Elements of Data Science, Machine Learning and Artificial Intelligence

Python Characteristics of Python: Easy to code and debug  Easy in readability, various available resources Open source, std. libraries. IDE easily available R or Python. Only tool for communication. Output important Data Analysis both R and Python easy OOP-> Python Easily installable from the Python website More easier version is Anaconda, best for all the areas Jupyter note book We use python and its standard libraries.  NumPy -> Helps in multidimensional array handling Scipy -> Statistical model and analysis Matplotlib -> Visualization packages Pandas -> Array handling and dataframes handling *NumPy basic operation: NumPy: Fundamental package for scientific computing with python. Powerful N dimensional array object. An array can either be a vector or matrix. By NumPy we can create both matrix or vector arrays Arbitrary datatypes can be defined. This allows NumPy to handle and integrate wide databases. The package should always call the python kernel when sessi...

  Linked Lists: Series of nodes, each node has a single pointer to the next node and in the last node's case a null pointer representing that there are no more nodes in linked list. Linked list will always maintain head and tail pointers so that insertion at either head or tail of the list is a constant time operation. Random insertion is excluded from this and will be a linear operation. Insertion - O(1) Deletion - O(n) Searching - O(n) Here we chose to always maintain pointers to the nodes at the head and tail of the linked list and so performing a traditional insertion to either front or back of linked list is O(1) operation. An exception to this rule is performing insertion before a node that is neither the head nor the tail in a singly linked list. When the node we are inserting before is somewhere in the middle of the linked list (the random insertion) the complexity is O(n). In order to add before the designated node we need to traverse linked list to find that node's cu...

 Short overview of Data Science: Due to increase in the mobiles, cloud data etc. the source of Data has increased a lot. Evolution of technology, social media data, iot data has impacted a lot in the increase of data sources. Moreover the online shopping data, education, banking and finance, media digitalization has increased the overall impact. Data Science focuses on processing and analysis of the data. Data science is a process of extracting the knowledge and insights form the data by using scientific methods. Data Science-> Programming + Statistics + Business Torture the data and it will confess to anything.  Data Scientist   must be good inn Math, technology and the business Skillset required: Statistics, Programming languages, Data Extraction and processing, Data wrangling and extrapolation, Machine Learning, Big Data processing frameworks, Data Visualization. Statistics: Math for understanding the data Programming: R, Python with sophisticated algorithms an...

 ML Introduction: Data Science and Machine learning: Definition: Data Science:  Extracting hidden patterns from huge datasets. Patterns- Trends, association, hidden rules, clusters. example temperature rises ice-cream melts Science- Here it means statistics and mathematics not physical science Data sets usually refer to large volume of cleansed, structured data prepared for analysis. Science refers to statistical tools and techniques employed to understand data and reliability of the identified patterns. A] That part of statistics which is used to understand the data is called descriptive statistics.  Descriptive statistics gives vital insights into data in terms of central values, spread and distribution shape of data. B] Part of statistics which is used to establish the reliability of the potential patterns identified is called inferential statistics. Machine learning: Machine learning is an integral and critical part of Data science. It refers to a collection of algori...