Welcome to the course review of Machine Learning with SciKit-Learn with Python. In this course, you will gain a practical understanding of the Scikit-Learn library, and learn how to implement Machine Learning (ML) concepts. Scikit-learn is a Python-based library that consists of various tools for statistical modeling and machine learning. Through regression, clustering, and classification functionalities, the library serves as a pre-defined set of functions designed for ML implementations. Lehman data analysts and programmers have reviewed this course and have collectively given a rating of 3.91868 out of 5.0 from 117 reviews.
What is Machine Learning?
Machine Learning is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience. It focuses on the development of advanced algorithms and statistical models that enable a system to learn and improve over time. Machine Learning algorithms can detect patterns in 'training' data that can be applied to new, unseen 'testing' data. These algorithms take historical data as input and create models that are capable of recognizing patterns, trends, and relationships in the data.
What is SciKit-Learn?
Scikit-learn is a free and open-source library for Python that provides tools for developers and data scientists to implement Machine Learning projects. It is built on top of NumPy, SciPy, and Matplotlib and offers simple and efficient tools for data mining and data analysis. Scikit-learn, with its linear models, clustering algorithms, and dimensionality reduction techniques, has been adopted as the industry standard in the field of machine learning. It offers many benefits such as ease of use, high performance, and powerful features.
What can you Expect to Learn from this Course?
The aim of this course is to develop the trainee's expertise working with the Python-based SciKit-learn library. It allows you to implement the concepts of machine learning in real-world applications and provides a complete practical understanding of the Scikit-learn library. You will gain an introduction to all the basic terms, concepts, and functionalities of the Scikit-learn library. You will understand how this library helps applications by adding machine learning concepts and tools. By the end of this course, you will learn about advanced level concepts that will enable you to implement machine learning with Scikit-learn.
Why you Should take this Course?
If you're looking to become a proficient data scientist or data analyst, this course is perfect for you. The use of the Scikit-learn library is standard in most industries, and this course focuses on building practical skills that can be applied in your profession. The content of this course is informative, engaging, and convenient for users of all levels of expertise.
Course Syllabus
Once you enroll in the course, you will encounter modules such as:
- Module 1 - Python and Scikit-learn basics
- Module 2 - Supervised Learning with Scikit-learn
- Module 3 - Unsupervised Learning with Scikit-learn
- Module 4 - Advanced Scikit-learn for developing Machine Learning Models
What to Expect in Module 1 - Python and Scikit-learn basics?
In Module 1, you will get introduced to Python and Scikit-learn basics. This will include terminologies, libraries, installations, and a brief setup of the environment required to execute Scikit-learn. You will learn about the tools that Scikit-learn offers for various ML concepts, such as plotting data, data manipulation, and model assessment.
What to Expect in Modules 2 and 3 - Supervised and Unsupervised Learning with Scikit-learn?
Module 2 and 3 cover Supervised and Unsupervised Learning. You will understand commonly used algorithms in both supervised and unsupervised learning. You will learn regression, classification, clustering, and dimensionality reduction along with evaluating methods. You will also learn about, Feature Scaling, Neural Networks, K-Mean Clustering, Hierarchical Agglomerative Clustering, and many more.
What to Expect in Module 4 - Advanced Scikit-learn for developing Machine Learning Models?
In Module 4, you will learn to develop advanced Machine Learning Models. You will learn about Face Recognition in Python and scikit-image, Deep Learning models in Scikit-learn, and Natural Language Processing (NLP) with Scikit-learn and gensim libraries. You will also learn about Scikit-learn's pipeline feature and discover how to develop and analyze Parallel computing in Scikit-learn. Module 4 will be essential for those Python libraries and applying models to the real-world.
Final Thoughts
Machine Learning with Scikit-Learn with Python covers every concept of Scikit-learn and ensures that developers and data scientists of all levels can understand and execute Machine Learning. The course content is well-structured, and the exercise assignments help you obtain an in-depth understanding of each module. We highly recommend this course to all aspiring data scientists and anyone looking to advance their career in AI, ML.
Get Enrolled!
By now, you have an idea of what you can expect from this course. If you are looking to learn Machine Learning with Scikit-learn, then this course is perfect for you! Enroll now and start learning the art of Machine Learning with Scikit-learn using Python.
