Introduction to Machine Learning

What is data science?

Many companies are interested in acquiring and analyzing more data, whether it is consumer behavior data, camera data from cars, or biomedical data for new medicines. People who specialize in developing questions around data and answering them using mathematical models are data scientists.

Data Science

Why is data science so hot right now? If you have data, you can extract so much information and make better decisions. These decisions can make a business more efficient or develop insights into business trends that your eyes can’t see.

What is Big Data?

Data Science

Data is a way to encoding information onto a machine. Whenever you make an Excel spreadsheet with different categories, text, and quantities, you have a data set. If you have multiple data sets, you store them in a container called a database.

Big data accumulates these smaller datasets over time as we continue making purchases on Amazon or record our heart rates using a FitBit. These datasets are large in size, and it is challenging to process and store big data. However, they contain a wealth of information that machine learning algorithms can use to solve different problems.

Introduction to machine learning

Machine learning is one of the fast-growing technologies in the world that make its place already in most of the top companies in the world. Machine learning helps the computer to learn from the data and experience to make the decisions of their own like humans. Machine learning uses various algorithms and mathematical statistics and models to learn from the data and make decisions.

In our modern technological world, there are a lot of applications using machine learning like speech recognition, voice recognition, speech translation, product recommendation, video recommendation, navigation, and many more…

What is Machine Learning?

Till now only humans can learn from the experience and the data and can able to make decisions with respect to the data and the surroundings. We have the computers that will work with our instruction set. All the instructions are hardcoded to a computer called a program for their proper working.

Now, what is a computer itself can able to learn from the data and the environment and able to take decisions with respect to data. In addition, what if a computer system can improve by itself according to the data; we already achieved it using Machine Learning.

Machine learning comes under Artificial Intelligence AI. A machine that able to analyze and learn from the provided data and can make decisions or answers according to the data.

Arthur Samuel gives the term Machine language in 1959.

By using sample data from a huge dataset called training data, the machine learning algorithms can able to build models that help to provide decisions without programming it. Machine learning algorithms can learn from the data and they can improve themselves to make it more accurate and efficient.

History of Machine learning

History

The history of machine learning starts in 1943 when a book on neural networks was presented by Walter Pitts and Warren McCulloch. In 1949 Donald Hebb published a book called “ The Organization of Behavior” which includes relations of neural networks is the second milestone in machine learning history. Arthur Samuel first introduced the machine-learning concept in 1959. Later in every year, there was development in the field of machine learning till now. Now the concept of automated machine learning is progressing.

What is the difference between Machine Learning and Normal Programming?

Machine Learing

In programming, we need a programmer who can able to write the instructions called a program with logic for performing a task regarding to the software that is developing. It needs so many experts for perfection.

Machine checks and analyze the data according to the instructions to provide the output. Any small change in the data will make the machine fail or it needs the programmer to make the change in the instructions, which needs a lot of time and effort.

In the case of Machine-learning, the job of a programmer is done by the machine itself. We are providing the machine input data and the output, that machine analyzes and learns the relation between input and output for making instructions. In machine learning there is no need for a programmer for any change in input data as the machine will make the instructions according to the input and output data.

What is the need for Machine learning?

Machine Learing

Data is the most valuable and major problem in our modern world of technology. The huge amount of data and their classification and learning are very difficult tasks for traditional programming. There comes the importance of machine learning, where we can learn from the data using machine learning algorithms and models automatically without much effort and time.

We can train the machine learning algorithms to make the models and with the help of models, we can predict the required output. It can also improve them using the data. We can learn more about the need in the coming tutorial.

How does machine learning work?

Machine Learing

Machine learning is the brain where all the learning and decisions happen in the case of AI.

In case of the humans, we learn from experience, with more experience we can make decisions that are more accurate. What happens if we face a new situation we cant be 100 percent sure that our decision will be accurate like a known situation.

The same logic is for machines, we have to make the machines learn from the data for accurate output. Machine learning algorithm learns from the data and makes a model that can able to predict the output even for the new data.

Machine learning accuracy depends on the amount of data and the quality of the data and the algorithm we choose for the data. We can learn the details later.

When Ml has a complex problem, it collects the data from all the sources and does some preprocessing on the data to make it error-free. Then we make a sample data called training data to feed into the ML algorithm. 

The algorithm accepts the training data to make a model, which is able to predict the output. After the model has done, we have to check the model with new data for checking the model can predict the output accurately.

Using machine learning, we do not need to make a new set of instructions for the new data.

Features of machine learning

  1. It uses data for learning and identifying patterns
  2. It can improve themselves from the data learning
  3. It can make data-driven decisions for AI
  4. Machine learning and data mining resemble each other if data is huge

Types of machine learning

Machine Learing

Machine learning methods can be divided into three types depending on the data quality we provide and the output we expect from the model, they are,

  1. Supervised learning
  2. Unsupervised learning
  3. Reinforcement learning
  4. Semi-supervised learning

Further again they are divided into many categories that we can learn deeply inside our coming tutorials.
 

Programming languages for machine learning

Machine Learing

Several programming languages will allow you to use machine learning libraries so that you don’t have to code up your own algorithms. We’ll briefly describe 3 common languages used for machine learning.

Python

Data Science

Python is an open-source general programming language that is the language of choice in today’s data science community. This is because:

  1. It is relatively easy to learn
  2. You can do many data-related tasks
  3. It is open-source
  4. It has an established data science community
  5. There are many libraries developed in Python

R

Data Science

The R programming language should not be discounted. Statisticians develop cutting-edge statistical and machine learning algorithms in R first before translating into another language.
While the syntax in R is a little clunky compared to Python, it still has many of the same benefits. If you want to use the latest machine learning algorithms for your projects, use R.

SQL

Data Science

Structured Queried Language (SQL) is known as a language to manipulate and pull data from databases. But you can use SQL to run machine learning algorithms directly or through Python/R scripts.
This is a common way machine learning is implemented in the industry and it is an important language to know as you develop your data science toolbox.

How to find machine learning projects

Here are some sites that are popular for crowdsourcing data scientists on real-world problems:

  1. GitHub contains many open-source projects that need developers.
  2. Kaggle is a competition site that allows you to work as an individual or as part of a team to develop real-world solutions.
  3. Freelance sites like Upwork or Fiverr will enable you to see jobs that real businesses are struggling with.

Summary

  • Data science uses data and mathematical models to optimize behaviors and come up with decisions.
  • Machine learning is a technology that uses data to learn patterns and come up with predictions given new data
  • Machine learning can be applied to many real-world problems.
  • Many programming languages will allow you to use machine learning quickly.