Artificial Intelligence A-Z 2024: Build 7 AI + LLM & ChatGPT
This comprehensive syllabus covers a wide range of AI topics, from fundamentals to cutting-edge techniques.
You’ll start by diving into the basics of reinforcement learning, a powerful approach to training AI agents.
The course introduces concepts like the Bellman Equation, Markov Decision Processes, and Q-Learning through intuitive explanations and hands-on implementations.
This solid foundation prepares you for more advanced techniques.
Next, you’ll explore Deep Q-Learning, a neural network-based approach to reinforcement learning.
The syllabus walks you through the intuition behind Deep Q-Learning, experience replay, and action selection policies.
You’ll then implement Deep Q-Learning step-by-step, giving you practical experience with this widely-used technique.
The course also covers Deep Convolutional Q-Learning, which combines reinforcement learning with convolutional neural networks (CNNs).
You’ll learn about eligibility traces and implement Deep Convolutional Q-Learning from scratch, gaining valuable skills in computer vision and image processing.
Moving on, you’ll dive into A3C (Asynchronous Advantage Actor-Critic), a state-of-the-art reinforcement learning algorithm.
The syllabus explains the three A’s in A3C – Asynchronous, Advantage, and Actor-Critic – and guides you through implementing this advanced technique using LSTMs.
Excitingly, the course includes a section on building and training PPO (Proximal Policy Optimization) and SAC (Soft Actor-Critic) models for self-driving cars.
You’ll learn the theory behind these cutting-edge algorithms and apply them to a real-world problem.
The syllabus also covers Large Language Models (LLMs) like ChatGPT.
You’ll gain insights into how LLMs work, their ingredients, and the process of fine-tuning them.
The course even includes a hands-on implementation of fine-tuning LLMs using Hugging Face.
Throughout the course, you’ll have access to code repositories and a PDF handbook to support your learning.
Additionally, there are supplementary sections on artificial neural networks, convolutional neural networks, and advanced Q-Learning topics.
Complete A.I. & Machine Learning, Data Science Bootcamp
Starting with an introduction to the online classroom, this course fosters a supportive learning environment where you can easily ask questions and interact with peers and instructors.
You’ll quickly move into practical applications, exploring machine learning through engaging exercises like the Machine Learning Playground and building a YouTube Recommendation Engine.
The course breaks down complex topics into understandable segments, covering different types of machine learning, data evaluation, and the six-step machine learning framework.
Key sections include hands-on training in data manipulation with Pandas and NumPy, data visualization with Matplotlib, and model building with Scikit-learn.
You’ll tackle real-world projects on supervised learning, enhancing your problem-solving skills and applying theoretical knowledge in practical scenarios.
For those interested in the latest in A.I., the course also covers neural networks, deep learning, and TensorFlow 2, teaching you to manage unstructured data and leverage Google Colab and GPUs.
Beyond technical skills, you’ll learn to effectively present your work, ensuring you can communicate complex ideas clearly to various audiences.
Career advice, contributions to open source projects, and a thorough Python programming section are included to round out your education, making this bootcamp a solid foundation for anyone looking to enter the fields of machine learning and data science.
Deep Learning Prerequisites: The Numpy Stack in Python (V2+)
Starting with a clear introduction, it sets expectations on the learning outcomes and the practical application of skills acquired.
The course covers Numpy, introducing essential concepts like arrays, dot products, and matrices, which are foundational for numerical computations in Python.
Following Numpy, you’ll explore Matplotlib for data visualization, learning to create impactful charts and plots.
The Pandas section equips you with skills for data manipulation and analysis, crucial for handling real-world data sets.
Scipy is also covered, focusing on scientific computing techniques that are vital in machine learning tasks.
Practical exercises after each section provide hands-on experience, reinforcing your learning and preparing you for applying these skills in machine learning projects.
The course also addresses setting up your environment, ensuring you’re ready to use tools like TensorFlow and Theano, and offers Python coding assistance for beginners.
Tensorflow 2.0: Deep Learning and Artificial Intelligence
This comprehensive guide to TensorFlow 2.0 covers deep learning and artificial intelligence extensively.
You’ll gain practical coding experience from the outset, working with real datasets and leveraging free GPU/TPU resources on Google Colab to train advanced models effortlessly.
The course begins by explaining core machine learning concepts like neurons and how models learn.
You’ll then build feedforward artificial neural networks (ANNs) for image classification and regression tasks.
