Data Science, Machine Learning and Artificial Intelligence are offering a lucrative career to programmers with high pay and exciting work. Python can be a great opportunity for programmers. Specially for those, who are willing to learn these new skills and upgrade themselves.
Machine Learning
Machine learning is just a part of data science but often a big part of a “data science” project.Machine learning algorithms are algorithms that learn (often predictive) models from data. Instead of formulating “rules” manually, a machine learning algorithm makes it easy to learn the model.
Machine learning algorithms help you formulating the complex rules. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.Machine learning tasks are classified into several broad categories. In supervised learning, the algorithm builds a mathematical model from a set of data that contains both the inputs and the desired outputs.The processes involved in machine learning are similar to that of predictive modeling and data mining and. Both processes involved in machine language require searching through data to look for patterns and adjusting program actions accordingly.
How machine learning works
Machine learning algorithms can be categorized as supervised and unsupervised. Supervised algorithms require a data scientist or data analyst with machine learning skills who provides both input and desired output. Data scientists determine which variables, features and the model should analyze to develop predictions.The algorithm will apply what was learned to new data once the training is complete, .
While in other hand, unsupervised algorithms are not required to be trained with desired outcome data. Instead, unsupervised algorithms use an iterative approach which are called deep learning to review data and arrive at conclusions.
Data Science
Data Science can be defined as a mix of varied tools, algorithms, and machine learning principles with the goal to find hidden patterns from the raw data. 
Data science is the study of information system. The data science usually covers the study of information comes from, what it represents and how it can be turned into a valuable resource in the creation of IT and business strategies. Traditionally the data used to be small in size and mostly structured but today most of the data is unstructured or semi structured. The figure shows that by 2020, more than 80 % of the data will be unstructured.
The future of Data science is awesome and it has become so popular in recent days. The typical skills of a data scientists are
- Computer science: programming, hardware understanding, etc.
- Math: Linear algebra, calculus, statistics
- Communication: visualization and presentation
- Domain knowledge
Data Science and Machine Learning with Python
In technical terms, Python is an object-oriented, high-level programming language with dynamic semantics.Python is very simple and easy to learn.Python is relatively simple. It’s easy to learn and easy to understand since python requires a unique syntax that focuses high on readability. You can read our previous blog post , Artificial Intelligence with Python to get more knowledge about the importance of Python in AI.
Python has gathered a lot of interest and developers give it a high priority recently as a choice of language for data analysis. Here are some reasons which go in favor of learning Python for Data science and machine learning purpose:
- Open Source – free to install
- Awesome online community
- Very easy to learn
- Can become a common language for data science and production of web based analytics products.
Being a data science tool, Python helps to explore the concepts of machine learning in the best way possible. Machine Learning is all about mathematical optimization, probability and statistics. The learning methods are all made easy by Python. Below are some features of Python that describes the importance of Python in Data Science and Machine Learning in a best way.
Easy to learn
Python is easy to learn and easy to code.That’s why it’s so much popular among developers.You need to follow three simple steps for writing code and text within a web page’s context using Jupyter after installing Python.
- install pip3
- install Juypter
- generate the config: jupyter notebook –generate-config
Extremely Scalable
Python is successful in recent days due to the availability of data science libraries for aspiring candidates. These libraries are always up to date and also has a wide range of support community.
Many libraries in Python are available to perform data analysis, below are the reasons to choose Python for data analysis.
- NumPy is important to perform scientific computing with Python. It helps to handle high-level mathematical functions to operate on multi-dimensional arrays and matrices.
- SciPy works in association with NumPy arrays and offers effective routines for numerical integration and up-gradation.
- Pandas, also developed on top of NumPy, delivers data structures and operations to change numerical tables and time series.
- Matplotlib is a 2D plotting library. It offers data visualizations in the form of histograms, power spectra, bar charts, and scatterplots with minimal coding lines.
Developed on above scope, Scikit-learn acts as a machine learning library that leads to classification, regression, and clustering algorithms that involve support vector machines, logistic regression, naive Bayes, random forests, and gradient boosting.
The growth of Python is due to its support community.Python is easy, simple, powerful, and innovative due to its broader usage in a variety of contexts.Python offers many visualization options for developers. Developers can understand data easily, work with charts, use graphical plot and develop web-ready plots with the help of data visualization packages in python. The increased number of volunteers are developing Python libraries as Python has extended its reach to the data science community.
Conclusion
Python is a very popular language due to its simplicity. It’s easy to learn and easy to code. The recent version has improved its data visualization. It has powerful packages like NumPy, SciPy, Pandas , Pybrain,Scikit-Learn libraries.Python is known for making programs work in the least lines of code. Python automatically identifies and associates data types and follows an indentation based nesting structure. That’s why, Python proves itself a very strong contender in Data science and Machine Learning environment.
