Kaggle Machine Learning Competition Boot Camp Series​​​​​​​
​Kaggle is one of the most popular and novice friendly platforms for machine learning competitions, with a wide range of challenges covering topics such as computer vision, natural language processing, and time series analysis. ​
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Step by Step Method to Master AI
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Step 4: Neural Network and Deep Learning Course
Advanced Python Course with Further Math for Machine Learning/AI
Entry Requirement: Must meet Python GCSE Coding Standard or be able to write reasonably complex Python code, such as using nested loops.
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Prerequisite Course: Further Mathematics Course for Machine Learning/AI
Date: TBD - 5 Days
Time: 10:00 - 12:00 (UK Time)
School Year Group: Year 8 - Year 13
​Course Mode: Online
Tutor-Child Ratio: 1:5; Max 5 Children Per Teacher.
Fee: £250
* Taught by UK Russell Group University Professor
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* Course Content designed by UK Russell Group University Professor, Open Source AI Library Author and senior software developers with rich AI Industry experience .
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* Learn AL/ML concepts and process with Python
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* Apply Further math knowledge from our Further Mathematics Course for Machine Learning/AI to learn Data Science with Python.
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* Apply Further Math concepts and Python to solve real problems using Machine Learning
Course Demo Video
Our approach to Learn Further Math with Python
Apply Further Math and Machine Learning in Python to solve real problems
Our Coding Platform
Coding Platform with AI Tutor
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Day 1 - Linear Algebra:
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NumPy
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Matplotlib
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Solving Systems of Equations with Python
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Coding Exercises
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Day 2 - Linear Algebra:​
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Linear Regression (OLS) with Python​
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Coding Exercises
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Day 3 - Calculus :
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Linear Regression (Gradient Descent) with Python
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Coding Exercises
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Day 4 - Kaggle Project and Competition Foundation (Part 1):​​
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Multiple Linear Regression with Python
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Machine Learning Model
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Kaggle Datasets
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Day 5 - Kaggle Project and Competition Foundation (Part 2):
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Introduce Kaggle Competition
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Machine Learning Project in Kaggle with Python
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Course Structure
Course Objectives
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Essential Mathematical Principles:
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Introduce core mathematical concepts and their applications in AI, covering vector and matrix operations, solving systems of equations, linear regression, and optimization techniques using Python libraries such as NumPy and Matplotlib.
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Enhance Logical Thinking:
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Foster problem-solving skills and logical reasoning by exploring mathematical operations and their significance in AI, with focused tasks on vector and matrix operations, solving systems of equations, and linear regression using both OLS and Gradient Descent in Python.
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Prepare for Advanced Studies:
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Lay a strong foundation for future learning in advanced mathematical topics and AI concepts by tackling challenging exercises in data exploration, feature engineering, and multiple linear regression using Python libraries like Pandas and scikit-learn, ensuring a smooth transition to more complex studies.
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Encourage Creativity:
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Encourage students to express their creativity and innovation through personalized projects that apply mathematical concepts in AI, such as implementing algorithms for linear regression, exploring the Kaggle platform, and performing data analysis and model evaluation using Python.
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Practice and Review:
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Provide opportunities for practice with targeted exercises and review sessions on Kaggle projects and competition foundations to reinforce learning, analyze key concepts, and solve problems using Python, ensuring a deep understanding of the mathematical principles essential for AI.
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Instructor-Led Sessions:
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The instructor and assistant teacher introduce the basic concepts of each topic, providing full and detailed explanations on vector and matrix operations, solving systems of equations, linear regression, optimization techniques, and their significance in AI using Python libraries such as NumPy and Matplotlib.
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Interactive Exercises:
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Students engage in targeted exercises to apply and practice what they have learned, including and building machine learning models using scikit-learn.
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Daily Homework Assignments:
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Homework is assigned each day to help students consolidate their understanding of the concepts covered in class.
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Kaggle Project and Competition:
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Students will learn and implement multiple linear regression, develop a deeper understanding of machine learning models, and explore practical applications using a Kaggle dataset. They will also be introduced to the Kaggle competition platform and complete a machine learning project, applying the concepts learned throughout the course.
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