Practical Deep Learning: From Beginner To Ai In 15 Days

Zero to Production-Ready: Master Deep Learning & AI in Just 15 Days. No Previous Experience Required.

What you'll learn

15-Day Roadmap to AI Mastery: Build, train, and deploy deep learning models in a structured timeline - step by step from beginner to pro

Hands-On Project Focus: Create real-world applications like spam filters, image classifiers, and price predictors to solidify your skills

Practical Deployment Expertise: Transform models into interactive apps with Gradio for immediate, hands-on results

From Basics to Advanced: Dive into neurons, CNNs, transfer learning, and more. Master the PyTorch easily on the way

Effective Data Handling: Learn how to preprocess, optimize, and evaluate diverse data types (images, text, …)

Have fun while learning: Many interactive quizzes and practical exams included

Harness the Power of Transfer Learning with ResNet for Advanced Classification

Master Essential Tools: Python, PyTorch, Jupyter, and Visual Studio Code

Requirements

Basic Python Knowledge: You should be comfortable writing simple Python scripts and understanding common data types, loops, and functions.

No Prior AI/Deep Learning Experience Needed: We’ll start from the ground up, so beginners are welcome.

A Reliable Internet Connection & A Computer: You’ll need to download Python, PyTorch, and related tools, and possibly explore cloud-based resources.

A Willingness to Learn & Experiment: Your curiosity and motivation are the most essential prerequisites. Everything else, we’ll guide you through step-by-step.

Description

Have you ever watched AI automatically classify images or detect spam and thought, “I wish I could do that”? With this course, you’ll learn how to build and deploy your own deep learning models in just 15 days - gaining practical, hands-on experience every step of the way.Why This Course?From day one, you’ll get comfortable with the essential concepts that power modern AI. No fluff, no endless theory - you'll learn by building real-world projects like Spam filters, or image detections. By the end, you won’t just know what neurons and neural networks are - you’ll be able to train, refine, and apply them to projects that truly matter.Who Is This Course For?Absolute beginners eager to break into the world of AI and deep learning.Data enthusiasts who want to strengthen their portfolios with hands-on projects.Developers and data scientists looking to deepen their PyTorch and model deployment skills.Anyone who craves a clear roadmap to mastering deep learning, one day at a time.What Makes This Course Unique?Day-by-Day Progression: Follow a structured, 15-day plan that ensures you never feel lost or overwhelmed.Real-World Projects: Predict used car prices, detect spam in SMS, classify handwritten digits, recognize fashion items—all using deep learning techniques.Modern Tools & Frameworks: Master industry-standard tools like PyTorch and dive into CNNs, transfer learning with ResNet, and more.Practical Deployment: Learn how to turn your trained models into interactive apps with Gradio, making your projects truly come alive.By the End of This Course, You Will:Confidently implement, train, and evaluate deep learning models.Understand how to prepare and process various types of data, from text to images.Know how to improve and optimize your models to achieve better performance.Be ready to deploy your AI solutions, making them accessible and interactive for real users.No Prior Experience NeededWhether you’re a coding novice or a data analyst stepping into AI, this course starts from the very basics. You’ll be guided through installing Python, PyTorch, and setting up your coding environment, all the way to training full-fledged neural networks on your GPU.Get Ready to Dive InIf you’ve always wanted to get into deep learning, now is your chance. Enroll today and join me on a practical, hands-on journey that will transform the way you see and build AI solutions. In 15 days, you’ll have gone from curious beginner to proud deep learning practitioner—with real projects to show for it.

Overview

Section 1: Introduction

Lecture 1 Overview: Practical Deep Learning

Section 2: [Day 1]: Foundations of Neural Networks: From Models and Neurons to Tensors

Lecture 2 Course Materials

Lecture 3 Installing the necessary tools (Windows)

Lecture 4 Installing the necessary tools (Linux)

Lecture 5 Installing the necessary tools (macOS)

Lecture 6 Running a first file

Lecture 7 What is a model?

Lecture 8 A First Neuron

Lecture 9 A First Neuron in Python

Lecture 10 What is a Tensor?

Lecture 11 Handling the Data Type of a Tensor in PyTorch

Lecture 12 Manually Setting Parameters

Section 3: [Day 2]: Neuron Training: From Adjusting Parameters to Batch Learning

Lecture 13 Introduction to Neuron Training

Lecture 14 What is learning?

