Data Science

Professional Certification Course to enter in IT Industry with Knowledge and Experience

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Complete Course Curriculum

Course Duration: 6- Months

Course Fees: 30000/-

Data Toolkit

  • Understanding The
    Upgrad Coding Console
  • Basics Of Python
  • Data Structures In
    Python
  • Control Structure And
    Functions In Python
  • Oop In Python

  • Logic and syntax
    building
  • Data structures: lists,
    strings, dictionaries, and
    stacks
  • Time complexity
  • Searching and sorting
  • Two pointers
  • Recursion

  • Introduction to numpy
  • Introduction to
  • Matplotlib
  • Introduction to pandas
  • Getting and cleaning data

  • Introduction to data
  • Visualization
  • Data visualisation using seaborn

  • Data sourcing
  • Data cleaning
  • Univariate analysis
  • Bivariate analysis and multivariate analysis

  • Problem statement
  • Evaluation rubric
  • Final submission
  • Solution

  • Basics of probability
  • Discrete probability
    distributions
  • Continuous probability
    distributions
  • Central limit theorem

  • Concepts of hypothesis
    testing - i: null and
    alternate hypothesis,
    making a decision, and
    critical value method
  • Concepts of hypothesis
    testing - ii: p-value method
    and types of errors
  • Industry demonstration of hypoproportionting:
    two-sample mean and
    proprotion test, a/b
    testing

  • Database design
  • Database creation in mysql workbench
  • Querying in mysql
  • Joins and set operations

  • Window functions
  • Case statements, stored
    routines and cursors
  • Query optimisation and
    best practices
  • Problem-solving using sql

  • Problem Satement
  • Evaluation Rubric
  • Final Submission
  • Solution

Machine Learning

  • Simple Linear Regression
  • Simple Linear Regression
    In Python
  • Multiple Linear
    Regression
  • Mutliple Linear
    Regression In Python
  • Industry Relevance Of
    Linear Regression

  • Problem Statement
  • Evaluation Rubric
  • Final Submission
  • Solution

  • UNIVUnivariate Logistic
    Regression
  • Multivariate Logistic
    Regression: Model
    Building And Evaluation
  • Logistic Regression:
    Industry Applications

  • Introduction To
    Clustering
  • K-Means Clustering
  • Hierarchical Clustering
  • Other Forms Of
    Clustering: K-Mode,
    K-Prototype, Db Scan

  • Introduction To Business
    Problem Solving
  • Business Problem
    Solving: Case Study
    Demonstrations

  • Problem Statement
  • Evaluation Rubric
  • Final Submission
  • Solution

  • Problem Statement
  • Evaluation Rubric
  • Final Submission
  • Solution

Deep Learning

  • Introduction To Decision Trees
  • Algorithms For Decision Tree Construction
  • Truncation And Puning
  • Random Forests

  • Introduction To Boosting And Adaboost
  • Gradient Boosting

  • Generalized Linear Regression
  • Regularized Regression

  • Problem Statement
  • Evaluation Rubric
  • Final Submission
  • Solution

  • Prinicipal Component Analysis And Singular Value Decomposition
  • Principal Component Analysis In Python

  • Introduction To
    Time Series And Its
    Components
  • Working With Stationary
    Time Series
  • End-To-End Analysis Of
    Time Series

Deep Learning And Neural Networks

  • Structure Of Neural Networks
  • Feed Forward In Neural Networks
  • Backpropagation In Neural Networks
  • Modifications To Neural Networks
  • Hyperparameter Tuning In Neural Networks

  • Introduction To Convolutional Neural Networks
  • Building Cnns With Python And Keras
  • Cnn Architectures And Transfer Learning
  • Style Transfer And Object Detection
  • Industry Demonstration: Using Cnns With Flowers Images
  • Industry Demonstration: Using Cnns With X-Ray Images

  • Problem Statement
  • Evaluation Rubric
  • Final Submission
  • Solution

  • What Makes A Neural Network Recurrent
  • Variants Of Rnns: Bidirectional Rnns And Lstms Building
  • Rnns In Python

  • Two Architectures: 3d Convs And Cnn-Rnn Stack
  • Understanding Generators
  • Starter Code Walkthrough
  • Problem Statement And Final Submission

