Build, train, and deploy machine learning models for real-world applications like classification, regression, and NLP tasks.
Master essential AI concepts, including neural networks, data preprocessing, model evaluation, and text processing.
Deploy AI models as web services using Flask, enabling real-time user interaction and cloud deployment on platforms like Heroku.
Utilize pre-trained models and transfer learning techniques to quickly implement NLP and image classification tasks.
Requirements
No Prior Coding Knowledge Required: The course starts from the basics of Python, so no prior programming experience is needed.
Basic Computer Usage: Learners should know how to navigate their computer, install software, and use a web browser.
Basic Math Knowledge: Understanding of algebra and basic linear algebra (matrices, vectors) will be helpful.
Computer with Internet Access: A laptop or desktop is needed to install software, download data, and follow the lessons.
Description
Welcome to "7 Days of Hands-On AI Development Bootcamp: Build Real-World AI Projects from Scratch," a course designed for absolute beginners who are eager to step into the world of artificial intelligence (AI). This course is ideal for those with little to no prior experience in programming or AI but have the curiosity and drive to learn. Whether you're a student, a career-changer, or simply interested in building your first AI project, this course is structured to take you from zero knowledge to deploying real-world AI models.Over the span of 7 days, you'll build projects every day, starting from the basics of Python programming to deploying a fully-functional AI model on the web. Each day is packed with hands-on projects, practical applications, and easy-to-follow instructions to ensure that you gain not just theoretical knowledge but real-world skills that you can apply right away.What You Will Learn:This course covers everything you need to get started with AI development. Each day is focused on a new topic, gradually building on what you've learned previously. Here's a brief overview of what you can expect:Day 1: Python for AI BasicsWe start with the foundation—Python programming. Python is the most popular language for AI, and by the end of Day 1, you'll understand basic Python syntax, data types, control flow, and how to use essential libraries like NumPy and Pandas. You'll also build your first simple program, setting the stage for the AI projects to come.Day 2: Exploratory Data Analysis (EDA)Data is the backbone of AI, and before you can train models, you need to know how to analyze it. On Day 2, you will learn how to clean, manipulate, and visualize data. Using libraries like Matplotlib and Seaborn, you'll explore datasets, handle missing data, and visualize relationships between different features. You'll work with real-world data to uncover hidden insights.Day 3: Introduction to Machine LearningOn Day 3, we dive into machine learning with a focus on Linear Regression. You'll learn the fundamentals of supervised learning, including how to split your dataset into training and testing sets, train a model, and evaluate its performance. By the end of the day, you'll build your first predictive model to forecast continuous variables like house prices.Day 4: Classification Models in Machine LearningNext, you'll tackle classification problems using Logistic Regression. Whether predicting if an email is spam or classifying customer churn, this day teaches you how to build a classification model and evaluate it using metrics like precision, recall, and accuracy. You'll also learn how to interpret confusion matrices to understand the performance of your model.Day 5: Neural Networks and Deep LearningDay 5 introduces the fascinating world of neural networks. You'll build a simple feedforward neural network to classify handwritten digits using the MNIST dataset. You'll gain hands-on experience with libraries like TensorFlow or PyTorch, and learn about key concepts such as activation functions, backpropagation, and training deep learning models.Day 6: Natural Language Processing (NLP)Day 6 focuses on Natural Language Processing (NLP), where you'll build a sentiment analysis model using text data. By leveraging pre-trained models from Hugging Face or building your own with TensorFlow, you'll classify text as positive or negative. This day provides an introduction to text preprocessing, tokenization, and transfer learning in NLP.Day 7: Deploying an AI Model as a Web ServiceOn the final day, you'll learn how to deploy your AI models as a web service using Flask. You'll integrate your AI models into a web application, making them accessible to users via a browser. Additionally, you'll deploy your app to a cloud platform like Heroku. By the end of the day, you'll have a working AI-powered web app that anyone can interact with online.Who This Course is For:Absolute Beginners: No prior programming or AI knowledge is required. This course is designed to be beginner-friendly.Students: If you're studying AI, machine learning, or data science, this course will give you the practical hands-on experience to solidify your learning.Career-Changers: If you're looking to switch to a career in AI or machine learning, this course will give you the foundation to start your journey.Hobbyists and Enthusiasts: If you're simply curious about AI and want to build projects for fun, this course will provide you with easy-to-follow instructions.Why Take This Course?This course is not just about theory—it's about building. You'll have real projects in your portfolio by the end of the week. Each day is packed with practical coding exercises and project-building that makes learning AI development easy and approachable. Whether you want to boost your career, impress employers, or explore the world of AI for personal interest, this course is designed to make that journey engaging, interactive, and rewarding.So, are you ready to build AI projects from scratch in just 7 days? Let's get started!
Overview
Section 1: Introduction to Course
Lecture 1 Introduction to Course
Section 2: Day 1: Python for AI Basics
Lecture 2 Introduction to Day 1: Python for AI Basics
Lecture 3 Introduction to Python Programming
Lecture 4 Python Basics
Lecture 5 Working with Lists and Dictionaries
Lecture 6 Introduction to NumPy
Lecture 7 Introduction to Pandas
Lecture 8 Hands-on Project: Basic Data Manipulation and File Handling
Section 3: Day 2: Exploratory Data Analysis (EDA)
Lecture 9 Introduction to Day 2: Exploratory Data Analysis (EDA)
Lecture 10 Loading and Inspecting Data
Lecture 11 Handling Missing Data
Lecture 12 Data Transformation and Feature Engineering
Lecture 13 Visualizing Data with Matplotlib and Seaborn
Lecture 14 Descriptive Statistics
Lecture 15 Hands-on Project: Exploratory Data Analysis on a Real Dataset
Section 4: Day 3: Introduction to Machine Learning (ML)
Lecture 16 Introduction to Day 3: Introduction to Machine Learning (ML)
Lecture 17 What is Machine Learning?
Lecture 18 Supervised Learning and Dataset Preparation
Lecture 19 Building a Linear Regression Model
Lecture 20 Evaluating the Model
Lecture 21 Feature Scaling and Regularization
Lecture 22 Hands-on Project: Predicting House Prices using Linear Regression
Section 5: Day 4: Classification Models in Machine Learning
Lecture 23 Introduction to Day 4: Classification Models in Machine Learning
Lecture 24 What is Classification?
Lecture 25 Logistic Regression for Classification
Lecture 26 Building a Logistic Regression Classifier
Lecture 27 Evaluating the Classification Model
Lecture 28 Visualizing the Decision Boundary
Lecture 29 Hands-on Project: Spam Detection Using Logistic Regression
Section 6: Day 5: Introduction to Neural Networks and Deep Learning
Lecture 30 Introduction to Day 5: Introduction to Neural Networks and Deep Learning
Lecture 31 What is a Neural Network?
Lecture 32 Introduction to Deep Learning Frameworks
Lecture 33 MNIST Dataset Overview
Lecture 34 Building a Simple Neural Network for MNIST Classification
Lecture 35 Evaluating the Neural Network
Lecture 36 Understanding Activation Functions
Lecture 37 Hands-on Project: Handwritten Digit Classification using Neural Networks
Section 7: Day 6: Building a Sentiment Analysis Model Using Natural Language Processing
Lecture 38 Introduction to Day 6: Building a Sentiment Analysis Model Using NLP
Lecture 39 Introduction to Natural Language Processing (NLP)
Lecture 40 Sentiment Analysis: Understanding Text Classification
Lecture 41 Text Preprocessing
Lecture 42 Using Pre-trained Models for NLP (Hugging Face)
Lecture 43 Building a Sentiment Analysis Model with TensorFlow
Lecture 44 Evaluating the Model
Lecture 45 Hands-on Project: Sentiment Analysis Using Pre-trained NLP Models
Section 8: Day 7: Deploying an AI Model as a Web Service
Lecture 46 Introduction to Day 7: Deploying an AI Model as a Web Service
Lecture 47 Introduction to Flask for Web Development
Lecture 48 Creating a Web Interface for Your Model
Lecture 49 Deploying the Flask App to Heroku
Lecture 50 Testing the Web Service
This course is designed for absolute beginners who are eager to learn AI development but have little to no prior experience in programming or machine learning. It is perfect for individuals who are curious about how AI works and want to get hands-on experience building real-world AI projects from scratch. Whether you're a student, career changer, or hobbyist, this course provides step-by-step guidance, making complex AI concepts accessible. If you're motivated to learn how to build AI models and deploy them, even without a technical background, this course is for you!

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