This course is crafted to teach you the most in-demand skills in the real world. This course aims to help you understand all the data science and machine learning concepts and methodologies with regard to Python.
When you take a quick look at the different sections of this course, you may think of these sections as being independent. However, these sections are interlinked and almost sequential. Each section is an independent concept and is like a course on its own. We have deliberately arranged these sections in a sequence as each subsequent section builds upon the sections you have completed. This framework enables you to explore more independent concepts easily.
At the end of every subsection, you are assigned homework to further strengthen your learning. All these assessments are based on the previous concepts and methods you have learned. Several of these assessment tasks will be coding-based, as the main aim is to get you up and proceeding to implementations.
By the end of this course, you will be able to easily tackle real-world problems and ensure steady career growth and will be equipped with the knowledge of all the essential concepts you need in order to excel as a data science professional.
The complete code bundle for this course is available at: https://github.com/PacktPublishing/Data-Science-and-Machine-Learning-Th…
publication date
2021-11-30
what you will learn
Understand and visualize data with Python
Explore probability and statistics in Python
Learn feature engineering and dimensionality reduction with Python
Cover artificial neural networks with Python
Cover CNN and RNN with Python
Learn deep reinforcement learning applications
key features
Advanced and recently discovered models and breakthroughs by the champions in the AI field * Key data science and machine learning concepts with examples in Python * Detailed explanation and live coding with Python
approach
Through this learning-by-doing course, you will acquire a solid academic foundation as well as practical hands-on training in data science and machine learning. After completing this course, you will have a solid grasp of all the key concepts needed for success in the fields of data science and machine learning.
audience
People who want to learn data science and machine learning with real datasets in data science, people who want to enter the field of data science from a non-engineering background, people who want to enter the field of machine learning, and people who want to learn data science and machine learning along with its implementation in practical projects are the target audience for this course.
There is no prerequisite for this course. You will begin with the fundamental ideas and gradually increase your understanding of the topic.
short description
A carefully crafted beginner’s friendly course that will equip you with all the required skills and key concepts. Learn key data science and machine learning concepts right from the beginning with examples in Python. You will also explore core concepts and methodologies of RL and Deep RL, along with several practical implementations.
subtitle
Learn Statistics, Machine Learning, Deep Learning, and Reinforcement Learning for Data Science in 100 Hours
keywords
Data Science, Machine Learning, Deep Learning, Data Visualization, Convolutional Neural Networks, Artificial Neural Networks, Recurrent Neural Networks, Data Analysis, Feature Engineering, Dimensionality Reduction, Probability, Statistics, Python, Reinforcement, Deep Reinforcement Learning, DQN
Product ISBN
9781803230146
