Anurag RANA

Normalization   is a process of organizing the data in database to avoid data redundancy, insertion anomaly, update anomaly & deletion anomaly.  Anomalies in DBMS There are three types of anomalies that occur when the database is not normalized. These are – Insertion, update and deletion anomaly. Let’s take an example to understand this. Example : Suppose a manufacturing company stores the employee details in a table named employee that has four attributes: emp_id for storing employee’s id, emp_name for storing employee’s name, emp_address for storing employee’s address and emp_dept for storing the department details in which the employee works. At some point of time the table looks like this: emp_id emp_name emp_address emp_dept 101 Nikhil Kangra D001 101 Nikhil Kangra D002 123 Ashish Shimla D890 166 Rahul Pathankot D900 166 Rahul Pathankot D004 The above table is not normalized.  Update anomaly : In the above table we have two rows for employee Nikhil as he belongs to two departm

To learn machine learning, we will use the Python programming language in this tutorial. So, in order to use Python for machine learning, we need to install it in our computer system with compatible  IDEs (Integrated Development Environment) . In this topic, we will learn to install Python and an IDE with the help of  Anaconda distribution . Anaconda distribution is a free and open-source platform for Python/R programming languages. It can be easily installed on any OS such as Windows, Linux, and MAC OS. It provides more than 1500 Python/R data science packages which are suitable for developing machine learning and deep learning models. Anaconda distribution provides installation of Python with various IDE's such as  Jupyter Notebook ,  Spyder ,  Anaconda prompt ,  etc . Hence it is a very convenient packaged solution which you can easily download and install in your computer. It will automatically install Python and some basic IDEs and libraries with it. Below some steps are given

Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for  building mathematical models and making predictions using historical data or information . Currently, it is being used for various tasks such as  image recognition ,  speech recognition ,  email filtering ,  Facebook auto-tagging ,  recommender system , and many more. This machine learning tutorial gives you an introduction to machine learning along with the wide range of machine learning techniques such as  Supervised ,  Unsupervised , and  Reinforcement  learning. You will learn about regression and classification models, clustering methods, hidden Markov models, and various sequential models. What is Machine Learning In the real world, we are surrounded by humans who can learn everything from their experiences with their learning capability, and we have computers or machines which work on our instructions. But can a machine also learn