Hands-On Machine Learning: Python Project Showcase

Hands-On Machine Learning: Python Project Showcase, available at $19.99, has an average rating of 4.5, with 41 lectures, based on 5 reviews, and has 4361 subscribers.

You will learn about 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. This course is ideal for individuals who are 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. or 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. or Finance Professionals: Those in the finance sector interested in leveraging machine learning for data analysis, risk assessment, and predictive modeling. or Business Analysts: Professionals seeking to enhance their analytical capabilities through machine learning techniques for better decision-making and insights. or Students and Researchers: Individuals pursuing studies or research in data science, machine learning, or related fields looking to strengthen their practical skills. or 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. It is particularly useful for 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. or 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. or Finance Professionals: Those in the finance sector interested in leveraging machine learning for data analysis, risk assessment, and predictive modeling. or Business Analysts: Professionals seeking to enhance their analytical capabilities through machine learning techniques for better decision-making and insights. or Students and Researchers: Individuals pursuing studies or research in data science, machine learning, or related fields looking to strengthen their practical skills. or 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.

Enroll now: Hands-On Machine Learning: Python Project Showcase

Summary

Title: Hands-On Machine Learning: Python Project Showcase

Price: $19.99

Average Rating: 4.5

Number of Lectures: 41

Number of Published Lectures: 41

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Original Price: $89.99

Quality Status: approved

Status: Live

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.

Who Should Attend

• 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.

Target Audiences

• 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.