Machine Learning

• It is simply making a computer perform a task without explicitly programming it.
• Nowadays in world every system that does well has a machine learning algorithm at its heart.
• For example, Amazon Product recommendations, Google Search Engine, Facebook, LinkedIn, etc.
• They are efficiently utilizing data collected from various channels which helps them to get a bigger picture of what should they do and what they are doing.

Deep Learning

• It is a class of machine learning algorithms which uses multiple layers to progressively extract higher level features from the raw input.
• For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as letters or faces or digits.
• Smart developers choose python because it is particularly suitable for Deep Learning projects because of its simple syntax and make the language accessible to non-programmers. 

Predictive Analytics

• It is the branch of the advanced analytics which encompasses a variety of statistical techniques from machine learning, data mining and predictive modelling.

Advanced Analytics

• It is a part of Data Science that uses high-level methods and their tools to focus on projecting future events, behaviors and trends.
• For advanced analytics data mining is a key aspect of providing the raw data that will be used by both big data and predictive analytics.
• Pandas is the Python Data Analysis Library, used for everything from importing data from Excel spreadsheets to processing sets for time-series analysis.

Exploration and Data Analytics

• It is a phenomenon under data analysis used for gaining a better understanding of data aspects like variables, relationships that hold between them and main features of data.
• Another side benefit of Exploration and Data Analytics is to refine your selection of feature variables that will be used later for machine learning.

Data Science

• It is the study of the data which involves developing methods of recording, storing, and analyzing data to effectively extract useful information.

Statistics

• Python is a general-purpose language with statistics modules in which R has more statistical analysis features than Python, and specialized syntaxes.

Improved Productivity

• Python is a very productive language and they don’t need to spend too much time in understanding the behavior or syntax of the programming language.

Interpreted Language

• It is an interpreted language which means that Python directly executes the code line by line.

Dynamically Typed

• Python doesn’t know the type of variable until we run the code and during execution it automatically assigns the data type.

Free and Open-Source

• Python comes under the OSI approved open-source license then it makes free to distribute and use.
• We can download the source code, modify it and even distribute your version of Python.

Vast Libraries Support

• The standard library of Python is huge, we can find almost all the functions needed for our task so, we don’t have to depend on external libraries.

Portability

• To run program in many languages like C/C++, we need to change our code in different platforms.
• In python we only write once and executes it anywhere.

Disadvantages of Python

Slow Speed

• Python is an interpreted language and dynamically typed language where the line-by-line execution of code often leads to slow execution.

Not Memory Efficient

• Python has to do a little tradeoff, to provide simplicity to the developer.
• This programming language uses a large amount of memory and it can be a disadvantage while building applications when we prefer memory optimization.

Weak in Mobile Computing

• In server-side programming python is generally is used.
• Compare to other programming languages it has slow processing power and it is not memory efficient.

Database Access

• Python is easy and stress-free programming and it lacks behind, when we are interacting with the database.
• The Python’s database access layer is primitive and underdeveloped in comparison to the popular technologies like JDBCand ODBC.

Runtime Errors

• The data type of a variable can change at anytime because python is dynamically typed language.
• In future a variable containing integer number may hold a string which can lead to Runtime Errors.

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