Natural Language Processing

  • Introduction To Decision Trees
  • Algorithms For Decision Tree Construction
  • Truncation And Puning
  • Random Forests

  • Principles Of Model Selection
  • Model Evaluation
  • Model Selection: Best Practices

  • Prinicipal Component Analysis And Singular Value Decomposition
  • Principal Component Analysis In Python

  • Problem Statement
  • Evaluation Rubric
  • Final Submission
  • Solution

  • Introduction To Boosting And Adaboost
  • Gradient Boosting

  • Problem Statement
  • Evaluation Rubric
  • Final Submission
  • Solution

  • Introduction To Nlp
  • Basic Lexical Processing
  • Advanced Lexical Processing

  • Introduction To Syntactic Processing 
  • Parsing
  • Information Extraction
  • Conditional Random Fields

  • Problem Statement
  • Evaluation Rubric
  • Final Submission
  • Solution

  • Understanding Neural Networks
  • Loss Functions And Back Propagation
  • Understanding Tensorflow
  • Case Study : Imdb Movie Review Classification

  • Introduction To Semantic Processing
  • Distributional Semantics
  • Topic Modelling

  • Problem Statement
  • Evaluation Rubric
  • Final Submission
  • Solution

Business Analytics

  • Introduction To Decision Trees

  • Algorithms For Decision Tree Construction

  • Hyperparameter Tuning In Decision Trees

  • Ensembles And Random Forests

  • Introduction To Time Series And Its Components
  • Smoothing Techniques
  • Introduction To Ar Models
  • Building Ar Models

  • Problem Statement
  • Evaluation Rubric
  • Final Submission
  • Solution

  • Principles Of Model Selection
  • Model Building And Evaluation
  • Feature Engineering
  • Class Imbalance

  • Excel Functions
  • Data Analysis In Excel
  • Advanced Tools And Visualisations

  • Data Exploration In Tableau
  • Visualising And Analysing Data In Tableau With Basic Plots

  • Problem Statement
  • Evaluation Rubric
  • Final Submission
  • Solution

Business Requirements

  • ProbleIntroduction To Structured Problem Solving
  • Interviewing And Frameworks - I: 5w And 5whys
  • Interviewing And Frameworks - Ii: Spin
  • Industry Demonstrations On Frameworks
  • Understanding Business Model Canvas And Issue Tree Framework
  • Industry Demonstrations On Issue Tree Framework
  • Specialized Frameworks For Business Problems:
  • ps, 5cs, Etc.

  • Problem Statement
  • Evaluation Rubric
  • Final Submission
  • Solution

  • Introduction & Concepts Of Optimisation
  • Optimisation Using Excel
  • Optimisation Using Python
  • Or In Industry - Warehouse Problem, Assignment Problem, Jobshop Scheduling, Etc.

  • Introduction To Data Storytelling
  • Components Of A Good Story With Data - Understanding Your Stakeholder And Stakeholder Empathy, Levels Of Details For Different Stakeholders - Cxo/Leadership Vs Team Presentations, Visuals, Etc.
  • Golden Rules For Data Storytelling

Business Intelligence/
Data Analytics

  • Database Design Recap
  • Building Blocks Of Data Modelling
  • Problem Solving Using Data Modelling
  • Data Modelling: Optional Assignment

  • Window Functions
  • Case Statements, Stored Routines, And Cursors
  • Query Optimisation And Best Practices
  • Problem Solving Using Sql

  • Excel Functions
  • Data Analysis In Excel
  • Advanced Tools And Visualisations

  • Introduction To Nosql Databases And Mongodb
  • Querying In Mongodb
  • Aggregation In Mongodb-I
  • Data Modelling In Mongodb
  • Indexing In Mongodb
  • Aggregation In Mongodb-Ii
  • Replication And Sharding

  • Big Data And Cloud Computing
  • Amazon Web Services
  • Big Data Storage And Processing - Hadoop
  • Emr Cluster In Aws

  • Advanced Lexical Processing
  • Introduction To Hive
  • Basic Hive Queries
  • Advanced Hive Queries

