Machine Learning with Scikit-Learn and TensorFlow: 2-in-1

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. TensorFlow is quickly becoming the technology of choice for deep learning, because of its ease to  build powerful and sophisticated neural networks. To perform traditional machine learning tasks in supervised learning and unsupervised learning using cutting-edge techniques from deep learning, you need to be familiar with Python and basic machine learning concepts.

This comprehensive 2-in-1 course teaches you how to perform your day-to-day machine learning tasks with Scikit-learn and TensorFlow. It’s a perfect blend of concepts and practical examples which makes it easy to understand and implement. It follows a logical flow where you will be able to build on your understanding of the different machine learning concepts with every section.

This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, TensorFlow 1.X Recipes for Supervised and Unsupervised Learning, starts off with covering the basics of TensorFlow. You will then learn to improve the performance and speed of your machine learning models with the use of deep learning techniques. You will also gain hands-on experience of using both low-level and high-level APIs in TensorFlow to understand which one is better for your project. Next, you will perform unsupervised learning using cutting-edge techniques from deep learning.

The second course, Advanced Predictive Techniques with Scikit-Learn and TensorFlow, teaches you how to use ensemble algorithms to combine many individual predictors to produce better predictions. You will learn to apply advanced techniques such as dimensionality reduction to combine features and build better models. You will also learn to evaluate models and choose the optimal hyper-parameters using cross-validation. Next, you will understand the foundations for working and building models using neural networks. Finally, you will learn different techniques to solve problems that arise while performing predictive analytics in real-world scenario.

By the end of this Learning Path, you'll be able to perform traditional machine learning tasks in supervised learning and unsupervised learning using cutting-edge techniques from deep learning. Also, you’ll learn how to go from building basic predictive models to advanced models to produce better predictions.

Meet Your Expert(s):

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:

Alvaro Fuentes is a Data Scientist with an MSc in Quantitative Economics and MSc in Applied Mathematics with more than 10 years of experience in analytical roles. He worked at the Central Bank of Guatemala as an Economic Analyst, building models for economic and financial data. He founded Quant Company to provide consulting and training services in Data Science topics and has been a consultant for many projects in fields such as Business, Education, Psychology, and Mass Media. He also has taught many (online and on-site) courses to students from around the world in topics such as Data Science, Mathematics, Statistics, R programming, and Python.