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Python for AI, ML, DL, Robotics (Intermediate)
This 4-week intermediate course, "Python for AI, ML, DL, and Robotics," offered by the AI Agent Innovation Academy, dives deep ...
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Instructor
Jason James
Course details
Duration
:
4 weeks
Lectures
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18
Assignments
:
5
Quizzes
:
4
Level
:
Intermediate Courses
Python Development of AI, ML, DL, & Robotics (Intermediate) Certification
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Python for AI, ML, DL, and Robotics (Intermediate)
Offered by the AI Agent Innovation Academy
Week 1: Advanced Python for AI Applications
- Object-Oriented Programming in Python for AI
- Working with Python Libraries (NumPy, pandas, matplotlib)
- Efficient Data Handling and Manipulation
- Debugging, Testing, and Profiling AI Code
Assignment: Refactor beginner scripts using best practices
Week 2: Machine Learning and Data Engineering
- Feature Engineering and Selection Techniques
- Implementing ML Models with scikit-learn
- Model Evaluation: Cross-validation, Confusion Matrix, ROC Curve
- Data Preprocessing Pipelines for Real-World Datasets
Hands-on: Building and tuning a supervised ML pipeline
Week 3: Deep Learning & Neural Networks in Practice
- Advanced Neural Network Architectures (CNNs, RNNs, LSTMs)
- Transfer Learning and Model Fine-tuning
- Introduction to TensorFlow and PyTorch
- Training on GPUs: Setup and Workflow
Project: Image or Text Classification Challenge
Week 4: Applied Robotics and AI Integration
- Advanced Robotics with Python (ROS, OpenCV)
- Sensor Fusion Techniques and Data Integration
- AI Control Algorithms for Autonomous Robots
Project: Design and Simulate a Robot with AI-based Navigation
Final Assessment & Peer Review
Each week combines interactive lectures, coding exercises, mini-projects, and quizzes to reinforce learning. The course culminates with a capstone robotics project where students apply AI and Python concepts in a real-world scenario.
Upon completion, learners receive an Academy Certificate and portfolio feedback.
What prerequisites are needed for the 'Python for AI, ML, DL, and Robotics (Intermediate)' course?
Students should have a basic understanding of Python programming and familiarity with fundamental concepts in AI, machine learning, and data science.
Prerequisites: AI Essentials, Fundamentals of AI courses.
Prerequisites: AI Essentials, Fundamentals of AI courses.
What programming libraries will be used in this course?
The course will focus on advanced usage of Python libraries such as NumPy, pandas, matplotlib, TensorFlow, PyTorch, and OpenCV.
What is the final project for this course?
The final project involves designing and simulating a robot with AI-based navigation, applying the knowledge and skills acquired throughout the course.
Will students receive a certificate upon completion?
Yes, students will receive an Academy Certificate and portfolio feedback upon successfully completing the course.
Prerequisites
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Python for AI, ML, DL, Robotics (Basics)
$350
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What is Prerequisite coursesA prerequisite is a specific course that you must complete before you can take another course at the next grade level.
Become Certified in Python for AI, ML, DL & Robotics (intermediate)
Advanced Python for AI Applications
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1
Object-Oriented Programming in Python for AIIn this lesson, "Mastering Object-Oriented Programming in Python for AI Applications," students will delve into the core principles of object-oriented programming (OOP) and explore how to effectively apply these concepts to enhance artificial intelligence projects. By the end of the session, learners will be equipped with the skills to design and implement robust OOP structures, enabling them to create scalable AI solutions that are both efficient and maintainable. Join us to unlock the potential of OOP in the dynamic field of AI! -
2
Working with Python Libraries (NumPy, pandas, matplotlib)In this dynamic lesson, "Unlocking Data Power," students will dive into the essential Python libraries—NumPy, pandas, and Matplotlib—equipping themselves with the skills to manipulate, analyze, and visualize data effectively. By the end of the session, learners will confidently leverage these powerful tools to unlock insights from data, transforming complex datasets into meaningful visual representations. Get ready to enhance your data handling capabilities and make informed decisions with Python! -
3
Efficient Data Handling and ManipulationIn this lesson, "Mastering Efficient Data Handling and Manipulation Techniques," students will dive into the essential strategies for optimizing data workflows and enhancing analytical skills. Participants will learn how to effectively organize, process, and manipulate large datasets using advanced techniques, ensuring they can work smarter, not harder, in their data-driven projects. By the end of the lesson, students will be equipped with practical tools and methodologies to improve their data management practices and drive insightful decision-making.
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4
Debugging, Testing, and Profiling AI CodeIn this lesson, "Mastering AI Code: Debugging, Testing, and Profiling Techniques," students will explore essential strategies for effectively debugging, testing, and profiling their AI code to optimize performance and reliability. By the end of the session, learners will have the skills to identify and resolve coding issues, conduct thorough testing to ensure functionality, and utilize profiling tools to enhance the efficiency of their AI applications. Join us to elevate your coding proficiency and boost the reliability of your AI projects!
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5
Upgrade Your Code: Refactoring Beginner Scripts with Best Practices -
6
Quiz: Advanced Python Concepts
Machine Learning and Data Engineering
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7
Feature Engineering and Selection TechniquesIn this lesson, "Mastering Feature Engineering and Selection Techniques for Data Success," students will explore essential methods for transforming raw data into valuable features that enhance model performance. By the end of the lesson, participants will gain practical skills in identifying, creating, and selecting the most impactful features, empowering them to boost their data analysis and predictive modeling outcomes effectively. Join us to unlock the secrets of effective feature engineering and selection!