  • Problem Statement
  • Evaluation Rubric
  • Final Submission
  • Solution

Advanced Visualisations

  • Data Exploration In Tableau
  • Visualising And Analysing Data In Tableau With Basic Plots
  • Advanced Visualisations Using Tableau - I: Lod Expressions, Hexbin Charts, Sankey Diagrams, Waterfall Charts, Etc.
  • Advanced Visualisations Using Tableau - II: Pareto Charts, Bullet Graphs, Highlight Tables, Etc.
  • Case Study: Visualising Kpis

  • Problem Statement
  • Evaluation Rubric
  • Final Submission
  • Solution

  • Powerbi: Introduction And Setup
  • Visualising And Analysing Data In Powerbi
  • Data Transformations Using Powerbi
  • Making Interactive Dashboards With Powerbi

  • Introduction To Plotly
  • Basic Visualisations In Plotly
  • Advanced Visualisations In Plotly

  • Introduction To Data Storytelling
  • Components Of A Good Story With Data - Understanding Your Stakeholder And Stakeholder Empathy, Levels Of Details For Different Stakeholders - Cxo/Leadership Vs Team Presentations, Visuals, Etc.
  • Golden Rules For Data Storytelling

  • Problem Statement
  • Evaluation Rubric
  • Final Submission
  • Solution

Data Engineering

  • 4vs Of Big Data

  • Big Data: Industry Case Studies

  • Introduction To Cloud
  • Aws Setup

  • Concepts Retailed To Distributed Computing
  • Hadoop Distributed File System
  • Mapreduce Programming In Python

  • Enterprise Data Management
  • Relational Database Modelling
  • Normal Forms And Er Diagrams

  • Introduction To Data Ingestion
  • Structured Data Ingestion With Sqoop
  • Unstructured Data Ingestion With Flume

  • Fundamentals Of Apache Hive
  • Writing Hql For Data Analysis
  • Partitioning And Bucketing With Hive

  • Data Warehousing With Redshift
  • Analyze Data With Redshift

  • Spark Architecture
  • RDD, Dataframe API,Sparksql

  • Introduction And Problem Statement
  • Grading Rubrics And Submission

  • The Aws Cloud Platform
  • Building And Deploying Virtual Machines
  • Aws Cloud Storage Solutions
  • Application Deployment
  • Cloud Administration And Security
  • Load Balancing And Backup Strategies
  • Cloud Automation

  • Running Spark On Multinode Cluster
  • Spark Memory & Disk Optimisation
  • Optimising Spark Cluster Environment

  • Introduction To Apache Flink
  • Batch Data Processing With Flink
  • Stream Processing With Apache Flink
  • Sql Api

  • Intro To Real-Time Data Processing Architectures
  • Fundamentals Of Apache Kafka
  • Setting Up Kafka Producer And Consumer
  • Kafka Connect Api & Kafka Streams

  • Spark Streaming Architecture
  • Spark Streaming Apis
  • Building Stream Processing Application With Spark
  • Comparision Between Spark Streaming And Flink

  • Fundaments Of Airflow
  • Workflow Management With Airflow
  • Automating An Entire Data Pipeline With Airflow

  • Exploratory Data Analysis With Pyspark
  • Predictive Analysis With Spark Mllib

  • Introduction And Problem Statement
  • Grading Rubrics And Submission

  • Exploratory Data Analysis With Pyspark
  • Predictive Analysis With Spark Mllib

Data Generalist

  • Introduction To Decision Trees
  • Algorithms For Decision Tree Construction
  • Hyperparameter Tuning In Decision Trees
  • Ensembles And Random Forests

  • Introduction To Boosting And Adaboost
  • Gradient Boosting

  • Principles Of Model Selection
  • Model Building And Evaluation
  • Best Practices

  • Prinicipal Component Analysis And Singular Value Decomposition
  • Principal Component Analysis In Python

  • Classification
  • Feature Engineering
  • Industry Case Study

  • Generalized Linear Regression
  • Regularized Regression
  • Application Of Regularisation

  • Basic Lexical Processing: Tokenization, Bag Of Words, Tf-Idf
  • Advanced Lexical Processing: Canonicalization, Phonetic Hashing, Spell Corrector, Pointwise Mutual Information

  • Data Exploration In Tableau
  • Visualising And Analysing Data In Tableau With Basic Plots

  • Introduction To Data Storytelling
  • Components Of A Good Story With Data - Understanding Your Stakeholder And Stakeholder Empathy, Levels Of Details For Different Stakeholders - Cxo/Leadership Vs Team Presentations, Visuals, Etc.
  • Golden Rules For Data Storytelling

