Watch Machine Learning with scikit-learn and Tensorflow

Machine learning is an emerging technology that has revolutionized the way that companies approach data analysis. In today's world of big data, machine learning is increasingly being used to identify patterns, make predictions, and uncover hidden insights that can help organizations make better decisions.

Machine Learning with scikit-learn and Tensorflow from Packt Publishing is a comprehensive online course that teaches you everything you need to know about machine learning, from basics to advanced concepts. This course is designed to help you gain a deep understanding of the algorithms and techniques used in machine learning, as well as the tools and libraries required to build and deploy machine learning models.

Throughout this course, you will learn about scikit-learn, one of the most popular machine learning libraries for Python, as well as Tensorflow, a powerful framework for building and training deep neural networks. You will learn how to use these tools to develop a variety of different types of machine learning models, including linear regression, logistic regression, decision trees, and neural networks.

The course is divided into several modules, each of which covers a different aspect of machine learning. The first module provides an overview of the basics of machine learning, including concepts like supervised and unsupervised learning, training and testing data, and performance metrics. You will also get hands-on experience with scikit-learn, exploring its key functionalities and learning how to use it to build simple machine learning models.

The second module focuses on regression, a type of machine learning model that is used to predict continuous numerical values. You will learn how to use scikit-learn to develop linear and logistic regression models, as well as decision tree models, which can be used to identify complex nonlinear relationships between variables.

In the third module, you will dive deeper into scikit-learn and learn how to develop more advanced machine learning models using Bayesian methods and ensemble techniques. You will also explore support vector machines, which are used for both classification and regression problems.

The fourth module is devoted to deep learning with Tensorflow. You will learn how to build and train neural networks, which are highly specialized machine learning models that are designed to simulate the way that the human brain works. You will gain hands-on experience with Tensorflow, exploring its key functionalities and learning how to develop deep learning models for a variety of different applications.

The course concludes with a fifth module that focuses on deploying machine learning models. You will learn how to package and deploy your models using Docker containers, as well as how to integrate them into real-world applications using RESTful APIs.

Throughout this course, you will be guided by expert instructors who have extensive experience in machine learning and data science. You will have access to a variety of different learning materials, including video lectures, hands-on exercises, and comprehensive reading materials. You will also have opportunities to collaborate with other students, learning from their experiences and exchanging ideas and insights.

Overall, Machine Learning with scikit-learn and Tensorflow from Packt Publishing is an excellent course for anyone who wants to develop a deep understanding of machine learning and its applications to data science. Whether you are a beginner or an experienced data scientist, this course will provide you with the tools and knowledge you need to build and deploy effective machine learning models.

Machine Learning with scikit-learn and Tensorflow is a series that ran for 1 seasons (33 episodes) between March 28, 2018 and on Packt Publishing