Classification Models: Supervised Machine Learning in Python
What you'll learn
- Describe the input and output of a classification model
- Prepare data with feature engineering techniques
- Tackle both binary and multiclass classification problems
- Implement Support Vector Machines, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, Neural Networks, logistic regression models on Python
- Use a variety of performance metrics such as confusion matrix, accuracy, precision, recall, ROC curve and AUC score.
- Basic knowledge of Python Programming
Artificial intelligence and machine learning are touching our everyday lives in more-and-more ways. There’s an endless supply of industries and applications that machine learning can make more efficient and intelligent. Supervised machine learning is the underlying method behind a large part of this. Supervised learning involves using some algorithm to analyze and learn from past observations, enabling you to predict future events. This course introduces you to one of the prominent modelling families of supervised Machine Learning called Classification. This course will teach you to implement supervised classification machine learning models in Python using the Scikit learn (sklearn) library. You will become familiar with the most successful and widely used classification techniques, such as:
Support Vector Machines.
You will learn to train predictive models to classify categorical outcomes and use performance metrics to evaluate different models. The complete course is built on several examples where you will learn to code with real datasets. By the end of this course, you will be able to build machine learning models to make predictions using your data. The complete Python programs and datasets included in the class are also available for download. This course is designed most straightforwardly to utilize your time wisely. Get ready to do more learning than your machine!
Employment website Indeed has listed machine learning engineers as #1 among The Best Jobs in the U.S., citing a 344% growth rate and a median salary of $146,085 per year. Overall, computer and information technology jobs are booming, with employment projected to grow 11% from 2019 to 2029.
Who this course is for:
- Research scholars and college students
- Industry professionals and aspiring data scientists
- Beginners starting out to the field of Machine Learning
Meet our inspiring instructor on Udemy, an engineer dedicated to leveraging the power of machine learning and deep learning to solve real-world problems and improve design and performance assessment. With over ten years of experience in engineering and R&D environments, our instructor has developed a wealth of knowledge and expertise in the field of AI-ML.
Graduating from IIT Madras with a focus on AI-ML, our instructor has spent their career implementing action-oriented solutions to complex problems, showcasing their ability to turn theoretical concepts into practical applications that deliver real results. Their passion for innovation has driven them to seek out new challenges and stay at the forefront of the ever-evolving AI and ML industry.
Our instructor's extensive experience in engineering and R&D environments has honed their ability to identify critical problems and develop effective solutions. Their focus on AI and ML has allowed them to leverage the power of these technologies to improve design and performance assessment, solving real-world problems with ease.
Their dedication to sharing their knowledge and experience has led them to become instructors on Udemy, where they can share their expertise with a global audience. Their courses are designed to provide actionable insights and practical techniques that empower students to leverage the power of AI and ML to solve complex problems and drive innovation.
Join our instructor on Udemy and take the first step towards unlocking the power of AI and ML to drive innovation and achieve real-world results.