Advanced Programming & Databases

  • Database Design Recap
  • Building Blocks Of Data Modelling
  • Problem Solving Using Data Modelling
  • Data Modelling: Optional Assignment

  • Query Optimisation And Best Practices
  • Problem Solving Using Sql

  • In-Built Data Structures
  • Stack
  • Queue
  • Trees

  • Time Complexity
  • Recursion

  • Searching
  • Sorting
  • Two Pointers

Course NameFees Duration
Complete C Programming Course3000/-45 Days
Complete C ++ Programming Course3000/-45 Days
Core Java Development Course6000/-2 Months
Basic Python Development6000/-2 Months
Basic Android Development6000/-2 Months
Web Designing6000/-2 Months
JavaScript6000/-2 Months
Graphic Designing6000/-2 Months
Digital Marketing6000/-2 Months
PHP & MySQL6000/-2 Months
Internet of Things (IOT)6000/-2 Months
Angular JS6000/-2 Months
Node JS6000/-2 Months
React JS6000/-2 Months
C #6000/-2 Months
Project Development

Have a look on Exciting Project ideas on Data Science

You can build a simple calculator with C using switch cases or if-else statements. This calculator takes two operands and an arithmetic operator (+, -, *, /) from the user, however, you can expand the program to accept more than two operands and one operator by adding logic. Then, based on the operator entered by the user, it conducts the computation on the two operands. The input, however, must be in the format “number1 operator1 number2” (i.e. 2+4).

Using C language, you can also create a student management system. To handle students’ records (like Student’s roll number, Name, Subject, etc.) it employs files as a database to conduct file handling activities such as add, search, change, and remove entries. It appears a simple project but can be handy for schools or colleges that have to store records of thousands of students.

If you have ever lost track of which day of the week is today or the number of days in that particular month, you should build a calendar yourself. The Calendar is written in the C programming language, and this Calendar assists you in determining the date and day you require. We can implement it using simple if-else logic and switch-case statements. The display() function is used to display the calendar and it can be modified accordingly. It also has some additional functions.

This Phone book Project generates an external file to permanently store the user’s data (Name and phone number). The phone book is a very simple C project that will help you understand the core concepts of capacity, record keeping, and data structure. This program will show you how to add, list, edit or alter, look at, and delete data from a record.

An online voting system is a software platform that enables organizations to conduct votes and elections securely. A high-quality online voting system strikes a balance between ballot security, convenience, and the overall needs of a voting event. By collecting the input of your group in a systematic and verifiable manner, online voting tools and online election voting systems assist you in making crucial decisions. These decisions are frequently taken on a yearly basis – either during an event (such as your organization’s AGM) or at a specific time of the year. Alternatively, you may conduct regular polls among your colleagues (e.g. anonymous employee feedback surveys).

With this voting system, users can enter their preferences and the total votes and leading candidate can be calculated. It’s a straightforward C project that’s simple to grasp. Small-scale election efforts can benefit from this.

Tic-tac-toe, also known as noughts and crosses or Xs and Os, is a two-person paper and pencil game in which each player alternates marking squares in a three-by-three grid with an X or an O. The winner is the player who successfully places three of their markers in a horizontal, vertical, or diagonal row. You can implement this fun game using 2D arrays in the C programming language. It is important to use arrays while creating a Tic Tac Toe game in the C programming language. The Xs and Os are stored in separate arrays and passed across various functions in the code to maintain track of the game’s progress. You can play the game against the computer by entering the code here and selecting either X or O. The source code for the project is given below.

Mathematical operations are an everyday part of our life. Every day, we will connect with many forms of calculations in our environment. Matrices are mathematical structures in which integers are arranged in columns and rows. In actual life, matrices are used in many applications. The most common application is in the software sector, where pathfinder algorithms, image processing algorithms, and other algorithms are developed. Some fundamental matrix operations are performed in this project, with the user selecting the operation to be performed on the matrix. The matrices and their sizes are then entered. It’s worth noting that the project only considers square matrices.

Library management is a project that manages and preserves electronic book data based on the demands of students. Both students and library administrators can use the system to keep track of all the books available in the library. It allows both the administrator and the student to look for the desired book. The C files used to implement the system are: main.c, searchbook.c, issuebook.c, viewbook.c, and more.

