Name: 100 Days Data Science Bootcamp: Build 100 Real Life Projects
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Created byPianalytix • 75,000+ Students Worldwide

Last updated 9/2025

English

What you'll learn

Conduct feature engineering on real world case studies
Make powerful analysis, Make robust Machine Learning models
Make robust Machine Learning models, Master Machine Learning on Python
Real life case studies and projects to understand how things are done in the real world
Implement Machine Learning Algorithms
Learn to perform Classification and Regression modelling
Know which Machine Learning model to choose for each type of problem
Understand the full product workflow for the machine learning lifecycle.

Course content

101 sections • 732 lectures • 106h 24m total length

Introduction To The Course0:44
Begin the 100 days data science bootcamp with an introduction to the course, outlining structure, goals, and the plan to build 100 real life projects.
Course Outline Video1:04
Discover the course outline for the 100 days data science bootcamp, outlining the 100 real life projects you will build across the program.
Course Bonuses: Cheat Sheets, Downloads, Mind maps, Guides.0:47
Udemy Course Feedback0:14
Analyze Udemy course feedback within the 100 days data science bootcamp that aims to build 100 real life projects.

Introduction1:47
Explore bankcard tampering detection with computer vision to verify IDs such as Aadhaar and Pan cards, comparing original and tampered images using grayscale similarity, thresholds, and contour-based bounding boxes.
Loading Libraries and Dataset4:00
Use Google Colab to detect pan card tampering with computer vision via structural similarity between original and tampered images. Import libraries, download images from urls, and check formats and sizes.
Creating the Pan Card Detector with Opencv12:28
Create a pan card detector using OpenCV by resizing images, converting to grayscale, computing structural similarity, and using threshold and contours to identify tampering.
Creating the Flask App3:50
Create a Flask app with a config-driven structure (config.py, app.py, init.py), enable development and debug modes, and load the model in views.py while handling file uploads.
Creating important Functions4:56
Build a web app that accepts image uploads via get and post, resizes and grayscales images, and returns a prediction. Includes contour detection with cv2 and renders results on index.html.
Deploy the App in Heroku5:45
Upload project code to a GitHub repo, connect it to Heroku, and deploy main branch; set up a Python app, install dependencies from requirements.txt, and run app.py to obtain url.
Testing the Deployed Pan Card Detector1:56
Test and deploy a pan card detector computer vision app by designing a front end, uploading files, and verifying tampering results, with GitHub and Heroku deployment and different upload methods.
Download The Project Files0:29

Introduction to Dog Breed Prediction Flask App1:59
Importing the Data and Libraries6:15
Data Preprocessing3:10
Load libraries, inspect label distribution, and select three dog breeds for classification. One-hot encode targets, convert images to numpy arrays, and normalize pixel values by 255 to prepare 357 samples of 224x224x3 for model training.
Build and Train Model7:07
Build and train a cnn with conv2d, max pooling, flatten, and dense layers for a three-class classification using softmax, input shape 224x224x3, and adam optimization across 100 epochs.
Testing the Model2:28
Plot training and validation accuracy by epoch with matplotlib, analyze learning, and use predict to evaluate on the test set; tune hyperparameters to improve accuracy.
Creating the Flask App6:40
build a streamlit app that loads a dog breed model saved as dog breed dot h5, accepts a dog image, preprocesses to 224 by 224, and predicts among three breeds.
Running the App in System4:12
Set up and run a Streamlit app from the system using Anaconda prompt, navigating folders with cd, generating a requirements file, and launching at localhost:8501 to predict dog breed.
Download The Project Files0:27

Introduction2:08
Importing Libraries2:51
Create Text and image Watermark8:46
Learn to add image and text watermarks with OpenCV and numpy by converting images to RGB, finding the center, defining the ROI, and overlaying logos or text.
Creating the App13:16
Build a Flask app for image watermarking, configuring development settings, views, and a front-end with HTML and Materialize, then deploy to Heroku using a requirements file.
Deploy the App in Heroku5:28
Deploy the image watermarking app to Heroku by connecting the GitHub repository, using the main branch, and installing dependencies from requirements.txt before testing the front end.
Download the project files0:23

