Machine Learning with Python Course Overview

Course Prerequisites

Target Audience for Machine Learning with Python

Koenig Solutions' Machine Learning with Python course offers a comprehensive deep dive into the fundamentals and applications of machine learning. Ideal for aspiring data professionals.

• Data Scientists
• Machine Learning Engineers
• Data Analysts
• Software Developers interested in AI and machine learning
• Research Scientists and Academics in the field of computer science
• Statisticians looking to apply their skills in the tech industry
• IT Professionals seeking to expand their skill set into machine learning
• Graduates with a background in computer science, mathematics, or related fields
• Business Analysts who want to understand data-driven decision-making
• Product Managers aiming to leverage machine learning in product development
• Entrepreneurs looking to implement machine learning in new ventures
• Technical Project Managers overseeing machine learning projects
• Data Engineers who need to understand the algorithms behind data pipelines
• AI Enthusiasts and Hobbyists who are passionate about learning machine learning techniques
• Professionals in sectors like finance, healthcare, and e-commerce where machine learning is increasingly used

Learning Objectives - What you will Learn in this Machine Learning with Python?

Introduction to the Course’s Mentioned Learning Outcomes and Concepts Covered

This Machine Learning with Python course equips students with a comprehensive understanding of machine learning concepts, techniques, and algorithms using Python, along with practical applications and hands-on experience.

Learning Objectives and Outcomes

• Understand the fundamentals of machine learning, including its applications, challenges, and the various types of machine learning approaches.
• Gain proficiency in using Scikit-Learn for machine learning tasks, understanding its features and conventions, and implementing machine learning workflows.
• Acquire knowledge of linear algebra concepts crucial to machine learning, such as vectors, dot products, and hyperplanes.
• Learn probability theory and its applications in machine learning, including probability distributions and sampling methods.
• Develop a solid foundation in statistics, including descriptive and inferential statistics, measures of central tendency, dispersion, skewness, and kurtosis.
• Master data pre-processing techniques to clean and prepare data for analysis, including the use of pipelines and grid search for model optimization.
• Conduct exploratory data analysis (EDA) to understand data characteristics through visualizations like scatter plots, histograms, and box plots.
• Learn and apply feature engineering techniques to improve model performance by creating and selecting relevant features.
• Evaluate machine learning models using performance metrics such as confusion matrices and ROC curves, and refine models through cross-validation and parameter tuning.
• Obtain hands-on experience with supervised learning algorithms, including linear regression and classification techniques like logistic regression, and understand unsupervised learning methods such as clustering and association rule mining.