Basic Programming Skill

About Course


WWWIn the digital era it is very much required to acquire basic computer programming skills for all other branches apart from IT and Computer. With Basic Programming Skill, we provide the necessary language exposure like C. C++, JAVA/Dot Net and various application based learning of Algorithm and Data science for other branch students. The list of skills includes the base-line requirements for generic roles in the computer science field.

About Faculty:

Target Audience:

Interdisciplinary branch (Where it is not offered in regular curriculum)

Duration:60 Hour

Course:

Module No Topic No Content
1 1 Introduction to Artificial Intelligence, Introduction to Machine learning, History of AI, Proposing and evaluating AI application.
1 2 Problem space search, heuristic search strategies, search and optimization, planning and scheduling
1 3 Case study: Game Playing EX. Chess /Board Game , TSP
1 4 Implement a Man VS Machine Cross Board application (Hint. Index table)
2 1 Python GUI Programming, Tkinter Widgets, Standard Attributes, Container
2 2 Python Object Oriented Class, Object, Method, Constructors, Private and Public Members
2 3 Python Object Oriented Inheritance, Types of Inheritance, Method overriding
2 4 Python Object Oriented Function Overloading, Operator Overloading. Python required libraries , Python applications
3 1 Introduction to Data Science & Machine Learning Python for data science and machine learning, Introduction to Numpy, Numpy functions.
3 2 Introduction To Pandas For EDA Exploratory Data Analysis (EDA) using pandas, pandas functions like read_csv, read_excel etc. Handling missing values using pandas.
3 3 Data Visualization Using Matplotlib & Seaborn Introduction to Matplotlib & Seaborn. Data Visualization using matplotlib and seaborn. Different plots like barplot,countplot, hist plot etc.
3 4 Machine Learning Algorithms Using SKlearn Introduction of Machine learning. Supervised and unsupervised learning, Introduction to Sklearn machine learning library. Different statistical methods using Sklearn like linear regression, logistic regression, KNN etc.
4 1 Case Study 1: Machine Learning Project 1 1. Iris Flower classification Using ML: https://iris-new-app pb.herokuapp.com/
4 2 Mini Project 1
4 3 Machine Learning project for Music classification The goal is to create a machine learning model that how to analyze an audio signal in Python. We shall then utilize the skills learnt to classify music into different genres.
4 4 Mini Project