50 Ai Projects – Machine Learning & Deep Learning In Action

Ready-to-Use Projects with Full Code & Web App – Train, Test, Deploy in Colab, Flask & more

What you'll learn

Build 50 real-world AI projects for web, health, social media, sports, business, and more

Deploy AI applications online using Flask, Gradio, and Google Colab — no complex setup required

Master the fundamentals of machine learning and deep learning through hands-on, applied projects

Reuse project code in your own apps, client work, or startup ideas — ready to plug and play

Understand the full AI pipeline: from dataset exploration to model training, evaluation, and deployment

Work with top AI tools and frameworks: OpenCV, HuggingFace, PyTorch, TensorFlow, Scikit-learn

Build AI that can detect objects, summarize articles, translate languages, recognize emotions, and more

Create a powerful AI portfolio to impress recruiters, clients, and investors

Learn faster with a fully practical, no-fluff, no-theory approach — just build and deploy

Turn your AI skills into income by adapting these projects for businesses, freelance work, or personal products

Requirements

Basic knowledge of Python (variables, loops, functions).

A computer with an internet connection (Windows, Mac, or Linux).

Motivation to learn by working on practical, real-world projects.

Description

The most practical, scalable, and actionable artificial intelligence course ever published on Udemy.Build. Test. Deploy. Learn by doing — not just by watching.The Largest Collection of Reusable AI Projects — Always GrowingThis course has a bold mission:To build the largest, real-world-focused AI project library on Udemy.Every project is designed for practical use — powered by machine learning and deep learning — and ready to be adapted or deployed in your own apps or products.7 fully completed projects available right nowNew projects added every monthGoal: 50 ready-to-use AI projects in the coming monthsThen on to 100 AI projects — with no extra cost to youBuy Early, Pay LessThe course price will gradually increase as new projects are added.The earlier you join, the more value you get at a lower price.Every Project Includes:A clean, well-commented Python script or Google Colab notebookA deployable web app built with FlaskClear, concise walkthrough videosStructured, reusable code you can plug into your own productsDownloadable resources, ready to useA Different Kind of AI CourseNo endless theory;No fluff;100% hands-on and project-based.You’ll build real, working AI from day one.Each project is built for real-world usage — and for your portfolio.Example Projects You Can Access Right Now:AI for Football Match Score PredictionReal-time Emotion DetectionDrone Detection with Computer VisionObject Detection AI (cars, bikes, ambulances, and more)English → French Technical Translation AIAutomatic Text SummarizationPneumonia Detection from Medical X-ray ImagesAnd over 40 more projects on the way:AI agents, predictive analytics, intelligent assistants, and AI-powered no-code tools…Each Project Follows a Clear, Repeatable Format:Project overview and real-world objectiveDataset exploration and visualizationModel training (machine learning or deep learning)Performance evaluationWeb app deployment with Flask or GradioSource code downloadThis Course Is For You If You Are:A developer, data scientist, or self-learner passionate about AIA freelancer or entrepreneur looking to deliver real AI solutionsA student or teacher seeking classroom-ready AI demonstrationsA curious mind who prefers building over just watchingAnd Then What?Lifetime access to all current and future projectsFree monthly updatesA founding spot in the most ambitious AI project catalog on UdemyA powerful portfolio of deployable, real-world AI applicationsWhat You’ll Achieve:Build 50 AI projects (then 100)Learn how to take AI into productionReuse the code for your own use cases, products, or clientsMaster key tools like Colab, Flask, HuggingFace, OpenCVDeploy complete AI apps from idea to productionJoin now.Get instant access to the first batch of AI projects.And become a founding member of the largest AI project library ever created on Udemy.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Installing PyCharm

