Machine Learning with Python

27th Apr 2020
| admin |

If you want to shape your career as a Machine Learning Engineer, Data Scientist or Analyst, spare a few minutes to know about the discipline itself, scope of opportunities in Machine Learning, training and certifications, and prerequisites at SpireTec Solutions, a leading technology management training company with clients as big as United Technologies, Connexus Energy, Canon, and WFP (World Food Programme) United Nations.

What is Machine Learning?

Machine Learning or ML, in simple terms, is the ability of machines to learn from data and experience without being specially programmed to accomplish tasks ranging from very simple (e.g. Regression-a mathematical prediction) to highly complex (e.g. Image recognition or robotics). Thus, Machine Learning is nothing but Artificial Intelligence (AI). The machine knows its roles and responsibilities based on the conditions, past experiences and behaviour.

What is Python?

Python is an interpreted, object-oriented, high-level and dynamically typed programming language. Initially developed by Guido Van Roussum, its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. In today’s world Python is explicitly used for Data science/data analysis, Web development and Machine learning.

Popularity of Python

Python is simple and easy to learn. The language has useful libraries and frameworks explicitly designed for Machine Learning. Great readability of its syntaxes not just reduces the time of programming but also reduces the time for re-engineering, optimization, and maintenance as and when required in the product life-cycle. Here is a popularity trend over the past 5 years revealing the supremacy of Python over C++ and Java.

Career Opportunities

We are at the dawn of the fourth industrial revolution, where insane amount of data is produced every data. To get this into perspective consider this, from the dawn of the civilization till 2003 we produced about 5 exabytes of data but now that much data is produced every 2nd day!

Understanding the worth of data not just the world’s tech giants like Google, Facebook, and Amazon but also start-ups and small and medium businesses are investing in ways to untap the hidden potential of data. Two popular job profiles for candidates with skills of Machine Learning using Python are as follows:

  • Data scientist/Analyst


Understanding of ML concepts and algorithms; internals and proscons, probability and combinatorics, strong fundamentals of data structures, correctness and runtime analysis, familiarity with space-time-accuracy tradeoffs;


You should have hands on programming skills and write good quality code for a given requirement in RPython. Knowledge of SQL is required. Familiarity with distributed computing architecture like Spark, Map-Reduce paradigm and Hadoop makes job easier.

Besides, it demands problem solving approach and inclination and ability to pick up new techniques and technologies.

The average salary of a data scientist is $96000 per annum and that of a beginner Data scientist is $85000(according to PayScale).

  • Machine Learning Engineer

Applying Artificial Intelligence to create world class solutions that help organizations, its partners or customers conquer problems they come across in their business or day to day life, address technical challenges using AI Platforms and technologies, and stay ahead of the competition. Machine Learning professionals build computer programs that change when exposed to new data.

Machine Learning Training, Course, Certification

SpireTec Solutions offers Machine Learning with Python Training courses to help you create, train, deploy ML models, solve numerical problems, and much more. Live Virtual Training, On-site Classroom Training and Customized Training options are available. The prerequisites are basic knowledge of artificial intelligence, Python, NumPy, Scikit-learn, Scipy, and Matplatlib. The course has nine modules and classes are available 5 days a week.

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