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

Created by: EDUCBA Bridging the Gap
Learn real world skills online

What Will I 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.

Requirements

• No prior knowledge of machine learning required
• Basic knowledge of Python

Target audience

• Data Enthusiasts and Aspiring Data Scientists: Individuals looking to delve into practical applications of machine learning with a focus on case studies and hands-on projects.
• Python Programmers and Developers: Professionals proficient in Python who want to expand their skill set to include machine learning and gain practical experience in implementing algorithms.
• Finance Professionals: Those in the finance sector interested in leveraging machine learning for data analysis, risk assessment, and predictive modeling.
• Business Analysts: Professionals seeking to enhance their analytical capabilities through machine learning techniques for better decision-making and insights.
• Students and Researchers: Individuals pursuing studies or research in data science, machine learning, or related fields looking to strengthen their practical skills.
• Anyone Seeking Practical Machine Learning Experience: The course caters to a broad audience eager to gain hands-on experience in solving real-world problems using machine learning tools and methodologies.