Scikit-learn, also known as sklearn, is a widely-used open-source machine learning library that is written in Python. It provides a range of tools and algorithms for data analysis, data visualization, and data modeling. Sklearn can be used for various tasks such as classification, regression, clustering, and dimensionality reduction. As the demand for machine learning skills continues to grow, many individuals are seeking to enhance their knowledge of Scikit-learn through online courses. This article presents an overview of some of the best Scikit-learn courses available online, highlighting their key features and benefits.
Here’s a look at the Best Scikit Learn Courses and Certifications Online and what they have to offer for you!
10 Best Scikit Learn Courses and Certifications Online
- 10 Best Scikit Learn Courses and Certifications Online
- 1. Machine Learning with SciKit-Learn with Python by Exam Turf (Udemy) (Our Best Pick)
- 2. Introduction to ML Classification Models using scikit-learn by Loony Corn (Udemy)
- 3. Machine learning with Scikit-learn by Francisco Juretig (Udemy)
- 4. SciKit-Learn in Python for Machine Learning Engineers by Mike West (Udemy)
- 5. Advanced Predictive Techniques with Scikit-Learn& TensorFlow by Packt Publishing (Udemy)
- 6. An Introduction to Scikit-Learn by Dhruva Krishna (Udemy)
- 7. Machine Learning with Scikit-Learn in 7 Hours by Packt Publishing (Udemy)
- 8. scikit-learn Recipes by Packt Publishing (Udemy)
- 9. Step by step guide in mastering Scikit-Learn (2021) by Chandramouli Jayendran (Udemy)
- 10. Achieve your first Machine Learning project in Python in 2h by Damien Chambon (Udemy)
1. Machine Learning with SciKit-Learn with Python by Exam Turf (Udemy) (Our Best Pick)
The Machine Learning with SciKit-Learn with Python course offered by Exam Turf provides a practical understanding of the Scikit-Learn library and teaches the implementation of machine learning concepts. The course aims to enable trainees to develop applications using the Scikit-Learn library for ML implementation.
The course begins with an introduction that covers all the basic details of the concept and important topics. It then progresses to more advanced level concepts. Upon completing the course, trainees will be able to implement the concepts of machine learning using SciKit-Learn.
Scikit-Learn is a Python-based library that offers a predefined set of functions to add machine learning-based features in an application. It consists of various tools for statistical modeling and machine learning, including regression, clustering, and classification. Scikit-Learn is built on top of NumPy, SciPy, and Matplotlib and is only supported while implementing things using the Python programming language.
The course content is divided into sections that cover Introduction, NumPy, NumPy Array, Indexing Arrays of Arrays, Matplotlib, Pandas, Scikit Learn, Learning and Predicting, Cross Validation, and Movie Review Analysis.
The Introduction to ML Classification Models course is designed to provide a fundamental understanding of Machine Learning, with a focus on building classification models. It is suitable for developers or data scientists who have basic knowledge of Python programming and wish to learn about classification problem-solving. The course covers key concepts such as Supervised and Unsupervised Learning, Regression, Classification, and Overfitting.
There are three lab sections in the course which focus on building classification models using Support Vector Machines, Decision Trees, and Random Forests, using real data sets. The implementation will be performed using the scikit-learn library for Python.
The course is structured into six sections: Introduction, What is ML?, Support Vector Machines (SVMs), Decision Trees, Overfitting – the Bane of Machine Learning, and Ensemble Learning and Random Forests. The lab sections are integrated into each of these sections, making hands-on implementation an integral part of the learning experience.
Overall, the Introduction to ML Classification Models course provides a comprehensive overview of Machine Learning and equips learners with the skills needed to build classification models using Python’s scikit-learn library.
The “Machine Learning with Scikit-learn” course teaches advanced machine learning techniques using the Scikit-learn library. Prior familiarity with statistics and Python programming is recommended, but not required. The course is focused on the Python implementation of Scikit-learn, and less emphasis is placed on the underlying math.
The objective is for students to develop a good understanding of Scikit-learn and be able to identify which technique to use for a particular problem. The course covers the differences between AI, machine learning, statistics, and data mining. It also covers the installation of Scikit-learn and its dependencies, how to use Pandas data in Scikit-learn, and creating synthetic data sets tailored for regression, classification, and clustering.
Supervised learning, which involves predicting an objective variable using features, is covered in depth. The course covers classification and regression problems, Naive Bayes, regression techniques, Support Vector Machines, classification and regression trees, and “ensemble” methods such as random forests and boosting methods. The course also covers unsupervised learning, which involves learning from data with no target variable, including k-means and DBSCAN algorithms, principal components for reducing the dimensionality of data sets, and outlier detection.
