has a very wide range of scope.Machine learning has a limited scope.
source: https://www.javatpoint.com/difference-between-artificial-intelligence-and-machine-learning

What are the 8 steps of machine learning?

Step 1: Collecting Data

As you know, machines initially learn from the data that you give them. 

The quality of the data that you feed to the machine will determine how accurate your model is. If you have incorrect or outdated data, you will have wrong prediction and not relevant.

Data can be collected through many way. The company you are working for might provide you, databases, on-site data gathering, web scraping – pulling data from website which allow to use their data for this purpose, for practise you make take data from kaggle.

Step 2: Preparing the Data

After you have your data, you have to prepare it.

The data should be put all together and its helps to understand whats is the next process the changing and cleaning of data unwanted data, missing values, rows, and columns, duplicate visualize data to understand how it is structured and understand the relationship between various variables and classes present.

Splitting the cleaned data into two sets – a training set and a testing set.

Step 3: exploratory data analysis(EDA)

Is used data scientists to analyze and investigate data sets and summarize their main characteristics, often employing data visualization methods.

It helps determine how best to manipulate data sources to get the answers you need, making it easier for data scientists to discover patterns, test a hypothesis a better understanding of data set variables and the relationships between them.

Step 5: Choosing a Model

The process of choosing one of the models as the final model that addresses the problem. Model selection is different from model assessment.

For example, we evaluate or assess candidate models in order to choose the best one, and this is model selection.

Step 6: Training the Model

A dataset that is used to train an ML algorithm. It consists of the sample output data and the corresponding sets of input data that have an influence on the output.

The training model is used to run the input data through the algorithm to correlate the processed output against the sample output.

Step 7: Evaluating the Model

Training evaluation models are systematic frameworks for investigating and analyzing the effectiveness of training or learning journeys. 

Step 8: Parameter Tuning

Hyperparameter tuning is choose a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a model argument whose value is set before the learning process begins. The key to machine learning algorithms is hyperparameter tuning.

Bonus 🙂 Making Predictions using trained machine learning model

Making predictions is a strategy in which readers use information from a text (including titles, headings, pictures, and diagrams) and their own personal experiences to anticipate what they are about to read

Is machine learning easy for beginners?

Is coding required in machine learning?

What basics do I need for machine learning?

  • Statistics: random variables,probability,common distribution as a gaussian and more variances,correlation,simple linear,basic hypothesis testing this are some basic domain knowledge
  • Linear Algebra: matrices(indexing matrix),vectors along with the (numpy in python),matrix-matrix addition and subtraction,scalar multiplication and division,matrix-vector multiplication,geometric intuition of matrix-vector multiplication,matrix-matrix multiplication etc…
  • Calculus.
  • Probability.
  • Programming Languages.

Can I teach myself machine learning?

yes but difficult..

  1. Step 1: Adjust Mindset. Believe you can practice and apply machine learning. …
  2. Step 2: Pick a Process. Use a systemic process to work through problems. …
  3. Step 3: Pick a Tool. Select a tool for your level and map it onto your process. …
  4. Step 4: Practice on Datasets. …
  5. Step 5: Build a Portfolio.

What are the types of ML?

There are four types of machine learning algorithms: supervised, semi-supervised, unsupervised and reinforcement.

supervised learning: training data with labelled data

supervised learning:training data with unlabelled data or raw data

semi-supervised learning: the combines of small amount of labelled data with a large amount of the data during training its falls between both supervised and unsupervised learning

reinforcement:

How many algorithms are there in machine learning?

there are some variations of how to define the type of machine learning.algorithms but commonly they are divided into categories are follows

  • supervised learning common algorithms are 1. nearest neighbour 2.naives bayes 3.decision trees 4. support vector machines (svm) 5.neural networks
  • unsupervised learning common algorithms are 1. k-means clustering 2.association rules 3.KNN (k-nearest neighbors) 4.Hierarchal clustering 5.Anomaly detection. 6.Principle Component Analysis 7.Independent Component Analysis 8.Apriori algorithm
  • semi-supervised learning & reinforcement learning common algorithms are 1,Q-learning 2. temporal difference (TD) 3. deep adversarial networks

Can a non technical person learn machine learning?

yes.

refer them to other link with story.

Chitranshu Harbola

Self taught programmer, Web Developer and an aspiring Machine learning engineer cum Data Science student

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