Lecture 15 How Neuron Learns: A Scalable Approach

Lecture 16 Understanding Gradient Descent for Neuron Optimization

Lecture 17 Training a Neuron 1: Preparing and Optimizing

Lecture 18 Optimizing Training for Our Neuron Model

Lecture 19 Training a Neuron 2: Iterative Learning and Adjustments

Lecture 20 The Importance of Mean Squared Error in Model Training

Lecture 21 Batch Learning and Making Predictions with PyTorch

Section 4: [Day 3 & 4]: Single Neuron Regression: Predicting Used Car Prices with PyTorch

Lecture 22 [Day 3]: Introduction to Predicting Used Car Prices

Lecture 23 Overview of the Used Car Price Dataset

Lecture 24 Getting Started with Jupyter: Interactive Python Programming

Lecture 25 Exploring the Used Car Dataset with Pandas

Lecture 26 Investigating Key Data Relationships for Model Training

Lecture 27 Finalizing Input and Target Columns for Model Training

Lecture 28 Structuring Data for Model Input and Running an Initial Prediction

Lecture 29 Training the Model: Initial Setup and Challenges

Lecture 30 [Day 4]: Understanding Output Normalization for Stable Learning

Lecture 31 Implementing Output Normalization in PyTorch for Consistent Predictions

Lecture 32 Understanding Input Normalization for Consistent Training

Lecture 33 Implementing Input Normalization in PyTorch for Improved Predictions

Lecture 34 Experimenting with Training Parameters Through Loss Visualization

Lecture 35 Saving and Loading Model in PyTorch

Lecture 36 Exercise: Adding an Additional Column to the Model

Lecture 37 Solution: Adding an Additional Column to the Model

Section 5: [Day 5 & 6]: Neuron Classifier: Spam Detection in SMS

Lecture 38 [Day 5]: Introduction to Spam Detection

Lecture 39 Exploring and Preprocessing the SMS Spam Dataset

Lecture 40 Using Count Vectorizer to Transform Text into Numerical Data

Lecture 41 Optional / Extra: Exploring TF-IDF Vectorizer for Improved Text Preprocessing

Lecture 42 Training the Model for Spam Classification

Lecture 43 Optimizing Training for Our Neuron Classifier

Lecture 44 Understanding the Sigmoid Activation Function for Probability Output

Lecture 45 Switching to Binary Cross Entropy Loss for Effective Training

Lecture 46 Using BCE with Sigmoid for Loss Calculation and Prediction

Lecture 47 Evaluating Model with Key Performance Metrics

Lecture 48 [Day 6]: Understanding Training, Validation and Test Data in Model Development

Lecture 49 Implementing Training and Validation Data Splits in Python

Lecture 50 Applying and Evaluating the Model on Fresh Data

Lecture 51 Optional / extra: Improving Spam Detection with Large Language Model Embeddings

Lecture 52 Optional / extra: Generating Embeddings with BART for Spam Detection

Lecture 53 Optional / extra: Building a Function to Generate Embeddings for Spam Detection

Lecture 54 Optional / extra: Integrating Embeddings into the Spam Filter

Section 6: [Day 6]: Exam

Section 7: [Day 7 & 8]: Neural Network Classifier: Student Exam Results Prediction

Lecture 55 [Day 7]: From Single Neuron to Neural Networks

Lecture 56 Optional: Understanding Activation Functions in Neural Networks

Lecture 57 Optional / extra: Exploring Nonlinearity and Its Impact on Neural Networks

Lecture 58 Understanding Backpropagation in Neural Networks

Lecture 59 Optional: Decoding the Mathematics of Backpropagation

Lecture 60 Analyzing Student Performance Data for Exam Predictions

Lecture 61 Optional: Applying a Single Neuron to Student Exam Data

Lecture 62 Building and Training Our First Neural Network

Lecture 63 Optimizing Training for Our Neural Network Classifier

Lecture 64 Evaluating Neural Network Performance

Lecture 65 Simplifying the Code with nn.Sequential

Lecture 66 [Day 8]: Introducing ReLU Activation Function

Lecture 67 Optimizing Training with Adam

Lecture 68 Implementing Mini-Batch Learning for Efficient Training

Lecture 69 Optimizing Loss Tracking in Mini-Batch Training

Section 8: [Day 8]: Exercise: Loan Approval Classification

Lecture 70 Introduction to Loan Approval Prediction

Lecture 71 Exploring the Loan Approval Dataset

Lecture 72 Solution Part 1: Preparing Data for the Loan Approval Model

Lecture 73 Solution Part 2: Building and Training the Loan Approval Model

Section 9: [Day 9 & 10]: Neural Network for Multi-Class Classification: Handwritten Digits