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8
Implementing ML Models with scikit-learnIn this dynamic lesson, students will dive into the world of machine learning by mastering the implementation of models using Scikit-Learn. Participants will learn to build, evaluate, and fine-tune various machine learning algorithms, empowering them to transform data into actionable insights. By the end of the session, students will have the confidence and skills to apply Scikit-Learn in real-world scenarios, unlocking the potential of predictive analytics.
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9
Model Evaluation: Cross-validation, Confusion Matrix, ROC CurveIn this lesson, "Mastering Model Evaluation," students will dive into essential techniques for assessing the performance of machine learning models, including cross-validation, confusion matrix, and ROC curve analysis. By the end, learners will confidently interpret these evaluation tools to enhance model accuracy and make informed decisions based on their results, empowering them to elevate their data science skills to the next level.
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10
Data Preprocessing Pipelines for Real-World DatasetsIn this lesson, "Mastering Data Preprocessing Pipelines for Real-World Datasets," students will explore the essential techniques for transforming raw data into a clean, structured format ready for analysis. Participants will gain hands-on experience in building efficient preprocessing pipelines, learning to handle missing values, normalize data, and implement feature engineering to enhance model performance. By the end of the lesson, students will be equipped with practical skills to tackle real-world datasets confidently.
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11
Mastering Supervised Machine Learning: Build and Tune Your Own Pipeline
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12
Quiz: Machine Learning Techniques
Deep Learning & Neural Networks in Practice
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13
Advanced Neural Network Architectures (CNNs, RNNs, LSTMs)In this lesson, students will delve into the intricacies of advanced neural network architectures, focusing on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory networks (LSTMs). Through engaging discussions and hands-on examples, learners will gain a deep understanding of how these architectures function, their unique applications, and how to effectively implement them in real-world projects. Prepare to unlock the potential of deep learning as we explore these powerful tools together!
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14
Transfer Learning and Model Fine-tuningIn this lesson, "Mastering Transfer Learning: Techniques for Effective Model Fine-Tuning," students will explore the fundamental concepts of transfer learning and discover how to adapt pre-trained models for specific tasks. Through hands-on exercises, participants will gain practical skills in fine-tuning techniques, enabling them to enhance model performance and accelerate development in their machine learning projects. Join us to unlock the potential of transfer learning and elevate your AI capabilities!
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15
Introduction to TensorFlow and PyTorchIn this engaging lesson, students will embark on a journey into the exciting world of deep learning by exploring the fundamentals of TensorFlow and PyTorch. Participants will learn how to set up these powerful libraries, understand their core concepts, and develop their first simple neural networks, laying a strong foundation for future projects in artificial intelligence. Get ready to unlock the potential of machine learning with hands-on experience and practical examples!
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16
What is TensorFlow?
Building neural networks with Tensorflow doesn’t need to be a nightmare. If you follow a couple of key steps you can be up and running and using Tensorflow to predict a whole bunch of stuff. In fact, you can learn how to do it with Python in just 10 minutes.
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17
What is PyTorch?
PyTorch is a popular open-source framework for machine learning and deep learning, backed by the PyTorch Foundation. It simplifies data preparation, model building, and training with features like data loaders, pre-defined layers, automatic gradient calculation, and optimization functions. In this video, PyTorch contributor Sahdev Zala and Distinguished Engineer Brad Topol present PyTorch's key features and explain why you should consider becoming a contributor.
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18
Training on GPUs: Setup and WorkflowIn this lesson, "Mastering GPU Training: Setup and Workflow Essentials," students will delve into the critical components of setting up a GPU environment for deep learning. They will learn how to efficiently configure their systems, manage workflows, and optimize performance to enhance their training processes, equipping them with the skills needed to leverage the power of GPUs effectively. Join us to unlock the potential of accelerated computing in your machine learning projects!
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19
Mastering Image and Text Classification: A Hands-On Challenge
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20
Quiz: Deep Learning Fundamentals
Applied Robotics and AI Integration
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21
Advanced Robotics with Python (ROS, OpenCV)In this captivating lesson, students will dive into the world of advanced robotics by harnessing the power of Python, ROS (Robot Operating System), and OpenCV for computer vision tasks. Participants will learn to integrate and program complex robotic systems, enabling them to develop sophisticated applications that can perceive and navigate their environment effectively. By the end of the course, students will not only gain hands-on experience but also build a solid foundation for tackling real-world robotic challenges.
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22
Sensor Fusion Techniques and Data IntegrationIn this lesson, "Mastering Sensor Fusion: Techniques for Effective Data Integration," students will explore the fundamental concepts and methodologies behind sensor fusion, learning how to seamlessly integrate data from multiple sources to enhance accuracy and insights. By the end of the session, participants will be equipped with practical techniques to analyze and implement sensor fusion strategies in real-world applications, unlocking the potential for smarter decision-making and innovative solutions.
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23
How Does Sensor Fusion Address Data Synchronization?
How Does Sensor Fusion Address Data Synchronization? In this informative video, we’ll take a closer look at the fascinating technology of sensor fusion and its role in modern vehicles. Sensor fusion is a method that combines data from various sensors to create a comprehensive understanding of the vehicle's surroundings. We will discuss how this technology integrates information from cameras, radar, and other sensors, each contributing unique strengths to enhance vehicle performance.
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24
AI Control Algorithms for Autonomous RobotsIn this lesson, students will dive into the world of AI control algorithms specifically designed for autonomous robots. They will explore key concepts such as decision-making, sensor integration, and adaptive learning, equipping them with the skills to design and implement efficient algorithms that enhance robot autonomy and performance. By the end of the lesson, students will be proficient in leveraging these algorithms to tackle real-world challenges in robotics.
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25
Robotics Revolution: Designing and Simulating an AI-Navigated Robot
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26
Final Assessment: Elevate Your Learning Experience
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27
Quiz: Robotics and AI Integration
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