Elements of Data Science, Machine Learning and Artificial Intelligence

  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...

  Deep Learning my takeaway from the from the book by Goodfellow, Bengio and Courville Introduction Today, artificial intelligence (AI) is a thriving field with many practical applications and active research topics. The true challenge to artificial intelligence proved to be solving the tasks that are easy for people to perform but hard for people to describe formally—problems that we solve intuitively, that feel automatic, like recognizing spoken words or faces in images . By gathering knowledge from experience, this approach avoids the need for human operators to formally specify all of the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones. If we draw a graph showing how these. concepts are built on top of each other, the graph is deep, with many layers. For this reason, we call this approach to AI deep learning . Several artificial intelligence projects have sought to hard-code ...

Let us continue with our journey further with the AI, a modern approach.   In this chapter we look into how an AI agent achieves a goal with a sequence of actions. These types of agents are also known as problems solving agents. Before this we will just look at the diagram below.   In an atomic representation each state of the world is indivisible, it has no internal structure. A factored representation splits up each state into a fixed set of variables or attributes, each of which can have a value. Structured representations underlie relational databases and first-order logic. The axis along which atomic, factored, and structured representations lie is the axis of increasing expressiveness. Roughly speaking, a more expressive representation can capture, at least as concisely, everything a less expressive one can capture, plus some more.   PROBLEM-SOLVING AGENTS   Whenever we have to solve a problem, what has to be achieved, has to be analyzed in the first plac...