Lecture 3 Dependency configuration: Creating a suitable virtual environment

Section 2: Project 1 – AI for Football Score Prediction

Lecture 4 Presentation of the final project

Lecture 5 Presentation of the dataset

Lecture 6 Step 1: Importing the necessary libraries

Lecture 7 Step 2: Downloading, Decompressing, and Preparing the ESPN Soccer Data

Lecture 8 Step 3: Displaying the ESPN database schema

Lecture 9 Step 4: Loading fixtures.csv, teamStats.csv, standings.csv, and leagues.csv

Lecture 10 Step 5: Exploratory Analysis

Lecture 11 5.1. Overall analysis of fixture results

Lecture 12 5.2. Overall analysis of the results of teamStats

Lecture 13 5.3. Overall analysis of standings

Lecture 14 5.4. Distribution analysis for leagues

Lecture 15 Step 6: Checking for inconsistencies in Standings.csv

Lecture 16 Step 7: Checking for inconsistencies in teamStats.csv

Lecture 17 Step 8: Checking for inconsistencies in leagues.csv

Lecture 18 Step 9: Checking for inconsistencies in fixtures.csv

Lecture 19 Step 10: Merging and Joins - Consolidating Data for Modeling

Lecture 20 Step 11: Handling Missing Values (NaN) and Optimizing Data Quality

Lecture 21 11.1. Imputation Using Bayesian Linear Regression

Lecture 22 11.2. Validation and Cleaning of Team Standings Data

Lecture 23 11.3. Removing Columns Related to Future Matches

Lecture 24 11.4. Removing Non-Relevant Competitive Context Columns

Lecture 25 11.5. Removing Non-Relevant Update-Related Columns

Lecture 26 11.6. Final Data Integrity Check

Lecture 27 Step 12: Data Enrichment with Derived Variables and Performance Indicators

Lecture 28 Step 13: Transforming Categorical Variables

Lecture 29 Step 14 – Standardizing Numerical Data

Lecture 30 Step 15: Analyzing Variable Importance and Feature Selection

Lecture 31 Step 16: Training and Validating the Score Prediction Model

Lecture 32 16.1. Evaluating the Prediction Model's Performance

Lecture 33 Step 17 – Adapting and Re-training the Model Based on the Football API Data

Lecture 34 Step 18: Storing on Drive

Lecture 35 Final Project Notebook – Download

Lecture 36 Web Application Structure

Lecture 37 The requirements.txt file

Lecture 38 API.football.com

Lecture 39 Back-end integration

Lecture 40 Front-end integration

Lecture 41 Launching the application

Section 3: Project 2 – Real-time detection of human emotions

Lecture 42 Presentation of the final project

Lecture 43 Dataset overview

Lecture 44 AI model training: the 25 key steps

Lecture 45 Analysis of Model Results on Test Data

Lecture 46 Final Project Notebook

Lecture 47 Project Structure and Integration into the Application

Lecture 48 The requirements.txt file

Lecture 49 The app.py file

Lecture 50 The index.html

Section 4: Project 3 - Automatic detection of drones and other flying objects with AI

Lecture 51 Presentation of the final project

Lecture 52 Dataset overview

Lecture 53 AI model training: the 27 key steps

Lecture 54 Analysis of evaluation metrics for the YoloV8 model

Lecture 55 Analysis of loss evolution during training and validation

Lecture 56 Final Project Notebook

Lecture 57 Integrating the AI Model into the Web Application

Lecture 58 The requirements.txt file

Lecture 59 The app.py file

Lecture 60 The index.html file

Section 5: Project 4 - AI for object detection (cars, motorcycles, ambulances, etc.)

