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50-Days 50-Projects: Data Science, Machine Learning Bootcamp

Name: 50-Days 50-Projects: Data Science, Machine Learning Bootcamp
Rating: 4.3 (131 reviews)

Build & Deploy Data Science, ML, Deep Learning Projects Course(Python, Flask, Django, AWS, Azure, GCP, Heruko Cloud)

Created byPianalytix • 75,000+ Students Worldwide

Last updated 10/2025

English

What you'll learn

Make robust Machine Learning models
Understand the full product workflow for the machine learning lifecycle.
Real life case studies and projects to understand how things are done in the real world
Know which Machine Learning model to choose for each type of problem
Learn how to program in Python using the latest Python 3
Learn to pre process data, clean data, and analyze large data
Learn to use NumPy for Numerical Data
Learn to use Pandas for Data Analysis
Have a great intuition of many Machine Learning models

Course content

51 sections • 367 lectures • 46h 28m total length

Introduction To The Course0:44
Course Outline Video4:45
Course Bonuses: Cheat Sheets, Downloads, Mind maps, Guides.0:47
Udemy Feedback0:14
Analyze Udemy feedback to enhance your data science and machine learning project approach in the 50-day bootcamp, leveraging practical insights from the course.

Introduction To Pan Card Tempering Detector1:40
Detect bank card tampering with a computer vision workflow that compares user images to originals using grayscale, thresholds, and contour analysis in a 10-step process.
Loading libraries and dataset3:49
Creating the pancard detector with opencv12:18
Learn to build a pancard detector with OpenCV by converting images to grayscale, computing the structural similarity index, and drawing bounding boxes to detect tampering.
Creating the Flask App3:39
Learn to build a Flask app by configuring environments, secret keys, and session settings, and wiring routes, views, and model integration in a practical project.
Creating Important functions4:45
Create important functions, define a functioning index to run the app, classify uploaded images, compute thresholds and contours, and determine the bounding rectangle for rendering predictions.
Deploy the app in Heroku5:34
Testing the deployed pan card detector1:46
Test and deploy a computer vision pan card detector by building a frontend, uploading files, and deploying via GitHub and Heroku, while evaluating 28.57 percent accuracy.
Download The Project Files0:29

Introduction to dog breed prediction1:52
Train a convolutional neural network in TensorFlow to identify dog breeds from images, a multiclass supervised learning task. Load data in Google Colab, preprocess, encode labels, build, and evaluate model.
Importing the data and libraries6:05
learn to import data and libraries, connect to Google Cloud, download the dog breed dataset, and build a convolutional neural network with TensorFlow to identify dog breeds.
Data Preprocessing3:00
Build and Train Model6:57
Build and train a CNN in a sequential model with convolutional and max pooling layers, flattening, and dense layers for multiclass classification, using softmax and categorical cross-entropy.
Testing the model2:18
Creating the Flask App6:29
Running the app4:02
Set up a Conda environment, generate a requirements file, install Streamlit, and run a local app to predict images, such as a Bernese Mountain Dog, on localhost.
Download The Project Files0:27

Introduction --Image Watermarking App -Deploy On Heroku2:00
Importing libraries and logo2:40
Leverage Google Cloud's computing power to apply OpenSea logos and text watermarks to images, download logos with requests, and process them with NumPy and Pillow for watermarking.
Create text and image watermark8:36
Creating the app13:05
Create an image watermarking app with Flask, handling image uploads, locating the center, applying logo or text watermarks, and deploying on Heroku with a requirements file.
Deploying the app in heroku5:18
Deploy a watermarking app to Heroku by connecting GitHub, creating a repo, uploading files, and enabling automatic deploys from the main branch.
Download The Project Files0:23

Introduction to traffic sign classification3:17
importing the data and libraries5:45
Image processing4:12
creating and testing the model6:50
define and test a cnn architecture for a 43-class multiclass image classification task, tuning input size, 3x3 filters, activation, pooling, and dropout, then train with sparse categorical crossentropy and adam.
Creating model for test set5:06
Download The Project Files0:23

Introduction to text extraction2:09
Importing libraries and data3:35
Extracting the test from image4:16
Modifying the extractor7:47
Learn practical image processing workflows for data extraction, including erosion, morphology, edge detection, skew correction, and template matching, with Tesseract, plus building a Flask app to deploy the pipeline.
creating the extractor app8:10
Build a Flask-based optical character recognition app by creating a project folder, configuring development settings, handling image uploads, and extracting text with Tesseract OCR.
Running the extractor app2:09
Install the project dependencies from the requirements file, start the app, and test the optical character recognition extractor by uploading an image to extract text.
Download The Project Files0:21

Introduction3:49
Build a convolutional neural network in TensorFlow on Colab to detect plant diseases from images, performing data prep, normalization, training, validation, and envisioning a farmer smartphone app.
Importing libraries and data3:38
Import libraries and data, then build and evaluate a CNN classifier for plant disease with TensorFlow and Keras, using data from Google Drive in Colab and OpenCV.
Understanding the data4:56
Observe a balanced 900-image dataset of three plant disease classes—common dressed potato, ugly blight, and tomato bacterial spots—each 256 by 256 RGB, with an 80/20 train-test split and one-hot encoding.
Model building7:23
Build a three-class plant disease classifier using a CNN with 2D max pooling, flatten and dense layers, trained with categorical cross entropy and Adam, achieving 99.44% test accuracy.
Creating an app using streamlit11:18
build a streamlit app for plant disease detection by loading a trained model, uploading an image, and predicting the disease class.
Download the project files0:22

Introduction3:01
Learn to detect and count vehicles in images and videos using cascade classifiers with OpenCV, converting to grayscale, loading pre-trained cascades for cars and buses, and drawing labeled detections.
Importing libraries and data3:00
Transforming Images and creating output10:43
Explore image transformations such as grayscale conversion and blur, then apply cascade classifiers to detect cars and buses, count detections, and produce output video.
Creating A Flask App17:17
Download the project files0:22

Introduction to Bird Species Prediction2:31
Mount your Google drive in Colab, load and visualize bird images, and build a CNN for multiclass bird species prediction using image normalization, one-hot encoding, and model training.
Importing Libraries And Data6:04
Data processing Bird Species Prediction3:27
Analyze six bird species (American goldfinch, barn owl, carmine bita, downy woodpecker, emperor penguin, flamingo); balance data, visualize, normalize by 255, one-hot encode labels, and split 80/20 training and testing.
Creating ML Model10:06
Build a cnn model using a sequential architecture with conv2d, pooling, flatten, and dense layers for multiclass bird species classification, then train, evaluate, and save the model for deployment.
Creating A Flask App13:17
Create a Flask app for a bird species classification model, covering project structure, config, and loading the trained model with image upload and prediction workflow.
Download The Project Files0:22

Requirements

Some prior coding or python scripting experience is required.
Basic Knowledge of machine learning & data science

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).

Data science can be defined as a blend of mathematics, business acumen, tools, algorithms, and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions.

In data science, one deals with both structured and unstructured data. The algorithms also involve predictive analytics. Thus, data science is all about the present and future. That is, finding out the trends based on historical data which can be useful for present decisions, and finding patterns that can be modeled and can be used for predictions to see what things may look like in the future.

Data Science is an amalgamation of Statistics, Tools, and Business knowledge. So, it becomes imperative for a Data Scientist to have good knowledge and understanding of these.

With the amount of data that is being generated and the evolution in the field of Analytics, Data Science has turned out to be a necessity for companies. To make the most out of their data, companies from all domains, be it Finance, Marketing, Retail, IT or Bank. All are looking for Data Scientists. This has led to a huge demand for Data Scientists all over the globe. With the kind of salary that a company has to offer and IBM is declaring it as the trending job of the 21st century, it is a lucrative job for many. This field is such that anyone from any background can make a career as a Data Scientist.

In This Course, We Are Going To Work On 50 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: Sentiment Analysis Django App -Deploy On Heroku

Project-22: Attrition Rate Django Application

Project-23: Find Legendary Pokemon Django App -Deploy On Heroku

Project-24: Face Detection Streamlit App

Project-25: Cats Vs Dogs Classification Flask App

Project-26: Customer Revenue Prediction App -Deploy On Heroku

Project-27: Gender From Voice Prediction App -Deploy On Heroku

Project-28: Restaurant Recommendation System

Project-29: Happiness Ranking Django App -Deploy On Heroku

Project-30: Forest Fire Prediction Django App -Deploy On Heroku

Project-31: Build Car Prices Prediction App -Deploy On Heroku

Project-32: Build Affair Count Django App -Deploy On Heroku

Project-33: Build Shrooming Predictions App -Deploy On Heroku

Project-34: Google Play App Rating prediction With Deployment On Heroku

Project-35: Build Bank Customers Predictions Django App -Deploy On Heroku

Project-36: Build Artist Sculpture Cost Prediction Django App -Deploy On Heroku

Project-37: Build Medical Cost Predictions Django App -Deploy On Heroku

Project-38: Phishing Webpages Classification Django App -Deploy On Heroku

Project-39: Clothing Fit-Size predictions Django App -Deploy On Heroku

Project-40: Build Similarity In-Text Django App -Deploy On Heroku

Project-41: Heart Attack Risk Prediction Using Eval ML (Auto ML)

Project-42: Credit Card Fraud Detection Using Pycaret (Auto ML)

Project-43: Flight Fare Prediction Using Auto SK Learn (Auto ML)

Project-44: Petrol Price Forecasting Using Auto Keras

Project-45: Bank Customer Churn Prediction Using H2O Auto ML

Project-46: Air Quality Index Predictor Using TPOT With End-To-End Deployment (Auto ML)

Project-47: Rain Prediction Using ML models & PyCaret With Deployment (Auto ML)

Project-48: Pizza Price Prediction Using ML And EVALML(Auto ML)

Project-49: IPL Cricket Score Prediction Using TPOT (Auto ML)

Project-50: Predicting Bike Rentals Count Using ML And H2O Auto ML

Tip: Create A 50 Days Study Plan, Spend 1-2hrs Per Day, Build 50 Projects In 50 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:

Anyone who is beginner in data science.

50-Days 50-Projects: Data Science, Machine Learning Bootcamp

What you'll learn

Explore related topics

Course content

Introduction To The Course4 lectures • 7min

Project-1: Pan Card Tempering Detector App -Deploy On Heroku8 lectures • 34min

Project-2: Dog breed prediction Flask App8 lectures • 31min

Project-3: Image Watermarking App -Deploy On Heroku6 lectures • 32min

Project-4: Traffic sign classification6 lectures • 26min

Project-5: Text Extraction From Images Application7 lectures • 28min

Project-6: Project On Plant Disease Prediction6 lectures • 31min

Project-7: Vehicle Detection And Counting5 lectures • 34min

Project-8: Create A Face Swap Application5 lectures • 36min

Project-9: Bird Species Prediction Flask App6 lectures • 36min

Requirements

Description

Who this course is for: