As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for IT professionals and data-scientists. The scikit-learn library is one of the most popular platforms for everyday Machine Learning and data science because it is built upon Python, a fully featured programming language.

Para 2: how this LP will make you successful at the task

This comprehensive 3-in-1 course is your one-stop solution to everything that matters in mastering machine learning algorithms and their implementation. Develop pipelines and process data through manipulation, extraction, and data-cleansing techniques. Learn clean coding techniques which are applicable to any scalable Machine Learning projects.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

This course will help you discover the magical black box that is Machine Learning by teaching a practical approach to modeling using Python along with the scikit-Learn library.

The first course, Fundamentals of Machine Learning with scikit-learn, covers strong foundation for entering the world of Machine Learning and data science. In this course, you’ll learn all the important Machine Learning algorithms that are commonly used in the field of data science. Finally, you’ll learn algorithms: Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, and Feature engineering. Finally, you’ll learn how these algorithms work and their practical implementation to resolve your problems.

The second course, Hands-On Machine Learning with Python and scikit-Learn, covers implementation of the best Machine Learning practices with the help of powerful features of Python and scikit-learn. You’ll learn to develop complex pipelines and techniquesfor building custom transformer objects for feature extraction, manipulation, and other effective data cleansing techniques. Finally, you’ll know how to select a model, apply optimal hyper-parameters, and deploy it.

The third course, Learn Machine Learning in 3 Hours, covers hands-on examples with machine learning using Python. This video starts by focusing on key ML algorithms and how they can be trained for classification and regression. You’ll also work with supervised and unsupervised learning. You’ll also use the highly popular scikit-learn library throughout the course while performing various ML tasks.

By the end of this training program you’ll get hands-on with machine learning using powerful features of Python and scikit-learn to implement the best Machine Learning practices.

About the Authors

• Giuseppe Bonaccorso is a machine learning and big data consultant with more than 12 years of experience. He has an M.Eng. in electronics engineering from the University of Catania, Italy, and further postgraduate specialization from the University of Rome, Tor Vergata, Italy, and the University of Essex, UK. During his career, he has covered different IT roles in several business contexts, including public administration, military, utilities, healthcare, diagnostics, and advertising. He has developed and managed projects using many technologies, including Java, Python, Hadoop, Spark, Theano, and TensorFlow. His main interests on artificial intelligence, machine learning, data science, and philosophy of mind.
• Taylor Smith is a Machine Learning and software development enthusiast with over five years' data science experience. He loves to help businesses find value in Machine Learning by applying interesting computational solutions to challenging business problems. Currently working as a Principal Data Scientist, Taylor is also an active open-source contributor and staunch Pythonista.
• After taking a Physics degree at Oxford, Thomas Snell entered the Biophysics industry. Performing numerical simulation; from there, took a numerical simulation PhD in Geophysics. During his PhD, Thomas developed a keen interest in Machine Learning, eventually founding two open source projects: a cryptocurrency trader and an evolutionary system to design quantum algorithms. Shortly after sharing these projects with the open source community, he worked as a Data Scientist while finishing his PhD, developing a system to cluster job data and predict career paths for groups of individuals.