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100 Days Of Code: Real World Data Science Projects Bootcamp

Name: 100 Days Of Code: Real World Data Science Projects Bootcamp
Rating: 4.3 (126 reviews)

Build 100 Projects in 100 Days- Data Science, Machine Learning, Deep Learning (Python, Flask, Django, AWS, Heruko Cloud)

Created byPianalytix • 75,000+ Students Worldwide

Last updated 10/2025

English

What you'll learn

Have a great intuition of many Machine Learning models
Know which Machine Learning model to choose for each type of problem
Implement Machine Learning Algorithms
Create supervised machine learning algorithms to predict classes.
Understand the full product workflow for the machine learning lifecycle.
Explore how to deploy your machine learning models
Learn which Machine Learning model to choose for each type of problem
Real life case studies and projects to understand how things are done in the real world
Learn to use NumPy for Numerical Data
Use Matplotlib to create fully customized data visualizations with Python
Explore large datasets and wrangle data using Pandas
Learn to use Seaborn for statistical plots

Course content

101 sections • 732 lectures • 106h 31m total length

Introduction To The Course0:44
Course Outline1:04
Course Bonuses: Cheat Sheets, Downloads, Mind maps, Guides.0:47

Introduction1:47
Loading Libraries and Dataset4:00
Creating the Pan Card Detector with Opencv12:28
Develop a pan card detector with opencv by resizing images to 250 by 160, converting to grayscale, and using the structural similarity index to identify tampering.
Creating the Flask App3:50
Develop a Flask app for your model by building config classes, setting upload paths, and integrating app, init, and views to run with development and debug settings.
Creating Important Functions4:56
Develop essential functions for a web app that uploads, resizes, converts to grayscale, and detects contours with cv2 using structural similarities.
Deploy the App in Heroku15:45
Deploy a data science app by uploading the project to GitHub, linking to Heroku, and deploying the Python app (app.py) with a proc file and requirements.txt to get the url.
Testing the Deployed Pan Card Detector1:56
Download The Project Files0:29

Introduction1:59
Build a convolutional neural network with Keras and TensorFlow to identify dog breeds from images, loading Kaggle data in Google Colab, applying one-hot encoding and normalization, and evaluating predictions.
Importing the Data and Libraries6:15
Data Preprocessing3:10
Build and Train Model7:07
Testing the Model2:28
Creating the Flask App6:40
Running the App in System4:12
Open the Anaconda prompt, navigate to your project, create a requirements file, install Streamlit, and run streamlit run main_app.py to launch dog breed prediction app on localhost:8501 with a PNG.
Download The Project Files0:27

Introduction2:08
Importing Libraries and Logo2:51
Learn how to use Google Colab to add a watermark to images with OpenCV, including logo and text watermark, using libraries cv2, numpy, requests, and PIL.
Create Text and Image Watermark8:46
Creating the App13:16
Learn to build a Flask image watermarking app, configure settings, handle image uploads, apply logo or text watermarks, and deploy with Heroku.
Deploying the App in Heroku5:28
Download The Project Files0:01

Introduction3:25
Importing the Data and Libraries5:56
Connect Kaggle to Google Colab, download and unzip the German traffic sign dataset, and build a CNN traffic sign classifier using TensorFlow and Keras with essential libraries.
Image Processing4:23
Visualize traffic sign images, find a 50 by 50 average shape, resize to 50 by 50 RGB, normalize by 255, convert to numpy arrays, one-hot encode labels, and split 80/20.
Creating and Testing the Model7:00
Create and test a convolutional neural network with conv2d layers, dropout, and softmax for 43-class classification, trained with sparse categorical cross entropy and Adam, then assess overfitting.
Creating Model for Test Set5:17
Develop and test a traffic sign recognition pipeline by cleaning test data, scaling images to 50 by 50, and normalizing them. Achieve strong accuracy across 43 classes.
Download The Project Files0:23

Introduction3:56
Develop a convolutional neural network to detect plant diseases using TensorFlow and Keras in Colab, with data visualization, preprocessing, training, evaluation, predictions, and a farmer-focused app.
Importing Libraries and Data3:49
Understanding the Data and Data Preprocessing5:06
Visualize and preprocess a three-class plant-disease dataset of 900 RGB images, convert to numpy arrays, normalize by 225, and perform a train-test split with one-hot encoding using Keras.
Model Building7:34
Build a CNN for plant disease classification with conv 2d, max pooling, flatten and dense layers, using softmax activation and categorical cross entropy, trained with Adam and evaluated for accuracy.
Creating an App Using Streamlit11:29
Download The Project Files0:23

Introduction to Vehicle Detection3:09
Detect and count vehicles in images and videos using OpenCV and Haar cascades, loading XML classifiers, converting to grayscale, applying contour-based localization, and counting cars and buses.
Importing Libraries and Data Vehicle Detection3:10
Transforing Images and Creating Output Vehicle Detection10:54
Convert the image to grayscale and apply Gaussian blur to reduce noise. Use dilation and Haar cascades to detect cars and buses, drawing rectangles and counting vehicles in video.
Creating a Flask App Vehicle Detection17:28
Create a Flask app for vehicle detection and counting using OpenCV cascade classifiers on uploaded images. Deploy it to Heroku from GitHub with a requirements.txt and proc file.
Download The Project Files0:22

Introduction to Bird Species Prediction Flask App2:35
Importing Libraries and Data6:14
Data Processing Bird Species Prediction3:37
Six bird species are classified using 224 by 224 RGB images with 811 labels, an 80/20 train-test split, image normalization, and one-hot encoding.
Creating ML Model Bird Species Prediction10:16
Create a cnn model for bird species prediction using conv2d, max pooling, and softmax output. Train with categorical cross entropy and Adam, while applying regularization and dropout to prevent overfitting.
Creating a Flask App13:27
Build a Flask app for a bird species classification model, configure development settings, load a trained Keras model, handle image uploads, and render predictions to a front-end.
Download The Project Files0:23

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).

We have been able to process such a voluminous amount of data. We are able to analyze and draw insights from this data owing to these advanced computational systems.

However, despite all these advancements, data remains a vast ocean that is growing every second. While the huge abundance of data can prove useful for the industries, the problem lies in the ability to use this data.

As mentioned above, data is fuel but it is a raw fuel that needs to be converted into useful fuel for the industries. In order to make this raw fuel useful, industries require Data Scientists. Therefore, knowledge of data science is a must if you wish to use this data to help companies make powerful decisions.

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 in 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 the position of prestige in the company. The company relies on his 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 health-care 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 Scientist 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.

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

100 Days Of Code: Real World Data Science Projects Bootcamp

What you'll learn

Explore related topics

Course content

Course Introduction3 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 Watermarking App -Deploy On Heroku6 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 • 36min

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

Requirements

Description

Who this course is for: