easylearn.ing
Easy Learning with Custom ChatGPT Publishing & AI Bootcamp Masterclass
Development > Data Science
31.5 h
£14.99 £12.99
4.3 ★★★★★
24485 students

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Language: English

AI & ChatGPT Mastery: Build 150+ Projects From Scratch

What you will learn:

  • Master Python programming fundamentals.
  • Understand AI, machine learning, and neural networks.
  • Learn essential math (algebra, calculus, statistics).
  • Build and deploy 150+ practical AI and ChatGPT projects.
  • Customize and publish ChatGPT models.
  • Become proficient in AI problem-solving.
  • Develop strong coding skills through video tutorials.
  • Master data handling, preprocessing, and model evaluation.
  • Learn best practices for AI project deployment.
  • Build confidence to tackle advanced AI challenges.

Description

Become an AI and ChatGPT expert from scratch! This comprehensive bootcamp masterclass provides a practical, hands-on learning journey to the exciting world of artificial intelligence. Dive into Python programming, master fundamental math concepts (algebra, calculus, statistics), and unlock the secrets of machine learning and neural networks. Unlike other courses, this immersive experience includes over 150 projects, empowering you to build real-world applications and deploy customized ChatGPT models. Beginners with no prior programming or AI experience will discover how to construct, optimize, and publish their own ChatGPT systems.

We'll guide you through essential Python skills, from basic syntax to advanced data manipulation with NumPy and Pandas. You'll build a robust foundation in mathematics, which is critical for understanding AI algorithms. The course then delves into the core principles of artificial intelligence, covering neural networks and deep learning. This curriculum culminates in mastering the art of ChatGPT customization and deployment, teaching you to create chatbots tailored for your unique needs and publish them for a wider audience.

With detailed video tutorials, each step is clearly explained, making complex concepts readily accessible. This masterclass isn't just about theory; it's about doing. Build confidence in your AI skills by creating innovative AI-powered tools and applications. From simple AI utilities to advanced ChatGPT implementations, you'll expand your skillset with every project. The course focuses on practical application and direct implementation, ensuring you're ready to tackle real-world challenges and confidently enter the dynamic field of artificial intelligence.

Enroll now and start building your AI and ChatGPT expertise! This course empowers you to leverage the power of artificial intelligence, opening doors to exciting career opportunities and countless creative projects. Don't wait—begin your journey into the AI revolution today!

Curriculum

Creating and Publishing GPTs to ChatGPT Store

This section provides a detailed walkthrough of publishing your GPTs on the official store. You'll learn the process in three parts, covering everything from initial creation to final publishing. Part 1 lays the groundwork, Part 2 dives into implementation and optimization, and Part 3 ensures a successful launch of your AI creation.

Week 1: Python Programming Basics

This foundational week introduces Python programming. You'll start with setting up your development environment and covering fundamental concepts such as control flow, functions, modules, data structures (lists, tuples, dictionaries, sets), string manipulation, file handling, and writing clean, efficient Python code with daily practical projects.

Week 2: Data Science Essentials

This week delves into essential data science tools and techniques. You will learn NumPy for numerical computing, master data manipulation with Pandas, learn data cleaning and preparation, data aggregation, and data visualization using Matplotlib and Seaborn, culminating in a comprehensive exploratory data analysis (EDA) project.

Week 3: Mathematics for Machine Learning

This section covers the mathematical foundations of machine learning. You'll learn fundamental linear algebra, advanced linear algebra concepts, calculus (derivatives and integrals), probability theory, and statistics. Each topic is coupled with explanations and practical examples to reinforce learning, culminating in a mini-project applying linear regression from scratch.

Week 4: Probability and Statistics for Machine Learning

Build upon the math foundations by diving deeper into probability theory, probability distributions, statistical inference, hypothesis testing, correlation, and regression analysis. Practical application is prioritized, and you’ll conclude with a project analyzing real-world datasets.

Week 5: Introduction to Machine Learning

This week provides an introduction to core machine learning concepts, including terminology, supervised learning, regression models (including polynomial and regularization), classification, logistic regression, model evaluation, cross-validation, and the k-Nearest Neighbors algorithm, and a supervised learning project.

Week 6: Feature Engineering and Model Evaluation

Become proficient in feature engineering, covering data scaling, normalization, encoding categorical variables, feature selection, and creating and transforming features. You will also master model evaluation techniques and hyperparameter tuning using cross-validation.

Week 7: Advanced Machine Learning Algorithms

This section covers advanced machine learning algorithms, including ensemble learning, bagging, random forests, boosting, gradient boosting, XGBoost, LightGBM, CatBoost, and handling imbalanced data. A project comparing models on real datasets completes the week.

Week 8: Model Tuning and Optimization

Focus on hyperparameter tuning using grid search, random search, Bayesian optimization, regularization techniques, and advanced cross-validation methods. The practical capstone project involves building and tuning a final model for optimal performance.

Week 9: Neural Networks and Deep Learning Fundamentals

Begin your journey into the world of neural networks and deep learning, exploring fundamental concepts such as neural network architectures, activation functions, loss functions, backpropagation, gradient descent, and building neural networks using TensorFlow and PyTorch. A project focuses on image classification using CIFAR-10.

Week 10: Convolutional Neural Networks (CNNs)

Learn the intricacies of Convolutional Neural Networks (CNNs), including convolutional layers, filters, pooling layers, building CNN architectures using Keras and PyTorch, regularization, and data augmentation. A project involves image classification on Fashion MNIST or CIFAR-10.

Week 11: Recurrent Neural Networks (RNNs) and Sequence Modeling

Dive into Recurrent Neural Networks (RNNs) and sequence modeling, covering RNN architecture, backpropagation through time (BPTT), LSTM networks, GRUs, text preprocessing, word embeddings, sequence-to-sequence models, and a final project focusing on text generation or sentiment analysis.

Week 12: Transformers and Attention Mechanisms

Explore the powerful Transformer architecture, covering attention mechanisms, self-attention, multi-head attention, positional encoding, and working with pre-trained Transformers like BERT and GPT. The project involves text summarization or translation.

Week 13: Transfer Learning and Fine-Tuning

Master transfer learning and fine-tuning techniques in computer vision and NLP, explore domain adaptation, and address associated challenges. The final project involves fine-tuning a model for a custom task.