The Electricity Cost Calculator project is an application-based micro project that predicts the following month’s electricity bill based on the appliances or loads used. Visual studio code was used to write the code for this project. This project employs a multi-file and multi-platform strategy (Linux and Windows). People who do not have a technical understanding of calculating power bills can use this program to forecast their electricity bills for the coming months; however, an electricity bill calculator must have the following features:

  • All loads’ power rating
  • Unit consumed per day
  • Units consumed per month, and
  • Total load calculation

 

The project’s goal is to inform a consumer about the MOVIE TICKET BOOKING SYSTEM so that they can order tickets. The project was created with the goal of making the process as simple and quick as possible. The user can book tickets, cancel tickets, and view all booking records using the system. Our project’s major purpose is to supply various forms of client facilities as well as excellent customer service. It should meet nearly all the conditions for reserving a ticket.

Snakes and ladders, also known as Moksha Patam, is an ancient Indian board game for two or more players that is still considered a worldwide classic today. It’s played on a gridded game board with numbered squares. On the board, there are several “ladders” and “snakes,” each linking two distinct board squares. The dice value can either be provided by the user or it can be generated randomly. If after moving, the pointer points to the block where the ladder is, the pointer is directed to the top of the ladder. If unfortunately, the pointer points to the mouth of a snake after moving, the pointer is redirected to the tail of the snake.

This system is built on the concept of booking bus tickets in advance. The user can check the bus schedule, book tickets, cancel reservations, and check the bus status board using this system. When purchasing tickets, the user must first enter the bus number, after which the system will display the entire number of bus seats along with the passengers’ names, and the user must then enter the number of tickets, seat number, and person’s name.
We will be using arrays, if-else logic, loop statements, and various functions like login(), cancel(), etc. to implement the project.

Pacman, like other classic games, is simple to play. In this game, you must consume as many small dots as possible to earn as many points as possible. The entire game was created using the C programming language. Graphics were employed in the creation of this game. To create the game, you have to first define the grid function to manage the grid structure. To control the movement, you can define functions such as move_right(), move_left(), move_up() and move_down(). C files to add ghosts and their functionalities, positions check, etc. can be added to make the game more fun. The customers will find this C Programming game to be simple to comprehend and manage.

Have a look on Exciting Project ideas on Data Science

Fake News Detection Using R Language

Fake News is prevalent everywhere and it disperses 10X faster than real news. This is an enormous source of trouble that has impacted every orbit of a common man’s life. Due to this, many problems occur like political polarization, other cultural conflicts, and violence. Thinking how this problem could be tracked and tackled well! This Fake News Detection project prepared from R Language’s dataset labels real and fake news well along with an appropriate representation of the textual information. Later, we may incorporate the notions of NLP i.e. Natural Language Processing and TF-IDF Vectorizer technique (whose full form is term frequency-inverse document frequency vectorizer) for an excellent approximation of what is real or fake? So, one needs not to feel fearful whether social authenticity is achieved because the labelization or classification done by NLP, TF-IDF Vectorizer examines the dataset of dimensions 7796*4 well and executes impeccably on Jupyter Lab whose web-based environment supports workflows of scientific computing as well as Natural Language Processing in a flexible and configurable manner

Creating your First Chatbot In Python

Chatbots are a way through which organizations may achieve customer-centricity by tracking and resolving all the real-time issues of customers well. Thinking about how this is achievable in real-time! There are some conversational NLP scripts running in those chatbots through which they understand the questions and then, reciprocate the solutions in the form of customer-oriented feedback. In this project, Python language accesses a larger volume of data via Intents JSON file for finding the patterns well. Those patterns will be helpful in returning appropriate responses the user desires to acquire for solving his/her problem. If required, such responses may be synchronized with necessary customizations thereby handling open-domain or domain-specific problems well. On an overall basis, choosing this project will not only be helping you learn more about Python and its libraries but also make you understand the decoding principles chatbots use for generating the responses assertively solving concurrent or future issues of a customer keeping in mind the accuracy and trustworthiness of feedback.

Detecting Frauds of Credit Cards via Python

Credit Card frauds are omnipresent in the pandemic era and are majorly performed by scammers. Such people are smart enough to steal your credit card details like CVV and Card Numbers and use that to access your account without your knowledge. Since a variety of digital ways are there to access someone’s account, the chances to catch such fraudulent scammers almost become low. Thinking about how one can increase the rate of catching such scammers! With this CC Fraud Detection i.e. Credit Card Fraud Detection project encompassed with hidden capabilities of Machine Learning, ANN i.e Artificial Neural Network, and decision trees, insights into the customers’ data will be labeled with appropriate modeling of their spending behavior. Those who are spending more will obviously be tracked by such scammers so that they may steal the financial freedom of those users well. With such tracking, the chances of prohibiting such fraud people from doing what they really want to become higher thereby preventing the privacy of information well with overall accuracy.

Using Deep Learning for the Classification of Breast Cancer

Breast Cancer is the second most common cancer spotted worldwide since its awareness programs are rarely conducted. You may think that in this technologically advanced world full of solutions one can smartly fight the battle of breast cancer! This is appropriate to some extent but if a delay occurs those solutions won’t be doing the miracles. So, this is essential to identify the traits of such cancer and you may also contribute to this by opting for Breast Cancer Classification as your project. Here, the dataset would be IDC i.e. Invasive Ductal Carcinoma as this is the most usual manifestation of breast cancer found in more than 70 percent of the patients. The benefit is that this dataset will synthesize all the diagnostic images of cancer-inducing cells and with help of Deep Learning attributes, the classification of patients (either they are suffering from this type of cancer or not) will be done precisely so that it is easier to identify the complexity of a patient’s situation. Later, if required, the analysis will be used wisely for the patient’s benefit thereby helping him/her recover from the consequences of breast cancer as soon as possible.

Movie Recommendation Platform with R Packages

Movie Recommendation Platform will work similarly to Netflix, Youtube, Hotstar. This will utilize R packages and predict the recommendations keeping in mind the users’ preferences, star cast, genre, and browsing history. Still wondering how this system will be beneficial! The system can possibly fill all the deficiencies of movie searches just by telling the choices accepted by the variability of users. Besides, the project can be created through two different techniques – a) Collaborative Filtering b) Content-Based Filtering. In Collaborative, a past behavior approach of a user towards movies will be considered to predict outcomes regarding what to watch or not? On the other side, content-based filtering utilizes a series of discrete characteristics totally based upon the description and profile of a movie watched recently or in the past. In both of these, R packages like data.table, ggplot2, and recommenderlab can be used for modeling the desired movie recommendations precisely and in a fun-loving manner. So, you must select this platform as your project and train it well for classifying and recommending movies with different concepts and tastes.

Sentiment Analysis Backed by R Dataset

Sentiment Analysis is really helpful as it identifies the subjective information from the available source material which businesses may use for understanding social sentiments. These sentiments give businesses an overview of what their customers talk about a brand or other associated services offered. Figuring how to initiate such analysis in real-time! With the computational power of R datasets (such as janeaustenr) and some general-purpose LEXICONS, we will be classifying negative and positive emotions of the number of people commented or mentioned with the contextual relevance. Later, some scores will be assigned to those sentiments ranging from 0 to 9, and with all this, businesses can make useful decisions or re-create their pre-decided strategies since this sentiment analysis platform has provided them meaningful insights after analyzing all the social media comments with a deeper meaning related to a brand or a service. Thus, beginners may start working on this project to analyze how one should be extracting meaningful game-changer insights from the analysis performed for a particular brand, service.

Prediction of Age & Gender through Deep Learning

critical thinking and CNN (i.e. Convolutional Neural Network) Implementation, this project would be an ideal choice for drawing the attention of the panel members. The prime aim is to detect the age and gender of a person after analyzing his/her picture. For accomplishing this, we will be using a DL model (rather than a regression model), package OpenCV, and dataset Audience. But some challenges would be there which we can’t afford to ignore. They are dim lighting, out-of-the-way facial expressions, and cosmetics applied on the skin. With them, it is possible to have multiple incompetencies while predicting larger degrees of variations during age prediction and gender detection. Henceforth, such challenges coming forward in the form of anomalies mustn’t be neglected. Instead, we should cross-check if their occurrence exists and focus more on filtering thousands of ages and genders tuning well with the exact identification of the age and gender.

Segmentation of Customers’ Groups with ML

ML algorithms demand creativity and exemplary research so that they may be implemented in real-time in the most simplest and understandable form. From those algorithms, unsupervised learning ones are counted in the difficult ones but they model well the users’ requirements. We will be using K-means unsupervised learning algorithm (this one is simpler than others) for segmenting the customers. Such segmentation is impacted by factors like their annual income, buying and selling patterns, age, gender, and interests. Language would be R and dataset – Mall_Customers. You may ask about its benefit and the answer is – executing an online marketing campaign for fulfilling business needs. As a result of this project, one (data science beginners are included) can’t only segment the customers well but also analyze when the businesses should execute their marketing campaigns on the available customer bases for extracting profit margins and gaining popularity worldwide. In a nutshell, you, or the beginners are well-prepared in helping the ventures out structure their products and services well around their targeted customers and excite the customers by introducing what they really aspire for?

Music Recommendation System

This is yet another and one of the most popular machine learning projects and can be used across different spheres. You might be very familiar with a music recommendation system if you’ve used apps like JioSaavn or Spotify. The system recommends some songs based on the songs you’ve liked or listened to. How does the system do this? This is a typical example where Machine Learning can be applied. This can further be extended for recommendation system which many E-Commerce sites use to suggest some other products which you like to buy with the current one or can be extended for the recommendation system in apps like Netflix or Amazon Prime.

Sentiment Analyzing

A sentiment analyzer learns about various sentiments behind a content through machine learning and predicts the same using AI. By creating an ML system that would analyze the sentiment behind texts, or a post, it might become so a lot easier for organizations to know and understand their consumer behavior better. Twitter data is taken into account as an ultimate entry point for beginners to practice sentiment analysis machine learning problems. Using Twitter datasets, one can get a charismatic combination of tweet contents and other related metadata such as hashtags, location, retweets, users, and many more which pave way for insightful analysis. The foremost problem that you can start working on as a beginner is to build a model to classify users’ profile photos as sad happy or neutral.

Face Mask Detection

In the COVID-19 crisis, it is mandatory for every traveler to wear a mask irrespective of the distance traveled. Wearing a mask protects the person wearing it and the uncountable number of living beings in indirect contact with that person. Well, apart from common sense, one needs to keep a watch on their visitors — for instance, a security guard now also monitors for people not wearing a mask, and he takes strict actions upon finding so. But up to what extent can one manually check for people not putting on a mask? One cannot keep a watch on every moving person to check whether they’re wearing a mask. Thus, this process needs to be automated. The UNLOCK phase has begun, industries & companies have reopened, people have resumed working from offices… Every organization is in need of an automated system that can automatically detect whether their visitors have worn a mask. A face mask detection system is the idea! You can build one that detects so and buzzes an alarm when one has violated the mask rule. This way, it becomes much easier and safer resulting in smooth operations to an extent.

Vehicle Detection & Recognition

Not all projects need global attention or requirement. Some satisfy local needs too. For example, modern universities have thousands of visitors every day and most of them visit the university by commuting on a vehicle. Where there are vehicles, the parking system automatically comes into the picture, along with guards blowing whistles indicating where to park, etc… An important part of this process is authentication — to verify how the visitors are related to the university (teachers, administration, students, visitors, terrorists, or who?). At times of unprecedented situations like tangled traffic, a lot of mishaps are prone to occur such as a student escaping the university illegally, a terrorist entering the university in disguise by taking advantage of the situation, and so on. An automated advanced vehicle detection & recognition system is a solution to such mishaps. This system should be designed in a way such that it detects the vehicle, the build, its number, and the passengers and verifies each time it passes what we call a tollgate and rings a siren whenever it finds suspicious activity/vehicle. This way nobody can escape the intelligent machine and the university/organization will be in safe hands!

Frequently Asked Questions

  • All branch graduates students or undergraduate students who want to become professional in Data Science.
  • All stream students eg. Engineering, BCA, BSc, BBA, Bcom, BA.
  • Any who wants to learn a new skill or improve skill for there career.

  • Basic computer knowledge.
  • General knowledge of what are programming languages.
  • Computer Science Background.
  • Good in Mathamatics & Statistics.

  • Yes, a course completion certificate is given after the completion of this course.

  • Both the offline and online modes are available for this course.

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