Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. scikit-learn has evolved as a robust library for machine learning applications in Python with support for a wide range of supervised and unsupervised learning algorithms. If you’re a data scientist or an IT professional who wants to learn machine learning using Python’s popular machine learning library scikit-learn, then go for this course.
This course teaches you how to perform your day-to-day machine learning tasks with scikit-learn. It’s a perfect blend of concepts and practical examples which makes it easy to understand and implement. You will begin with learning important machine learning algorithms that are commonly used in the field of data science such as Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, and Feature engineering. You will also be taken through supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning with hands-on practical projects.
Contents and Overview
This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.
In the first course, Fundamentals of Machine Learning with scikit-learn, you will learn all the important machine learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. The algorithms you will be learning are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, and Feature engineering.
In the second course, Learn Machine Learning in 3 Hours, you will learn key ML algorithms and how they can be trained for classification and regression. You will also work with supervised and unsupervised learning to help to get to grips with both types of algorithms.
In the third course, Real-World Machine Learning Projects with Scikit-Learn, you will build powerful projects using scikit-learn. Using algorithms, you will learn to read trends in the market to address market demand. You'll delve more deeply to decode buying behavior using Classification algorithms; cluster the population of a place to gain insights into using K-Means Clustering; and create a model using Support Vector Machine classifiers to predict heart disease.
By the end of this course, you will get hands-on with machine learning using powerful features of scikit-learn to implement the best machine learning practices.
Meet Your Expert(s):
We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:
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 are in artificial intelligence, machine learning, data science, and the philosophy of the mind.
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.
Nikola Živković is a software developer with over 7 years of experience in the industry. He earned a Master's degree in Computer Science from the University of Novi Sad back in 2011, but by then he was already working for several companies. At the moment he is a part of the Vega IT Sourcing team from Novi Sad. During his time in the industry, he worked on large enterprise systems, small web projects, data- and time-sensitive projects, as well as on machine learning projects. Apart from that, he has experience in knowledge sharing, talking at meetups, conferences, and as a guest lecturer at the University of Novi Sad.