AI Bootcamp: Beginner to Expert in Machine Learning 2024
This course adopts a bootcamp-style learning approach, delivering essential information through hands-on labs and projects to enhance your understanding of the material. You can freely use the projects to enhance your resume or GitHub profile to boost your career.
In this module, you’ll explore the applications of Machine Learning across various fields, including healthcare, banking, and telecommunications. You’ll gain a broad understanding of Machine Learning concepts, such as supervised versus unsupervised learning, and how to implement Machine Learning models using Python libraries.
It is suitable for individuals who:
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Need to quickly start working with Machine Learning, such as students.
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Want to prepare themselves for work tasks or job interviews.
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Have an interest in beginning their journey in Machine Learning, Deep Learning, AI, or Large Language Models like ChatGPT.
Requirements:
Firstly, don’t be afraid to delve into unfamiliar topics just because of their titles; everything is achievable step by step.
The course has no specific prerequisites, but for the labs, it’s helpful to have some basic knowledge of the Python programming language. If you’re unfamiliar, the course provides guides to assist you.
Learning Objectives:
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Provide examples of Machine Learning applications in different industries.
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Outline the problem-solving steps used in Machine Learning.
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Present examples of various machine learning techniques.
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Describe Python libraries used in Machine Learning.
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Explain the distinctions between Supervised and Unsupervised algorithms.
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Describe the capabilities of different machine learning algorithms.
What is machine learning
Embrace the Learning Journey
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4
Introduction to Machine Learning -
5
Python for Machine Learning
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6
Introduction to Programming with Python -
7
Supervised vs Unsupervised
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8
Machine Learning Fundamentals QuizTest your understanding of essential machine learning concepts with this interactive quiz! Explore key topics such as supervised and unsupervised learning, regression techniques, and model evaluation methods.
Each question offers valuable insights into fundamental concepts, helping you solidify your knowledge in machine learning.
Aim of Quiz: The aim of this quiz is to assess your grasp of foundational machine learning principles. By answering a series of thought-provoking questions, you'll enhance your understanding of supervised and unsupervised learning, regression algorithms, and the comparison between different learning approaches.
Whether you're a beginner eager to learn or an enthusiast seeking to reinforce your knowledge, this quiz is the perfect opportunity to challenge yourself and deepen your understanding of machine learning fundamentals.
Linear Regression
K-Nearest Neighbours
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10
Introduction to Regression
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11
Simple Linear Regression
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12
Model Evaluation in Regression Models
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13
Evaluation Metrics in Regression Models
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14
Simple Linear Regression
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15
Multiple Linear Regression
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16
Multiple Linear Regression
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17
Evaluation and Application of Regression Models
This quiz aims to test your understanding of regression models, focusing on concepts such as out-of-sample accuracy, the application of multiple linear regression, and the characteristics of linear regression models. By answering these questions, you can assess your knowledge of regression analysis and evaluate your proficiency in applying regression techniques to real-world problems.
Aim of the Quiz: The aim of this quiz is to assess your comprehension of regression modeling concepts and your ability to apply regression techniques effectively.
By completing this quiz, you will reinforce your understanding of out-of-sample accuracy, identify scenarios suitable for multiple linear regression, and differentiate between characteristics of simple and multiple linear regression models.
Decision Trees
Regression Trees
Logistic Regression
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25
Regression Trees
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26
Regression Trees
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27
Understanding Machine Learning Algorithms
This quiz is designed to test your understanding of various machine learning algorithms and their key concepts. It covers topics such as the k-Nearest Neighbors (kNN) algorithm, decision trees, and model complexity.
By answering these questions, you can assess your knowledge of different algorithms and their implications in machine learning tasks.
Aim of the Quiz: The aim of this quiz is to evaluate your comprehension of machine learning algorithms and their characteristics.
By completing this quiz, you will reinforce your understanding of how different algorithms work and their impact on model performance. Additionally, you will gain insights into common misconceptions and nuances associated with these algorithms.
Support Vector Machine
Multiclass Prediction
Clustering
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34
Multiclass Prediction
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35
Understanding Logistic Regression and Gradient Descent
This quiz aims to test your knowledge of logistic regression, a fundamental algorithm in machine learning used for binary classification tasks, and gradient descent optimization, a key technique for minimizing the cost function in logistic regression.
Through a series of questions, you will assess your understanding of logistic regression applications, differences between linear and logistic regressions, and the role of gradient descent and learning rate in logistic regression optimization.
Aim of the Quiz: The aim of this quiz is to evaluate your comprehension of logistic regression concepts and gradient descent optimization techniques.
By completing this quiz, you will reinforce your understanding of logistic regression applications, its comparison with linear regression, and the practical aspects of gradient descent and learning rate in logistic regression optimization.