Introduction to Traffic Sign Classification3:25
Build a traffic sign classification model with a convolutional neural network using TensorFlow and Keras, loading Kaggle data in Colab and preprocessing for training in self-driving car applications.
Importing the Data and Libraries5:56
Create a traffic sign classification cnn in google colab using tensorflow and keras, downloading the gtsrb dataset via kaggle, then preprocessing and building a sequential conv2d model.
Image Processing4:23
Visualize traffic sign data, resize images to 50 by 50, normalize by 255, and apply one-hot encoding to 43 classes, with an 80/20 train-test split.
Creating and Testing the Model7:00
Define and train a conv 2D architecture with conv 2D layers, max pool, and dropout for 43-class classification using softmax and sparse categorical cross entropy, with hyperparameter tuning.
Creating Model for Test Set5:17
Prepare and scale the test set images to 50 by 50, normalize by 255, and predict with the trained model to recognize 43 traffic sign classes with 95%+ accuracy.
Download the project files0:23

Introduction2:16
Extract text from images using Tesseract and Pytesseract, then enhance with OpenCV, reduce noise, apply threshold and erode, and draw rectangles around words to automate OCR workflows.
importing Libraries and Data3:45
Use Google Colab to install tesseract and perform OCR on a downloaded image, extracting text with pytesseract and OpenCV for image processing.
Extracting the Test from Image4:27
Extract text from an image using pytesseract with custom configurations, clean by removing symbols, then apply OpenCV steps to grayscale, denoise with blur, and threshold for clear text extraction.
Modifying the Extractor7:58
Creating the Extractor App8:21
learn to build a flask optical character recognition app with image upload and text extraction. cover config, app initialization, front-end, and deployment steps with gunicorn and tesseract setup.
Running the Extractor App2:19
Install dependencies with pip install -r requirements.txt, then run python app.py to launch the Pi Analytics OCR app and test image text extraction.
Download The Project Files0:21

Introduction to Plant Disease3:56
Build a convolutional neural network with TensorFlow and Keras to detect plant diseases from images, using Colab with Google Drive, data visualization, normalization, and model evaluation.
Importing Libraries and Data3:49
Use Google Colab to build a cnn classification model that predicts plant disease, by importing libraries, mounting Google Drive, loading data, and training with TensorFlow and Keras.
Understanding the Data and Data Preprocessing5:06
visualize a three-class plant disease dataset, convert 256x256 rgb images to a numpy array, normalize, perform train-test split, and apply keras one-hot encoding to 900 balanced images (300 per class).
Model Building7:34
Creating an App using Streamlit11:29
Build a plant disease detection app with Streamlit and a Keras model. Process uploaded leaf images with OpenCV, predict diseases like tomato bacterial spot.
Download The Project Files0:23

Introduction3:09
Detect and count vehicles in images and videos using OpenCV and Haar cascades; load online images, convert to grayscale, apply cascades, draw rectangles, and track labeled detections.
Importing Libraries and Data3:10
Transforming Images and Creating Output10:54
Transform images with grayscale conversion, gaussian blur, and dilation, then apply morphological operations and haar cascades to detect cars and buses, producing annotated output videos.
Creating a Flask App17:28
Create a Flask app for vehicle detection and counting using OpenCV cascade classifiers, image uploads, and preprocessing to detect multiple cars and draw rectangles, then deploy via GitHub and Heroku.
Download The Project Files0:23

Introduction2:37
Explore a face swapping project using OpenCV and Dlib to replace a destination face with a source face, following ten steps from shape predictor to face landmarks and seamless clone.
Importing Libraries and data4:32
Explore a Google Colab based face swapping project by importing OpenCV and Dlib, loading a pre-trained shape predictor 68 face landmarks model, and processing images from URLs.
Data Preprocessing and Creating Output14:09
Creating a Flask App14:49
Build a flask face swap app with config, app, and views handling image uploads via get and post; perform face swapping using dlib landmarks and triangulation, rendering results on index.html.
Download The Project Files0:23

Introduction2:35
Build a convolutional neural network with Keras to predict bird species from images. Explore data preparation, model building, training, evaluation, and visualization in a Google Colab workflow for educational use.
Importing libraries and Data6:14
Build a convolutional neural network in Google Colab to classify bird species with Keras, loading data from Google Drive and training with Adam on 224 by 224 images.
Data Processing3:37
Analyze data processing for six bird species, verify balanced data, normalize and reshape images, apply one-hot encoding, and perform train-test split on 811 labeled images for model training.
Creating ML Model10:16
Build a cnn sequential model with conv2d, max pooling, flatten, and dense layers to classify bird species using softmax activation, trained with categorical cross entropy and adam.
Creating a Flask App13:27
Learn to build a Flask app for a bird species classification model, including setting up config, loading a pre-trained six-class model, handling uploads, and predicting with get and post requests.
Download The Project Files0:22

Requirements

Basic knowledge of machine learning

Description

In This Course, Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Machine Learning, Data Science, Artificial Intelligence, Auto Ml, Deep Learning, Natural Language Processing (Nlp) Web Applications Projects With Python (Flask, Django, Heroku, AWS, Azure, GCP, IBM Watson, Streamlit Cloud).