Mastering convolutional neural networks (CNNs), you’ll apply them to image data like CIFAR-10 and Fashion MNIST, incorporating techniques like data augmentation and batch normalization.
For sequence data like time series, you’ll explore recurrent neural networks (RNNs) including LSTMs and GRUs, covering applications in forecasting, text classification, and using RNNs for image tasks.
Natural language processing (NLP) is comprehensively covered, delving into embeddings, text preprocessing, and utilizing CNNs/RNNs for NLP tasks.
Other key topics include recommender systems, transfer learning for computer vision, generative adversarial networks (GANs), and deep reinforcement learning theory/applications like a stock trading agent.
You’ll learn low-level TensorFlow usage, building custom models, loss functions, optimization methods like Adam, and distributed training strategies.
Deploying models as web services using TensorFlow Serving and TensorFlow Lite for mobile apps is also covered.
Throughout, you’ll find helpful tips on setting up environments, using Python/Jupyter, effective learning strategies, and preparing for the TensorFlow Developer Certificate exam.
Machine Learning for Absolute Beginners - Level 1
You’ll start by understanding the rise of AI and its different types like classical programming, machine learning, and deep learning.
The course explains the distinction between applied and generalized AI, giving you context on why AI is becoming so prevalent now.
Next, you’ll dive into the basics of machine learning.
The “black box” metaphor helps demystify how ML models work.
You’ll learn about features, labels, training models, and the importance of generalization.
This lays the groundwork for the different categories of ML systems.
The course classifies ML into three main types: supervised, unsupervised, and reinforcement learning.
For supervised learning, you’ll explore classification (predicting categories) and regression (predicting numerical values).
Unsupervised techniques like clustering and dimension reduction are covered too.
Reinforcement learning, where an agent learns by trial-and-error in an environment, is also introduced.
You’ll understand how these decision-making agents work through examples.
Throughout the course, you’ll encounter quizzes that reinforce the key concepts.
And there’s even a bonus lecture at the end!
Artificial Intelligence & Machine Learning for Business
You’ll start by understanding what AI and ML are, their history, and the different types of ML like supervised, unsupervised, and reinforcement learning.
The course explains the capabilities and limitations of AI compared to humans, helping you determine when to apply these technologies.
It then dives into the potential risks of advanced AI systems like ANI (Artificial Narrow Intelligence), AGI (Artificial General Intelligence), and ASI (Artificial Super Intelligence).
You’ll learn about recent AI breakthroughs and how the world is changing due to these advancements.
The syllabus covers the building blocks of AI, common terminologies used in the field, and the tools and techniques employed for data capturing, processing, and model building.
From decision trees to neural networks, you’ll gain insights into various ML algorithms.
But that’s not just theory.
The course explores real-world applications of AI across industries like banking, e-commerce, healthcare, and telecom.
It also shows how AI can revolutionize different business functions such as HR, sales, operations, marketing, and supply chain management.
If you’re keen on implementing AI in your organization, the course guides you through developing an AI strategy, the level of change required, and timelines.
It discusses the key roles in an AI team and how to recruit top talent.
Moreover, the syllabus addresses common misconceptions about AI and ML, ensuring you separate fact from fiction.
It walks you through the entire AI creation process, from problem definition to model evaluation, using tools like OpenAI and guidelines from Google.
Finally, the course examines the promises of AI in driving innovation and efficiency, as well as the challenges and ethical considerations around developing safe and responsible AI systems.
Artificial Intelligence: Reinforcement Learning in Python
You’ll begin by exploring the explore-exploit dilemma through multi-armed bandit problems, implementing algorithms like epsilon-greedy, UCB1, and Thompson sampling.
From there, you’ll dive into the core concepts of reinforcement learning, including Markov decision processes, the Bellman equation, and value functions.
The course walks you through coding dynamic programming algorithms like policy iteration and value iteration for gridworld environments.
You’ll also learn Monte Carlo methods for policy evaluation and control, as well as temporal difference learning techniques like SARSA and Q-learning.
The syllabus covers using function approximation with linear models for more complex problems like the CartPole game.
To solidify your understanding, there’s a stock trading project where you’ll apply Q-learning to develop a trading strategy.
The course even includes supplementary sections on setting up your Python environment with libraries like NumPy, SciPy, Matplotlib, and TensorFlow, as well as effective learning strategies for machine learning.
Deep Learning Prerequisites: Linear Regression in Python
The course starts by explaining what linear regression is and how it fits into the broader machine learning ecosystem.
