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Hands-On Machine Learning: Python Project Showcase
Dive into practical Machine Learning with Python, featuring real-world projects and case studies for hands-on mastery
This Course Includes
udemy
4.9 (4 reviews )
4.5 total hours 
english
Online - Self Paced
course
Udemy
About Hands-On Machine Learning: Python Project Showcase
Welcome to an immersive journey into the world of machine learning through practical projects and case studies. This course is designed to bridge the gap between theoretical knowledge and real-world applications, providing participants with hands-on experience in solving machine learning challenges using Python.
In this course, you will not only learn the fundamental concepts of machine learning but also apply them to diverse case studies, covering topics such as linear regression, clustering, time series analysis, and classification techniques. The hands-on nature of the course ensures that you gain practical skills in setting up environments, implementing algorithms, and interpreting results.
Whether you're a beginner looking to grasp the basics or an experienced practitioner aiming to enhance your practical skills, this course offers a comprehensive learning experience. Get ready to explore, code, and gain valuable insights into the application of machine learning through engaging projects and case studies. Let's embark on this journey together and unlock the potential of machine learning with Python.
Lecture 1: Introduction to Machine Learning Case Studies
This section initiates the course with an insightful overview of machine learning case studies. Lecture 1 provides a glimpse into the diverse applications of machine learning, setting the stage for the hands-on projects and case studies covered in subsequent lectures.
Lecture 2: Environmental SetUp
Get ready to dive into practical implementations. Lecture 2 guides participants through the environmental setup, ensuring a seamless experience for executing machine learning projects. This lecture covers essential tools, libraries, and configurations needed for the hands-on sessions.
Lecture 3-8: Linear Regression Techniques
Delve into linear regression methodologies with a focus on problem statements and hands-on implementations. Lectures 3-8 cover normal linear regression, polynomial regression, backward elimination, robust regression, and logistic regression. Understand the nuances of each technique and its application through practical examples.
Lecture 10-15: k-Means Clustering and Face Detection
Explore the intriguing world of clustering with k-Means. Lectures 10-15 guide you through creating scattered plots, calculating Euclidean distances, printing centroid values, and applying k-Means to analyze face detection challenges.
Lecture 16-19: Time Series Analysis
Uncover the secrets of time series modeling. Lectures 16-19 walk you through the process of creating time series models, training and testing data, and analyzing outputs using real-world examples like Bitcoin data.
Lecture 20-29: Classification Techniques
Embark on a journey through classification techniques. Lectures 20-29 cover fruit type distribution, logistic regression, decision tree, k-Nearest Neighbors, linear discriminant analysis, Gaussian Naive Bayes, and plotting decision boundaries. Gain a comprehensive understanding of classifying data using different algorithms.
Lecture 30-41: Default Prediction Case Study
Apply your skills to a real-world scenario of predicting defaults. Lectures 30-41 guide you through defining the problem statement, data preparation, feature engineering, variable exploration, and visualization using confusion matrices and AUC curves.
This course provides a holistic approach to machine learning, combining theoretical concepts with practical case studies, enabling participants to master the implementation of various algorithms in Python.
What You Will Learn?
- Understanding Machine Learning Case Studies: Learn the practical application of machine learning through real-world case studies..
- Environment Setup for Machine Learning: Get hands-on experience in setting up the necessary environment for implementing machine learning algorithms.
- Linear Regression Techniques: Understand and implement linear regression models, starting with the problem statement and progressing to regressions..
- Robust Regression and Logistic Regression: Explore robust regression techniques and delve into logistic regression for binary classification problems..
- k-Means Clustering: Gain insights into unsupervised learning with k-Means clustering, including creating scattered plots and calculating Euclidean distances..
- Time Series Modeling: Learn to model and analyze time series data, exploring applications like Bitcoin price prediction..
- Classification Algorithms: Master various classification techniques, including logistic regression, decision trees, k-nearest neighbors, linear discriminant ana.
- Building Predictive Models: Understand the process of defining problem statements, preparing and cleaning data, and creating predictive models..
- Feature Engineering: Gain proficiency in feature engineering techniques, transforming variables, and preparing data for machine learning models..
- Visualization Techniques: Learn to visualize data using confusion matrices, AUC curves, SNS plots, and other visualization methods..
- Application in Finance: Apply machine learning to financial scenarios, exploring payment delays, standing credit, defaulting, and other relevant financials.
- Throughout the course, participants will acquire practical skills and knowledge to tackle real-world machine learning challenges..