Lecture 61 Presentation of the final project

Lecture 62 Dataset overview

Lecture 63 Training the AI model with YOLOv9: the 32 steps of the complete pipeline

Lecture 64 Decoding Training and Validation Metrics

Lecture 65 Final Project Notebook

Lecture 66 Integrating AI into a web application

Lecture 67 The requirements.txt file

Lecture 68 The app.py file

Lecture 69 The index.html

Section 6: Project 5 - English → French translation AI for technical texts

Lecture 70 Presentation of the final project

Lecture 71 The fine-tuned AI model for translation

Lecture 72 The dataset used to train our specialized translator

Lecture 73 EN-FR Machine Translation: 13 Key Steps for Fine-Tuning MarianMT

Lecture 74 Results Analysis: Final Performance of the Translation Model

Lecture 75 Final Project Notebook

Lecture 76 Web application structure

Lecture 77 Technical guide: Cloning and integration of YOLOv9

Lecture 78 The requirements.txt file

Lecture 79 The app.py file

Lecture 80 The index.html

Section 7: Project 6 - Multilingual summary generation AI

Lecture 81 Presentation of the final project

Lecture 82 Presentation of the AI model used: facebook/mbart-large-50

Lecture 83 The datasets used to train our summary AI

Lecture 84 Fine-tuning the mBART model for automatic summarization

Lecture 85 Understanding and Analyzing the Evaluation Results of Our Model

Lecture 86 Final Project Notebook

Lecture 87 Deployment of the multilingual summary model in a web application

Lecture 88 The requirements.txt file

Lecture 89 The app.py file

Lecture 90 The index.html file

Section 8: Project 07 - AI for detecting pneumonia from medical images

Lecture 91 Presentation of the Final Project

Lecture 92 The EfficientNetB0 Model

Lecture 93 The Dataset: Chest X-Ray Images

Lecture 94 Fine-tuning the EfficientNetB0 model

Lecture 95 Model Evaluation and In-Depth Analysis of Results

Lecture 96 Final Project Notebook

Lecture 97 Web application structure

Lecture 98 The requirements.txt file

Lecture 99 Decryption of app.py

Lecture 100 Decoding index.html

Section 9: Understanding Artificial Intelligence (Optional)

Lecture 101 AI, Machine Learning & Deep Learning

Lecture 102 How AI works ? 1/4

Lecture 103 How AI works ? 2/4

Lecture 104 How AI works ? 3/4

Lecture 105 How AI works ? 4/4

Lecture 106 How Deep Learning works and what it is

Lecture 107 Loss & Retropagation

Lecture 108 Activation Functions

Lecture 109 Utility of Neural Networks

Lecture 110 CPU VS GPU

Lecture 111 TPU

Lecture 112 TensorFlow, PyTorch and Keras

Lecture 113 Integrated Development Environments for Deep Learning

Section 10: Exploring CNN: Key concepts and applications in computer vision (Optional)

Lecture 114 Introduction to Computer Vision

Lecture 115 CNN - What is an image ?

Lecture 116 How CNNs work ?

Lecture 117 RGB Channels

Lecture 118 Convolution on color images

Lecture 119 Convolution steps (Strides)

Lecture 120 Understanding Padding in CNNs

Lecture 121 ReLU activation function in CNNs

Lecture 122 Understanding Pooling Layers

Lecture 123 Fully Connected Layers 1/2

Lecture 124 Fully Connected Layers 2/2

Section 11: YOLO: Fundamentals and Methods of Image Annotation (Optional)

Lecture 125 Introduction to YOLO

Lecture 126 Data Labeling or annotation - what is it ?

Lecture 127 Image annotation types

Lecture 128 Annotation tools

Lecture 129 YOLOV9 Training Process 1/3

Lecture 130 YOLOV9 Training Process 2/3

Lecture 131 YOLOV9 Training Process 3/3

Lecture 132 Summary of the training process

Lecture 133 YOLO loss

Developers, data scientists, and self-learners who want to build real AI projects, not just follow theory,Freelancers and entrepreneurs looking to integrate AI into their products or services quickly and efficiently,Students and teachers in AI, computer science, or data science seeking practical, classroom-ready projects,Curious minds and AI enthusiasts who prefer learning by doing, not just watching,Anyone who wants to reuse ready-to-deploy AI code for real-world use cases