Machine Learning with Python

Anmol Sharma

2 months ago

Table of Content
  • Introduction
  • What is Machine Learning?
  • Applications of Machine Learning
  • What is Python?
  • Why Machine Learning with Python?
  • How to get started with Machine Learning Python?
  • Conclusion

Introduction

          Machine Learning is a subset of Artificial Intelligence and it is one of the most dominating technologies at present, having the potential to bring the next revolution in the world. On the other hand, Python is a programming language with wide applications, and Machine Learning is one of those applications. So, let us begin to understand Machine Learning with Python in-depth with a famous quote.
 “Machine learning will automate jobs that most people thought could only be done by people.” ~Dave Waters.

What is Machine Learn

           Machine Learning is a set of algorithms/techniques, which naturally learns through data and improves over time as they are exposed to more data. Algorithms and data are the key part of machine learning actions. Algorithms are constantly under work to become more accurate and reliable but the main factor that determines the success of the Machine Learning model is the data available for training the same model.
Take a look at the picture below that shows the workflow of the Machine Learning model:
Workflow of the Machine Learning model | insideaiml
Image source - ResearchGate
There are three subsets in machine learning and they are as follows
  • Supervised Learning 
  • Unsupervised Learning
  • Reinforcement Learning

Supervised Learning

         Supervised learning means training the model with labelled data i.e., data with both input and output labels.

Unsupervised Learning

         In the case of unsupervised learning, the model is trained using unlabelled data i.e., In this case, we don't know the output for a particular input value.

Reinforcement Learning

         Reinforcement learning makes the machine learn from the result of its actions i.e, the model will learn whether to perform a particular operation or not by its experience.

Applications of Machine Learning

         Machine Learning has applications in several domains. Below are some common applications of Machine Learning:
  • Prediction of house price, stock price, and cryptocurrency prices
  • Sentiment analysis in news
  • Recommendation systems in social media and e-commerce
  • Image detection in phones and security cameras
  • Weather forecasting
  • Automated chatbots and many more...

What is Python?

         Python is an interpreted, high-level, object-oriented programming language. It is the most famous programming language at present because of its simple syntax, high readability, and wide applications. 
It is mostly used for the following purposes:
  • Data Science
  • Back-end Development
  • App Development

Why Machine Learning with Python?

        There are several programming languages like R, C#, Java that can be used for machine learning, but Python stands out among these languages. And the reason is that Python has a vast number of libraries/packages and tools to implement machine learning most easily and effectively. 
Below are the most popular packages and tools used for machine learning with python.
  • NumPy - Used for scientific calculations in Python.
  • Pandas - Used for data analysis in Python.
  • Matplotlib - Used for visualization in Python. 
  • Scikit-learn – Used for implementing machine learning algorithms and techniques. 
  • TensorFlow – Used for implementing deep neural network algorithms and techniques. 
  • NLTK – Used for natural language processing purposes. 

How to get started with Machine Learning Python?

Note: Before starting Machine Learning with Python, you should have a good understanding of Python, statistics, and machine learning algorithms.
Now moving on to Machine Learning with Python, it is recommended to use Jupyter Notebook for programming purposes. So, you should also have an idea of how to use the Jupyter Notebook.
The next thing is mastering the packages used for Machine Learning in Python which is mentioned above and learning how to deal with data. Once you are done with the above steps you can start building your projects for better understanding.

Conclusion 

         We have covered some of the most important things required to start your Machine Learning with Python journey. We learned about Machine Learning, its application, Python, why Machine Learning with Python, and how to begin this whole process. 
We hope you learned what you were looking for. Do reach out to us for queries on our AI dedicated discussion forum and get your query resolved within 30 minutes. 
    
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