According to Glassdoor, the average salary for a Data Scientist is $117,345/yr. This is above the national average of $44,564. Therefore, a Data Scientist makes 163% more than the national average salary.

This makes Data Science a highly lucrative career choice. It is mainly due to the dearth of Data Scientists resulting in a huge income bubble.

Since Data Science requires a person to be proficient and knowledgeable in several fields like Statistics, Mathematics, and Computer Science, the learning curve is quite steep. Therefore, the value of a Data Scientist is very high in the market.

A Data Scientist enjoys a position of prestige in the company. The company relies on its expertise to make data-driven decisions and enable them to navigate in the right direction.

Furthermore, the role of a Data Scientist depends on the specialization of his employer company. For example – A commercial industry will require a data scientist to analyze their sales.

A healthcare company will require data scientists to help them analyze genomic sequences. The salary of a Data Scientist depends on his role and type of work he has to perform. It also depends on the size of the company which is based on the amount of data they utilize.

Still, the pay scale of Data scientists is way above other IT and management sectors. However, the salary observed by Data Scientists is proportional to the amount of work that they must put in. Data Science needs hard work and requires a person to be thorough with his/her skills.

Due to several lucrative perks, Data Science is an attractive field. This, combined with the number of vacancies in Data Science makes it an untouched gold mine. Therefore, you should learn Data Science in order to enjoy a fruitful career.

In This Course, We Are Going To Work On 100 Real World Projects Listed Below:

