Master the Fundamentals of Machine Learning with Python
Table of Contents
- Introduction
- The Instructors
- Applications of Machine Learning
- Self-Driving Cars
- Cloud Service Providers
- Stock Trading
- Cancer Detection
- Course Modules
- Introduction and Regression
- Classification
- Clustering
- Final Project
- Hands-on Labs with Jupyter Lab
- Machine Learning Algorithms Covered
- Linear Regression
- Regression Trees
- Logistic Regression
- K-Nearest Neighbors
- Support Vector Machines
- Multiclass Prediction
- Decision Trees
- K-Means Clustering
- Final Project - Predicting Rain in Australia
- Key Learning Outcomes
- Conclusion
🤖 Fundamentals of Machine Learning with Python
Machine learning has become an integral part of various fields and industries, revolutionizing the way we deal with data and make predictions. In the "Fundamentals of Machine Learning with Python" course, instructed by Saeed Aghabozorgi, Joseph Santarcangelo, and Azim Hirjani, you will dive into the world of machine learning and learn how to apply different machine learning algorithms using Python.
1. Introduction
The course begins with an introduction to machine learning, providing an overview of the key concepts and techniques. You will gain a solid understanding of Supervised and unsupervised learning, classification, regression, and clustering.
2. The Instructors
Let's meet the instructors who will guide you through this exciting learning journey. Saeed Aghabozorgi, with his extensive experience as a senior AI/ML Customer Engineer at Google, brings real-world expertise in developing enterprise-level solutions. Joseph Santarcangelo, with his background in electrical engineering and research in machine learning, offers insights into the impact of videos on human cognition. Azim Hirjani, a talented Data Scientist Intern at IBM, contributes his content creation skills and knowledge in the field of data science.
3. Applications of Machine Learning
Discover the wide range of applications where machine learning is making a significant impact. Self-driving cars rely on machine learning algorithms to classify objects and navigate safely. Cloud service providers, like IBM and Amazon, utilize machine learning for protecting their services against attacks. In the stock trading industry, machine learning helps analyze trends and Patterns for informed decision-making. Furthermore, machine learning aids in the early detection of cancer through intelligent analysis of medical images.
4. Course Modules
The course is organized into four modules, each focusing on a specific area of machine learning.
4.1 Introduction and Regression
In this module, you will dive into the foundations of machine learning and understand the principles of regression analysis. By working with an automobile dataset, you'll learn how to estimate the CO2 emission of cars and make predictions for future models.
4.2 Classification
Classification is a fundamental task in machine learning. This module will introduce you to logistic regression and demonstrate its application in predicting customer loyalty using telecommunication data.
4.3 Clustering
Uncover the world of unsupervised learning through clustering techniques. You will explore the K-nearest neighbors algorithm to classify customers based on telecommunication data. Additionally, you'll learn about support vector machines for classifying human cell samples.
4.4 Final Project
Put your newfound knowledge to the test with the final project. Using a variety of classification algorithms, you will predict whether it will rain in Australia or not. This project will showcase your ability to apply different machine learning techniques in a practical Scenario.
5. Hands-on Labs with Jupyter Lab
Throughout the course, you will have the opportunity to apply what you've learned in hands-on labs. These labs utilize Jupyter Lab, a popular interactive environment for data science, and Python libraries such as Pandas, Numpy, and Scikit-Learn. By working with real-world datasets, you will gain invaluable practical experience.
6. Machine Learning Algorithms Covered
This course covers a wide range of machine learning algorithms, each with its unique strengths and applications.
6.1 Linear Regression
Learn how linear regression models can be used to estimate the relationship between variables. You will work with an automobile dataset to predict CO2 emissions.
6.2 Regression Trees
Explore the power of regression trees in predicting housing prices using real estate data. This algorithm provides a non-linear approach to regression problems.
6.3 Logistic Regression
Understand the fundamentals of logistic regression and how it can be used to analyze customer data. The insights gained can help make informed business decisions.
6.4 K-Nearest Neighbors
Discover the K-nearest neighbors (KNN) algorithm, which is widely used for classification tasks. By working with telecommunication customer data, you will learn how to classify customers based on their attributes.
6.5 Support Vector Machines
Support vector machines offer a powerful approach to classification. In this module, you will classify human cell samples as benign or malignant using SVM.
6.6 Multiclass Prediction
Using the popular iris dataset, you will explore multiclass prediction techniques. By applying machine learning algorithms, you will classify different types of flowers accurately.
6.7 Decision Trees
Decision trees are versatile and interpretable models. You will build a decision tree model to determine the most suitable drugs to prescribe to patients based on their medical attributes.
6.8 K-Means Clustering
Discover the world of unsupervised learning with K-means clustering. By segmenting a customer dataset, you will identify groups of individuals with similar characteristics.
7. Final Project - Predicting Rain in Australia
In the final project, you will bring together all the knowledge gained throughout the course to tackle a real-world problem: predicting rain in Australia. By applying a combination of machine learning algorithms, you will analyze meteorological data and make accurate predictions.
8. Key Learning Outcomes
Upon completion of this course, you will achieve the following learning outcomes:
- Explain, compare, and contrast various machine learning topics and concepts.
- Understand the working principles of different machine learning algorithms.
- Apply machine learning techniques using Python and popular libraries.
- Gain practical experience through hands-on labs and a final project.
9. Conclusion
Congratulations on embarking on this journey into the fundamentals of machine learning with Python. With the guidance of experienced instructors, hands-on labs, and real-world examples, you are well-equipped to embark on your own machine learning projects and make informed data-driven decisions.
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