Real data sets from Kaggle, such as spam SMS data and house prices in the United States, are used to teach students what to expect when working with real data. The course is updated regularly to keep up with Scikit-learn’s latest features and functions. While the examples presented are kept simple, the methods scale well into real-world applications. The course is divided into three sections: Introduction to Scikit-learn, Supervised methods, and Unsupervised methods.
The SciKit-Learn in Python for Machine Learning Engineers course is the fourth in a series. It is recommended to take the courses in order to understand what machine learning engineers do in the real world. The course is designed to teach SciKit-Learn using a lab integrated approach, as programming is something that must be done to master it. The course covers the basic foundations of SciKit-Learn, model building basics, and building traditional machine learning models in the real world.
The course is focused on building traditional machine learning models in SciKit-Learn. It includes lessons on SciKit-Learn basics from A-Z, lab integration, real world interview questions, building basic models in SciKit-Learn, and learning the vernacular of building machine learning models. Python is the gold standard for building machine learning models in the applied space, and SciKit-Learn has become the gold standard for building traditional models in Python.
Python is a high-level language that is user friendly and has many applications outside of machine learning. The course starts with the basics of SciKit-Learn, including basic terminology and scoring models. Labs are included in each lesson to build on what has been learned.
There are five reasons to take this course: to become a machine learning engineer, to become a Google certified data engineer, due to the insane growth of data, to learn machine learning in plain English, and to be ahead of the curve in the burgeoning field of machine learning engineering. The course covers introduction, linear regression, feature extraction and preprocessing, natural language processing, nonlinear classification, and K-means clustering.
Packt Publishing offers a course titled “Advanced Predictive Techniques with Scikit-Learn & TensorFlow” that aims to help learners improve the performance of their predictive models, build more complex models, and use techniques to enhance the quality of their predictions. The course covers ensemble methods, which combine predictions from individual predictors to improve prediction accuracy in regression and classification problems.
In addition to models and algorithms, practical considerations such as feature selection, feature engineering, and hyper-parameter tuning are crucial when solving real-world problems with predictive analytics. The course covers these topics to help learners answer related questions.
The course introduces learners to the use of deep learning models for predictive analytics using the TensorFlow library. The instructor, Alvaro Fuentes, has an M.S. in Quantitative Economics and Applied Mathematics and over ten years of experience in analytical roles. He founded Quant Company to provide consulting and training services in data science and has taught courses on data science, mathematics, statistics, and programming.
Fuentes is proficient in Python and R programming, Spark, SQL (PostgreSQL), MS Excel, machine learning, statistical analysis, econometrics, and mathematical modeling. He has both professional and teaching experience in predictive analytics, having solved practical problems in his consulting practice using Python tools and covered topics on predictive analytics in a more general course on data science.
Course sections include ensemble methods for regression and classification, cross-validation and parameter tuning, working with features, introduction to artificial neural networks and TensorFlow, and predictive analytics with TensorFlow and deep neural networks.
Course Title: An Introduction to Scikit-Learn
Course Instructors: Dhruva Krishna
This course provides an overview of Scikit-Learn, a widely used modelling package in Python. The course covers key concepts in the model building workflow, including data preprocessing, various models (such as Regressions, Support Vector Machines, Neural Networks, and Hierarchical Clustering methods), performance evaluation, and improvement techniques such as Cross Validation and Hyperparameter Tuning.
The course is divided into 8 sections: Introduction, Preprocessing, Regression, Classification, Regression & Classification, Clustering, Model Selection and Evaluation, and Final Thoughts.
In the Introduction section, students will learn the basics of Scikit-Learn and its importance in machine learning.
The Preprocessing section covers data cleaning and transformation techniques.
The Regression section discusses how to use Scikit-Learn for linear regression and other related models.
The Classification section provides an overview of how to use Scikit-Learn for classification models.
The Regression & Classification section discusses how to use Scikit-Learn for both regression and classification models.
The Clustering section covers the use of clustering models in Scikit-Learn.
The Model Selection and Evaluation section provides information on how to evaluate models for their performance and how to improve them through Cross Validation and Hyperparameter Tuning.
Finally, the Final Thoughts section provides a summary of the course and a glimpse into potential areas for further study.
The “Machine Learning with Scikit-Learn in 7 Hours” course by Packt Publishing teaches users how to perform machine learning tasks using Python’s scikit-learn library. The course is ideal for data scientists and IT professionals who want to learn machine learning concepts and apply them in real-world scenarios. The course includes three complete courses, covering important algorithms such as Linear Regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, and Feature Engineering. The course also covers supervised and unsupervised learning, reinforcement learning, and semi-supervised learning through practical projects.