Project-1: Pan Card Tempering Detector App -Deploy On Heroku

Project-2: Dog breed prediction Flask App

Project-3: Image Watermarking App -Deploy On Heroku

Project-4: Traffic sign classification

Project-5: Text Extraction From Images Application

Project-6: Plant Disease Prediction Streamlit App

Project-7: Vehicle Detection And Counting Flask App

Project-8: Create A Face Swapping Flask App

Project-9: Bird Species Prediction Flask App

Project-10: Intel Image Classification Flask App

Project-11: Language Translator App Using IBM Cloud Service -Deploy On Heroku

Project-12: Predict Views On Advertisement Using IBM Watson -Deploy On Heroku

Project-13: Laptop Price Predictor -Deploy On Heroku

Project-14: WhatsApp Text Analyzer -Deploy On Heroku

Project-15: Course Recommendation System -Deploy On Heroku

Project-16: IPL Match Win Predictor -Deploy On Heroku

Project-17: Body Fat Estimator App -Deploy On Microsoft Azure

Project-18: Campus Placement Predictor App -Deploy On Microsoft Azure

Project-19: Car Acceptability Predictor -Deploy On Google Cloud

Project-20: Book Genre Classification App -Deploy On Amazon Web Services

Project 21 : DNA classification for finding E.Coli - Deploy On AWS

Project 22 : Predict the next word in a sentence. - AWS - Deploy On AWS

Project 23 : Predict Next Sequence of numbers using LSTM - Deploy On AWS

Project 24 : Keyword Extraction from text using NLP - Deploy On Azure

Project 25 : Correcting wrong spellings - Deploy On Azure

Project 26 : Music popularity classification - Deploy On Google App Engine

Project 27 : Advertisement Classification - Deploy On Google App Engine

Project 28 : Image Digit Classification - Deploy On AWS

Project 29 : Emotion Recognition using Neural Network - Deploy On AWS

Project 30 : Breast cancer Classification - Deploy On AWS

Project-31: Sentiment Analysis Django App -Deploy On Heroku

Project-32: Attrition Rate Django Application

Project-33: Find Legendary Pokemon Django App -Deploy On Heroku

Project-34: Face Detection Streamlit App

Project-35: Cats Vs Dogs Classification Flask App

Project-36: Customer Revenue Prediction App -Deploy On Heroku

Project-37: Gender From Voice Prediction App -Deploy On Heroku

Project-38: Restaurant Recommendation System

Project-39: Happiness Ranking Django App -Deploy On Heroku

Project-40: Forest Fire Prediction Django App -Deploy On Heroku

Project-41: Build Car Prices Prediction App -Deploy On Heroku

Project-42: Build Affair Count Django App -Deploy On Heroku

Project-43: Build Shrooming Predictions App -Deploy On Heroku

Project-44: Google Play App Rating prediction With Deployment On Heroku

Project-45: Build Bank Customers Predictions Django App -Deploy On Heroku

Project-46: Build Artist Sculpture Cost Prediction Django App -Deploy On Heroku

Project-47: Build Medical Cost Predictions Django App -Deploy On Heroku

Project-48: Phishing Webpages Classification Django App -Deploy On Heroku

Project-49: Clothing Fit-Size predictions Django App -Deploy On Heroku

Project-50: Build Similarity In-Text Django App -Deploy On Heroku

Project-51: Black Friday Sale Project

Project-52: Sentiment Analysis Project

Project-53: Parkinson’s Disease Prediction Project

Project-54: Fake News Classifier Project

Project-55: Toxic Comment Classifier Project

Project-56: IMDB Movie Ratings Prediction

Project-57: Indian Air Quality Prediction

Project-58: Covid-19 Case Analysis

Project-59: Customer Churning Prediction

Project-60: Create A ChatBot

Project-61: Video Game sales Analysis

Project-62: Zomato Restaurant Analysis

Project-63: Walmart Sales Forecasting

Project-64 : Sonic wave velocity prediction using Signal Processing Techniques

Project-65 : Estimation of Pore Pressure using Machine Learning

Project-66 : Audio processing using ML

Project-67 : Text characterisation using Speech recognition

Project-68 : Audio classification using Neural networks

Project-69 : Developing a voice assistant

Project-70 : Customer segmentation

Project-71 : FIFA 2019 Analysis

Project-72 : Sentiment analysis of web scrapped data

Project-73 : Determining Red Vine Quality

Project-74 : Customer Personality Analysis

Project-75 : Literacy Analysis in India

Project-76: Heart Attack Risk Prediction Using Eval ML (Auto ML)

Project-77: Credit Card Fraud Detection Using Pycaret (Auto ML)

Project-78: Flight Fare Prediction Using Auto SK Learn (Auto ML)

Project-79: Petrol Price Forecasting Using Auto Keras

Project-80: Bank Customer Churn Prediction Using H2O Auto ML

Project-81: Air Quality Index Predictor Using TPOT With End-To-End Deployment (Auto ML)

Project-82: Rain Prediction Using ML models & PyCaret With Deployment (Auto ML)

Project-83: Pizza Price Prediction Using ML And EVALML(Auto ML)

Project-84: IPL Cricket Score Prediction Using TPOT (Auto ML)

Project-85: Predicting Bike Rentals Count Using ML And H2O Auto ML

Project-86: Concrete Compressive Strength Prediction Using Auto Keras (Auto ML)

Project-87: Bangalore House Price Prediction Using Auto SK Learn (Auto ML)

Project-88: Hospital Mortality Prediction Using PyCaret (Auto ML)

Project-89: Employee Evaluation For Promotion Using ML And Eval Auto ML

Project-90: Drinking Water Potability Prediction Using ML And H2O Auto ML

Project-91: Image Editor Application With OpenCV And Tkinter

Project-92: Brand Identification Game With Tkinter And Sqlite3

Project-93: Transaction Application With Tkinter And Sqlite3

Project-94: Learning Management System With Django

Project-95: Create A News Portal With Django

Project-96: Create A Student Portal With Django

Project-97: Productivity Tracker With Django And Plotly

Project-98: Create A Study Group With Django

Project-99: Building Crop Guide Application with PyQt5, SQLite

Project-100: Building Password Manager Application With PyQt5, SQLite

Tip: Create A 50 Days Study Plan Or 100 Day Study Plan, Spend 1-3hrs Per Day, Build 100 Projects In 50 Days Or 100 Projects In 100 Days.

The Only Course You Need To Become A Data Scientist, Get Hired And Start A New Career

Note (Read This): This Course Is Worth Of Your Time And Money, Enroll Now Before Offer Expires.

Who this course is for:

Beginners in data science

What you'll learn

Explore related topics

Course content

Introduction4 lectures • 3min

Project-1: Pan Card Tempering Detector App -Deploy On Heroku8 lectures • 35min

Project-2: Dog breed prediction Flask App8 lectures • 32min

Project-3: Image Watermarking6 lectures • 33min

Project-4: Traffic sign classification6 lectures • 26min

Project-5: Text Extraction From Images Application7 lectures • 29min

Project-6: Plant Disease Prediction Streamlit App6 lectures • 32min

Project-7: Vehicle Detection And Counting Flask App5 lectures • 35min

Project-8: Create A Face Swapping Flask App5 lectures • 37min

Project-9: Bird Species Prediction Flask App6 lectures • 37min

Requirements

Description

Who this course is for: