7 Days Of Hands-On Ai Development Bootcamp
Published 10/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
From Zero to AI: A Beginner’s Guide to Building and Deploying AI Projects
What you’ll learn
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
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
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!
