Watch Advanced Predictive Techniques with Scikit-Learn and TensorFlow

Advanced Predictive Techniques with Scikit-Learn and TensorFlow is a video training course offered by Packt Publishing. The course is aimed at professionals who have some basic knowledge and experience in Python programming and are looking to expand their skills in predictive modeling.

The course is taught by Dr. Valentina Porcu, a data scientist and consultant with more than 10 years of experience in the field. Dr. Porcu introduces learners to the concepts of predictive modeling, including supervised and unsupervised learning, and explains how these techniques can be applied in real-world scenarios.

The course is divided into 7 modules, each focusing on a different aspect of predictive modeling. The first module provides an introduction to the course and explains the basic concepts of machine learning. Dr. Porcu then proceeds to the second module, where she provides an overview of the Scikit-Learn library and explains how it can be used to implement various machine learning algorithms.

The third module of the course is dedicated to exploring the concepts of supervised learning, such as linear regression, logistic regression, and decision trees. In this module, Dr. Porcu demonstrates how to build predictive models using these algorithms and how to evaluate their performance.

The fourth module of the course focuses on unsupervised learning techniques, such as clustering and principal component analysis. Dr. Porcu explains how these techniques can be used to identify patterns in data and how they can be used to make predictions.

The fifth module of the course is dedicated to working with TensorFlow, a popular deep learning framework. Dr. Porcu provides an overview of TensorFlow and explains how it can be used to develop and train neural networks.

In the sixth module of the course, Dr. Porcu demonstrates how to implement various deep learning techniques using TensorFlow, including convolutional neural networks, recurrent neural networks, and autoencoders.

The final module of the course focuses on advanced topics in predictive modeling, such as model selection, feature engineering, and ensembling. Dr. Porcu explains how these techniques can be used to improve the accuracy and performance of predictive models.

Throughout the course, Dr. Porcu provides hands-on training and real-world examples to help learners understand the concepts and techniques of predictive modeling. The course also includes quizzes and exercises to test learners' knowledge and reinforce their learning.

Overall, Advanced Predictive Techniques with Scikit-Learn and TensorFlow is a comprehensive and practical course that teaches learners how to build and evaluate predictive models using machine learning and deep learning techniques. Whether you're a data scientist, software developer, or analyst, this course will help you expand your skills and advance your career in the field of predictive modeling.