You’ll learn to code simple 1-dimensional linear regression models in Python and measure their performance using metrics like R-squared.
The syllabus covers deriving the mathematical solutions and implementing them in code.
As you progress, the course dives into multiple linear regression, allowing you to model scenarios with multiple input variables.
You’ll also learn how to apply linear regression to polynomial data using Python libraries like Numpy and Scipy.
Crucially, the course addresses practical machine learning issues like generalization error, overfitting, and cross-validation.
You’ll understand how to split data into train and test sets, interpret model weights, and use techniques like regularization (L1 and L2) to improve performance.
The syllabus covers working with different data types, including categorical inputs using one-hot encoding.
You’ll even learn about the probabilistic interpretation of squared error and how to bypass the dummy variable trap using gradient descent.
While focused on linear regression, the course provides a glimpse into advanced machine learning concepts like deep learning using frameworks like TensorFlow.
It also offers guidance on setting up your coding environment with tools like Anaconda and Jupyter Notebook.
Complete 2022 Data Science & Machine Learning Bootcamp
You’ll start by learning the basics of machine learning and data science.
The course then dives into your first project - predicting movie box office revenue using linear regression.
This hands-on project will teach you how to gather, clean, explore, and visualize data using Python.
You’ll also understand the intuition behind linear regression models and analyze the results.
Next, you’ll build a strong foundation in Python programming, covering variables, lists, dataframes, functions, objects, and coding best practices.
This section is crucial for data science and machine learning tasks.
The course then introduces you to the gradient descent algorithm, a fundamental optimization technique used in machine learning.
You’ll learn about cost functions, partial derivatives, and implement batch gradient descent using SymPy.
Visualizations like 3D plots will help you grasp these concepts better.
In the next project, you’ll predict house prices in Boston using multivariable linear regression.
You’ll clean and explore the data, handle missing values, visualize distributions and outliers, and understand correlation and multicollinearity.
After building the regression model, you’ll evaluate it using metrics like R-squared and analyze residuals.
Text data preprocessing is covered through a project on building a naive Bayes classifier to filter spam emails.
You’ll learn techniques like tokenization, stemming, and removing stop words and HTML tags.
You’ll also create word clouds and visualize data using pie and donut charts.
The course then dives into neural networks, explaining their inspiration from the human brain.
You’ll use pre-trained models like InceptionResNet for image classification.
In another project, you’ll build an artificial neural network from scratch using Keras and TensorFlow to recognize images from the CIFAR-10 dataset.
You’ll preprocess data, compile models, use regularization techniques, and evaluate performance using metrics like the confusion matrix.
Additionally, you’ll learn to classify handwritten digits using TensorFlow, understanding tensors and the TensorFlow graph.
You’ll also serve a TensorFlow model through a website, using HTML, CSS, and JavaScript along with tools like OpenCV for image preprocessing.
Throughout the course, you’ll work on coding exercises and challenges to reinforce your learning.
The instructor provides complete notebooks for reference and encourages feedback to improve the course content continually.
Advanced AI: Deep Reinforcement Learning in Python
You’ll start with the fundamentals of reinforcement learning, exploring concepts like states, actions, rewards, policies, and Markov Decision Processes (MDPs).
The course dives deep into the Bellman equation, Q-learning, and epsilon-greedy algorithms, ensuring you grasp the theoretical foundations.
Next, you’ll get hands-on experience with OpenAI Gym, a powerful toolkit for developing and testing reinforcement learning algorithms.
You’ll implement techniques like random search, binning, and radial basis function (RBF) neural networks on classic environments like CartPole and Mountain Car.
The course then covers advanced topics like TD Lambda, which combines Monte Carlo and temporal difference methods.
You’ll also delve into policy gradient methods, tackling continuous action spaces with algorithms like REINFORCE on environments like Mountain Car Continuous.
Deep Q-learning, a breakthrough in combining deep neural networks with Q-learning, is explored in-depth.
You’ll implement it using TensorFlow and Theano on Atari games like Breakout, learning techniques like experience replay and handling partial observability.
The cutting-edge Asynchronous Advantage Actor-Critic (A3C) algorithm is also covered, with step-by-step code walkthroughs in Python.
You’ll gain insights into parallelizing reinforcement learning for improved performance.
Recognizing the importance of solid foundations, the course includes comprehensive reviews of Theano, TensorFlow, and Python coding best practices.
It also offers effective learning strategies tailored for machine learning and AI.