Lecture 74 [Day 9]: Introduction to Handwritten Digit Classification

Lecture 75 Exploring MNIST Data with TorchVision

Lecture 76 From Dataset to DataLoader: Preparing Data for Neural Network

Lecture 77 Building a Binary Classifier for 0 Detection

Lecture 78 Evaluating the Binary Classifier for 0 Detection

Lecture 79 Multi-Class Classification in Neural Networks

Lecture 80 Understanding One-Hot Encoding

Lecture 81 Training a Neural Network for Multi-Class Classification

Lecture 82 Optimizing Training for Our Neural Network Multi-Class Classifier

Lecture 83 Evaluating a Neural Network for Multi-Class Classification

Lecture 84 [Day 10]: Understanding Softmax for Class Probability Normalization

Lecture 85 Applying Softmax in Neural Network

Lecture 86 Experimenting with Different Neural Network Architectures

Lecture 87 Understanding Overfitting in Neural Networks

Lecture 88 Demonstrating Overfitting in Neural Network Training

Lecture 89 Strategies to Counter Overfitting

Lecture 90 Optional / extra: Applying a Neural Network to Custom Images

Lecture 91 Optional / extra: Overcoming Preprocessing Challenges in Model Application

Section 10: [Day 11 & 12]: Convolutional Networks: Fashion Item Classification (multi-class)

Lecture 92 [Day 11]: Introduction to Convolutional Neural Networks

Lecture 93 Exploring Fashion MNIST Data

Lecture 94 Optional: Assessing Previous Model Performance on Fashion MNIST Data

Lecture 95 Exploring Edge Detection with the Sobel Operator

Lecture 96 Understanding the Structure of Convolutional Neural Networks for Edge Detection

Lecture 97 Part 1: Implementing a CNN

Lecture 98 Part 2: Advancing CNN Implementation

Lecture 99 Optimizing Training for Our CNN

Lecture 100 Reducing CNN Complexity with Max Pooling

Lecture 101 Utilizing GPU Acceleration with PyTorch

Lecture 102 Optional: Enabling CUDA on NVIDIA GPUs

Lecture 103 Leveraging Google Colab's Free GPU

Lecture 104 Optimizing Tensor Computations on GPU

Lecture 105 Running Simple Model on GPU

Lecture 106 Accelerating CNN Execution Speed with GPU

Lecture 107 [Day 12]: Advancing CNN Complexity

Lecture 108 Enhancing CNN Performance with Increased Filter Complexity

Lecture 109 Introducing Dropout for Improved Generalization

Lecture 110 Optimizing CNN with Dropout Layers

Lecture 111 Refining CNN with Batch Normalization

Lecture 112 Optional: Understanding the Mathematics of Batch Normalization

Lecture 113 Optional / extra: Application of Overfitting Detection and Model Finalization

Section 11: [Day 13 & 14]: Transfer Learning with ResNet for Tire Quality Prediction

Lecture 114 [Day 13]: Introduction to Transfer Learning and Tire Quality Prediction

Lecture 115 Preparing the Tire Quality Dataset

Lecture 116 Exploring the Tire Quality Dataset

Lecture 117 An Introduction to ResNet in Transfer Learning

Lecture 118 Using ResNet-50 to Classify an Image of a Cat

Lecture 119 Optional / extra: Exploring the ResNet Research Paper

Lecture 120 Preparing Data for ResNet Training

Lecture 121 Part 1: Customizing ResNet-50 for Tire Quality Prediction

Lecture 122 Part 2: Building a Transfer Learning Model for Tire Quality Prediction

Lecture 123 [Day 14]: Part 3: Training the Transfer Learning Model

Lecture 124 Part 4: Evaluating Model Performance and Addressing Overfitting

Lecture 125 Data Augmentation for Combating Overfitting

Lecture 126 Integrating Data Augmentation into Model Training for Improved Accuracy

Lecture 127 Adapting Model Weights for Universal Compatibility

Lecture 128 Using the Trained Model to Predict Tire Quality

Lecture 129 Testing Approaches for Tire Model Deployment

Section 12: [Day 15]: Deploying AI Models with Gradio: From Setup to Real-World Predictions

Lecture 130 Introduction to Deploying AI Models with Gradio

Lecture 131 Getting Started with Gradio for Simple AI Apps

Lecture 132 Uploading and Processing Images with Gradio

Lecture 133 Integrating Gradio with PyTorch for Predictions

Lecture 134 Deploying Gradio for Real-World Tire Predictions

Section 13: [Day 15]: Exam

Section 14: Closing words

Lecture 135 Closing words

Absolute beginners eager to break into the world of AI and deep learning.,Data enthusiasts who want to strengthen their portfolios with hands-on projects.,Developers and data scientists looking to deepen their PyTorch and model deployment skills.,Anyone who craves a clear roadmap to mastering deep learning, one day at a time.