BIG-O AI: INTRODUCTION TO MACHINE LEARNING

Are you eager to learn Machine Learning but don’t know where to start? Do you feel overwhelmed by the vast amount of knowledge out there and are unsure of the most effective learning path?

The “Big O AI Introduction to Machine Learning” course will help you navigate through this complexity with a well-structured and carefully curated learning path. The course aims to build a strong foundation that will boost your confidence on your journey to mastering Machine Learning.

By the end of the course, you will have learned how to:

  • Prepare and visualize data: Understand how to clean and prepare datasets while leveraging tools and techniques to visualize data effectively.

  • Apply fundamental Machine Learning algorithms: Gain a solid understanding of how essential ML algorithms work and how to apply them.

  • Optimize models: Learn advanced techniques such as hyperparameter tuning, ensemble methods, and more to enhance model performance.

  • Understand Neural Networks: Grasp the principles of neural networks, providing you with a strong foundation to explore Deep Learning.

Ngôn ngữ minh họa: Ngôn ngữ được sử dụng rộng rãi nhất hiện nay Python.

Tuition Fee: Special offer for the first 5 early registrants. For details of tuition fees, please see the attached link below.

In addition, to get more detailed advice about the AI course, you can contact the following fanpage: Big-O Coding

You can view the opening time, class timetable and register via this link 

SUITABLE AUDIENCES (STUDENTS)

  • Prerequisites: Learners should have basic Python programming skills.

  • Optional Knowledge: Familiarity with fundamental calculus, algebra, and probability concepts, such as derivatives, vectors, and probability. Experience with popular ML libraries such as numpy, pandas, and sklearn is recommended.

  • If you are not a suitable student for this Big-O AI class, please call us at: 0937.401.483 for advice on taking the next open classes in the near future.

COURSE ILLUSTRATION EXERCISES

  • The exercises are divided into two types: Multiple-choice questions and programming assignments provided in Jupyter Notebooks.
  • A variety of exercises tailored to each lecture: from implementing algorithms from scratch for better understanding to applying them to real-world problems.
  • The final exam introduces a Machine Learning challenge, allowing learners to apply all their knowledge to solve real-world problems with practical datasets.

TIME AND LOCATION OF THIS COURSE

  • Duration: 2 months (8 weeks)
  • Fomat: Online via Zoom.
  • Number of students per class: 25 to 30 students maximum.
  • Each class has 1 main teacher and 5 teaching assistants.
  • Especially, there are weekly Office Hours for students to review the lesson if they can’t keep up with the lesson progress.

WHAT MAKES THE COURSES AT BIG-O CODING DIFFERENT

1. TEACHING PROGRAM:

  • The programs are taught by Algorithm experts with many years of experience (see also “Teaching Staff”).
  • Students have chances to meet and receive sharing from successful people who have gone before about their Algorithmic learning experiences and working experiences.
  • Each class in addition to the main lecturer has 5 teaching assistants: the teaching assistants are in charge of the class and the class’s own forum to ensure that all students’ questions will be answered quickly anytime, anywhere.

2. OBJECTIVES AFTER THE COURSE:

  • The entire system of solid Machine Learning foundation knowledge.
  •  Prepare and visualize data: Understand how to clean and prepare datasets while leveraging tools and techniques to visualize data effectively.
  • Apply fundamental Machine Learning algorithms: Gain a solid understanding of how essential ML algorithms work and how to apply them.
  • Optimize models: Learn advanced techniques such as hyperparameter tuning, ensemble methods, and more to enhance model performance.
  • Understand Neural Networks: Grasp the principles of neural networks, providing you with a strong foundation to explore Deep Learning.

AI COURSE SYLLABUS

A refresher on essential Python knowledge and an introduction to popular libraries like numpy and pandas. Learn how to explore and clean data effectively with those libraries.
Understand the difference between regression and classification problems. Learn how the KNN algorithm works and how to apply it to both regression and classification tasks.
Learn the concept of a Machine Learning model, methods for model evaluation such as k-fold cross-validation, and how to select the right model based on metrics.
Revisit probability concepts strongly tied to Machine Learning, such as distributions and joint probabilities. Learn the Naive Bayes algorithm and how to apply it to datasets with various distributions.
Dive into the "classic" Machine Learning problem of linear regression. Explore the Least Squares algorithm, which directly uses mathematical formulas to solve regression problems.
Understand how Gradient Descent optimizes models, from basic implementations to advanced versions such as Gradient Descent with Momentum or Adam.
Learn the Logistic Regression algorithm and its application to binary classification and multi-class classification problems.

Kỳ thi giữa kỳ của khóa học.

Explore Support Vector Machines, a powerful algorithm for classification and regression tasks. Understand concepts such as hyperplanes and kernels.
Learn how Decision Trees work, from splitting data based on attributes to their application in various Machine Learning problems.
Discover how to optimize models by tuning hyperparameters. Explore three main ensemble methods—bagging, boosting, and stacking—and their related algorithms
Understand unsupervised learning and clustering models. Learn about two popular clustering algorithms: K-means and Hierarchical Clustering.
Learn the Principal Component Analysis (PCA) method for dimensionality reduction and its applications in improving model performance and data visualization.
Master the process of building a basic ML pipeline. Learn techniques for data processing, cleaning, and extracting or creating useful features for Machine Learning models.
Understand the principles behind neural networks, their basic structure, and core concepts such as forward propagation and backward propagation.

Kỳ thi cuối kỳ.