Tutorials > 50 Ai Projects – Machine Learning & Deep Learning In Action

50 Ai Projects – Machine Learning & Deep Learning In Action

Date: 9 July 2025 | Posted by: 0nelove

50 Ai Projects – Machine Learning & Deep Learning In Action
Published 7/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.05 GB | Duration: 5h 33m
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
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