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Introduction to Machine Learning with Python

by Paresh Bramhane
Machine Learning with Python

Every time you begin to type something into Google, it somehow reads your mind and completes your search query. No marks for guessing if you correctly said that machine learning with python made this possible. Like many leading companies globally, Google employs machine learning with python models to create solutions that were either not possible at all earlier or not economically possible. 

Machine Learning with Python

Machine learning, a subset of AI (Artificial Intelligence), is one of the trending topics in the world. Machine Learning with Python promises to solve a lot of problems in future. Even today, it has been applied in almost every domain. From an image recognition system to a recommendation system for Netflix, machine learning makes it happen. It may seem that the traditional methods of programming can also provide such solutions, but past attempts have revealed otherwise. 

Imagine having to write a code to identify a dog in a picture. You would have to consider every feature of the dog, including the curves and shapes of different breeds and age groups. It doesn’t end there. Imagine having to write code to identify a dog in black and white pictures or pictures taken so that only a part of the dog is visible. The combinations are far too many to be practically and economically handled through traditional coding methods. However, machine learning works differently. To put it in an oversimplified way, the code written in a language like Python is trained on a set of data. Once the code has learnt to identify a pattern in the data, it’s then tested on another set of similar data. This testing continues till the results are aligned with the business requirement, and then the system is deployed. This is, obviously, a long process and not necessarily a linear one. It’s common to see machine learning models that don’t perform as required or the data set used for training the models has gone through some change, and new data needs to be acquired. A machine learning project requires proper planning and a team of professionals, including domain experts. Domain experts may not be machine learning professionals, but they are the ones who provide the guidelines related to data and the problem that’s been worked on.

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Machine learning with Python has been particularly successful because of the development that has taken place in the Python ecosystem. The community has been proactively developing libraries that implement machine learning algorithms. Once the algorithm is written and made public, you just have to use them as per your requirement. Consider the popular machine learning library, sci-kit learn, which implements the decision tree classifier. Going back to our program that identifies a dog in a picture, the decision tree algorithm can be trained to identify and ‘learn’ about the pattern in data. Since the sci-kit learn library already has the decision tree algorithm, we just need to know how to use it as per our program requirement.

Python has a vast collection of packages that make machine learning in Python one of the most preferred in the industry. The NumPy library, for instance, simplifies mathematical or scientific calculations. Another numerical library created by Google is Tensorflow. It is primarily used for deep learning applications like handwritten digits, face recognition, speech recognition. Even RankBrain, the Google deployment for page ranking system, uses Tensorflow. Another one is Matplotlib. As the name suggests, it’s used in 2D and 3D plotting. 

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The first step to successfully learn machine learning in Python is to start with the basics. Get started with Python and do small projects in the language. Once you have a certain comfort level, start with “Hello World” projects in machine learning. The idea is to get over the hitch of learning a new technology before the hitch forces you to ditch the learning path altogether. Like any new field, machine learning has a lot of new concepts and processes. Even if you have been a programmer, the machine learning methodology may not come naturally to you. Machine learning with Python has got its charm, and a lot of traditional software developers move into this field with some additional training. The skills that a machine learning professional should have include: 

  • Problem-solving
  • Teamwork
  • Probability and Statistics
  • Programming
  • Data Modeling
  • Communications Skills

Machine learning is a very popular career choice these days with excellent growth prospects. Almost all industries have opportunities for machine learning professionals as algorithms are being used productively. There are various career options in the domain, from developing an algorithm for specific problems to designing human-centred machine learning models. You could also be in a business-related role as machine learning is a broad field and requires professionals from various backgrounds. 

The demand for professionals in machine learning has been growing in financial services, IT-related companies, defence, and automobile companies. This brings us to the end of the blog on machine learning with Python. We hope that you were able to gain valuable insights from the same. 

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