A data analysis technique that introduces automation in the development of an analytical model is termed as Machine Learning. Algorithms are employed by machine learning which draws insight from the data, so it provides computers with the capability of attaining knowledge without being easily programmed. Machine learning’s main area of focus is on the building of computer programs that can sustain themselves and enhance when disclosed to more advanced data.

Guido van Rossum created python when he was developing an interpreter for an advanced scripting language. Python is placed under open-source technologies. Python has numerous advantages, for example, virtual environments of python. If you have right blend of libraries and tools, you can develop anything with python. Python programming language is used in different areas including

  • Backend web development
  • Artificial Intelligence
  • Scientific computing
  • Social News Networking and so on.

Python occupies 4th place in TIOBE Index where there are 100 other programming language competing along with it. Research activities on python language are being performed on a large scale and it is getting updated each year which indicates its importance. This indicates the importance of python in the world of programming languages.

Want to be an Expert in Machine Learning with Python?

Taking the first step or getting started to achieve something is the hardest one because we will be confused in which way we have to move forward and we might not be having many options.

learning skills                                                      Image raining the question of where to begin?

If you are not an expert in machine learning or python, the process explained below explains machine learning in python.

  1. Basic skills of Python

Having some basics in python is essential to gain the knowledge of machine learning. Python is used significantly in machine learning. Installation of python should be done first. Installing Anaconda is a better choice as scientific computing and machine learning packages are installed at some point,. Anaconda is an industrial implementation of Python for Windows, OSX, Linux and for packages essential for machine learning including matplotlib, scikit-learn and numpy. It also includes Ipython Notebook as well.

  1. Skills in Foundational Machine Learning

The work which data scientists do involves much of machine learning skills. The good news is that you don’t need to possess high-level understanding of the theoretical aspects of machine learning. So, acquiring basic skills in machine learning is the thing you must do after gaining some basics of python.

  1. Overview of Scientific Python Packages

After acquiring basic knowledge on python programming and understanding a bit about machine learning, it is better to refer open source libraries which make the practical machine learning tasks much easier to understand. Some of the open-source libraries are scikit-learn, matplotlib, numpy, panda and so on.

Numpy: This is useful for its array objects which are N-dimensional

Pandas: Data analysis library for python which consists of structures such as data-frames

Matplotlib: Publication quality figures producing 2D plotting library.

Scikit-learn: Machine learning algorithms which are used for tasks such as data analysis and data mining

  1. Let’s get started with Machine Learning in Python

After getting an insight on python basics, machine learning fundamentals, and open-source libraries, next step is to implement machine learning algorithms with scikit-learn, which is python’s de facto standard library for machine learning.

scikit-learn flowchart

                                                                             The flowchart of scikit-learn

The environment for executing python known as Ipython Notebook can be viewed online or downloaded and can be interacted on individual computer locally. Clear understanding of scikit-learn is necessary to proceed further to the next steps.

  1. Machine Learning topics with Python

After acquiring a clear understanding of scikit-learn, proceed further in exploring the various useful algorithms. One of the most famous machine learning algorithms is k-clustering. This algorithm is an effective and simple method for solving learning problems which are unsupervised.

  1. Study of Machine Learning topics with Python which are Advanced

After obtaining knowledge on scikit-learn, it is preferable to explore some more advanced topics. Better to start with vector machines which is a linear classifier that relies on transformations of data which are complex into a space with higher dimensionality. Reduction of the number of variables which are being considered in a problem is termed as dimensionality reduction. Principal component analysis is a type of unsupervised dimensionality reduction.

  1. Gaining in-depth knowledge of Python

python learning

                                                                  Image showing that learning is deep

Recent advancements in the past several years have increased the hidden power of and general interest in deep neural networks. If anyone is not familiar with deep learning, there are many websites offering numerous articles on the accomplishments, innovations and accolades of the technology.

So, if you follow these 7 steps, you can get a clear understanding of machine learning algorithms and the implementation of the algorithms using python’s popular libraries.


Machine learning is being widely implemented in Google. Google’s systems have been replaced by machine learning and deep learning techniques in the past few years. Their patented algorithm ‘PageRank’ which was the reason for their initial success is being replaced by ‘RankBrain’ algorithm which is based on deep learning. Numerous job opportunities are available on machine learning with python in the fields of banking and financial services, healthcare, retail and so on as it’s algorithms are replacing all the famous and native algorithms and due to unique features of python and its libraries through which the machine learning algorithms are implemented.

The uses of machine learning which are discussed above are just a particle of sand in a desert. Machine learning has wide range of applications in almost every domain. So, the salaries for the professionals with expertise in machine learning with python are sky-high. People who want to have a career of machine learning with python can receive machine learning and python training. Training enables them to gain expertise in the fields and gain better practical knowledge.