The three courses included in the training program are: “Fundamentals of Machine Learning with scikit-learn”, “Learn Machine Learning in 3 Hours”, and “Real-World Machine Learning Projects with Scikit-Learn”. In the first course, users will learn important machine learning algorithms that can be used for supervised and unsupervised learning, reinforcement learning, and semi-supervised learning. In the second course, users will learn key ML algorithms, including how to train them for classification and regression, while working with supervised and unsupervised learning. In the third course, users will build powerful projects using scikit-learn, including decoding buying behavior using Classification algorithms, gaining insights into population clusters using K-Means Clustering, and predicting heart disease using Support Vector Machine classifiers.
The course is taught by three esteemed authors, Giuseppe Bonaccorso, Thomas Snell, and Nikola Živković. Bonaccorso is a machine learning and big data consultant with over 12 years of experience, while Snell is a data scientist with a PhD in Geophysics and a keen interest in machine learning. Živković is a software developer with over 7 years of experience and a Master’s degree in Computer Science.
Packt Publishing offers the scikit-learn Recipes course, which aims to provide practical recipes for powerful data analysis with scikit-learn. Scikit-learn is a package that top data scientists prefer for machine learning because of its powerful data analysis and quick, accurate computations. This course targets individuals who have some basic knowledge or are new to scikit-learn. The course starts with generating synthetic data for building a machine learning model, pre-processing data with scikit-learn, and building various supervised and unsupervised models. Participants will also learn to implement optimization techniques like cross-validation, feature selection, regularization, and dimensionality reduction techniques. By the end of the course, participants will be able to build their own machine learning models and enhance their data analysis skills.
The author of the course, Sahiba Chopra, is an experienced data scientist who has more than four years of experience working on machine learning projects across a diverse set of industries. She has worked on predictive analytics, anomaly detection, credit risk modelling, and recommendation engines. As a self-taught data scientist, she knows and understands what participants are looking for and the concepts that will help them in their data science projects.
The course content is divided into several sections, including Data Pre-Processing with scikit-learn, Dimensionality Reduction, Linear Models, Support Vector Machines, Decision Trees and Ensembles, Clustering with scikit-learn, Cross-Validation, and Neural Networks. These sections cover various topics such as generating synthetic data, building supervised and unsupervised models, implementing optimization techniques, and reducing dimensionality. The course aims to provide participants with the knowledge and skills to build powerful machine learning models using scikit-learn.
The course titled “Step by step guide in mastering Scikit-Learn (2021)” is designed to provide a comprehensive understanding of data science and machine learning using Scikit learn (SKLearn). The course is aimed at beginners and offers a step-by-step explanation of supervised learning.
The course covers the entire workflow of Scikit-Learn, starting from data analysis and gathering to creating your own model to solve real-life problems. The instructors also explain the use of other functions such as Pandas, Numpy, Matplotlib, and Seaborn.
The course provides extensive coverage of creating models for classification and regression, using six or more datasets, and choosing estimators based on available data. It also offers a detailed explanation of how to improve results by changing parameters and hyper-parameters in a model.
The course content is divided into eight sections, namely, Introduction, Workflow of SKLearn Explained, Get Data Ready for Modelling, Choosing Estimators, Fitting and predicting in Scikit-Learn, Evaluation in SKLearn, Improving the Model – SKLearn, and Saving the Model.
The course is ideal for beginners who want to learn the basics of Scikit-Learn and machine learning. It is a comprehensive course that covers the essential concepts of data science and machine learning using Scikit-Learn, making it easy for learners to understand and implement the concepts.
The Achieve your First Machine Learning Project in Python in 2h course, taught by Damien Chambon, provides comprehensive training on the different steps involved in Machine Learning (ML) projects using Python for Data Science. The course promises to equip learners with the skills required to complete a ML project from start to finish in just 2 hours, covering all the essential steps of a Data Science project and how to execute them using Python.
The course aims to address the challenge that many aspiring Data Scientists face when transitioning from theory to practical application of ML concepts. The focus is on empowering learners to incorporate ML into their professional projects to enhance results, instead of getting bogged down in theory and losing valuable time.
The course offers a clear plan with simple yet powerful instructions that can be applied to any Machine Learning project, from data collection and preparation to model selection and algorithm optimization. It covers technical concepts and Python libraries, and learners are required to follow the steps closely to ensure valuable results.
Upon completion of the course, learners will have gained the skills to solve problems using ML and Python, and will be able to confidently approach projects using the skills they have acquired. The course also covers feature engineering, data exploration, and automation techniques, and is suitable for those with basic knowledge of Python and ML models.
The course comprises Introduction to Machine Learning, Prepare the workspace, Exploring the data, Preparing the data for the Machine Learning algorithms, Training your Machine Learning models, Communicating the results, and Conclusion.