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Discover the concepts of Data Science and Machine Learning through the use of Python. The software includes popular tools such as Numpy, Pandas, Matplotlib, and Scikit-Learn.

This course covers:

This Python for Machine Learning & Data Science Masterclass is an ideal course for those interested in learning the fundamentals of Python and applying it to practical data science and machine learning applications. The course includes 44 hours of on-demand video, 6 articles, 33 downloadable resources, 4 coding exercises and full lifetime access. You can access the material from your mobile or TV, allowing you to learn anytime and anywhere. Upon completion, you will receive a certificate of completion.

What you'll learn

Data science and machine learning with Python can open up exciting opportunities for anyone looking to become proficient in analyzing, visualizing, and gaining insight from data. With the help of data science and machine learning you can turn complex datasets into actionable information that can be used to power meaningful decisions. By learning how to use these powerful tools, you will be able to create complete data pipeline workflows in an efficient manner and build your own portfolio of real world projects.

Some of the major advantages of using Data Science and Machine Learning with Python include the ability to gain insights from large amounts of data quickly, which would otherwise require several hours or days without automation; furthermore, mastering critical scientific skills such as mathematics and statistics is essential when it comes to developing successful models. Knowing how these models work inside out can provide a great advantage when it comes to understanding why certain outcomes are produced. Data scientists understand what questions they are trying to answer through their analysis and then develop models featuring algorithms that try to generate answers autonomously. Therefore, by getting a deep understanding of Machine Learning along with its methods and tools, you’ll have all the skills necessary for improving upon existing models or creating new ones from scratch.

The course will teach you how to utilize Python for data science and machine learning.

Your task is to build data pipeline workflows for the purpose of analyzing, visualizing, and gaining insights from data.

The program will provide the opportunity to create a portfolio of data science projects using authentic data.

By utilizing data science, individuals are able to analyze their own data sets and acquire valuable insights.

Develop essential data science skills.

Acquire a comprehensive understanding of Machine Learning.

Create simulations based on actual scenarios and factual data analysis.

NumPy is a useful tool for numerical processing when working with Python.

Implement feature engineering techniques on practical case studies.

Pandas is a useful tool for data manipulation in Python.

Develop machine learning models with supervision to predict classifications.

Matplotlib can be used to create customized visualizations for data in Python.

The creation of regression machine learning algorithms is focused on predicting continuous values.

Learning Seaborn can help you create visually appealing statistical plots using Python.

Create a portfolio showcasing projects related to data science and machine learning for a resume.

Acquire the skill of utilizing Scikit-learn in order to implement effective machine learning algorithms.

The Anaconda data science stack environment allows for a quick setup process.

Acquire knowledge on effective methods for handling real-life data sets.

Gain a comprehensive understanding of the product workflow involved in the machine learning lifecycle.

Discover the process of deploying machine learning models as interactive APIs.

About the author

Jose Marcial Portilla is a highly experienced professional instructor and trainer for Data Science, Machine Learning and Python Programming. With a Bachelor of Science and Master’s degree in Mechanical Engineering from Santa Clara University, as well as publications and patents in multiple research fields, he has developed a unique skill set for analyzing data. Not only that but he uses his expertise to teach others the power of programming, the ability to analyze data and the skills required to present such data into beautiful visualizations.

Currently he holds the position of Head of Data Science at Pierian Training where he provides personnel with in-person training courses in Data Science and Python Programming. This service is provided at top companies ranging from General Electric to Cigna, SalesForce, Starbucks, McKinsey among others. To find out more information about these offerings feel free to navigate through the website link provided.

Requirements

Requirements for a basic knowledge of Python usually include the ability to create and modify functions. Most beginners should be capable of understanding the fundamentals of programming, such as object-oriented programming, control structures, variables, data types and basic I/O operations. Furthermore, it is necessary to understand the syntax used in Python and have a working understanding of concepts such as scoping, closures and recursion. Also important is having the ability to utilize standard libraries such as NumPy, Pandas or Scikit-Learn that are part of the basic toolkit associated with this popular language.

Description

The world of data science and machine learning is rapidly growing in our modern society, and those who want to stay ahead of the game need the latest knowledge in this area. Join over 3 million students with Jose Portilla’s comprehensive course introducing Python for Data Science and Machine Learning. In this course, you begin by mastering the fundamentals of data processing in python, such as variables and data frames. After that you learn more advanced concepts such as web scraping, natural language processing techniques, building powerful machine learning models with artificial intelligence algorithms, and more. You will even get to explore complex topics like neural networks and deep learning to prepare you for a career in coding. Upon completion of this course, you will have the skills necessary to tackle complex datasets while creating amazing applications with your newly acquired skillset.

Can you provide information on the course contents?

This course offers comprehensive instruction on Data Science and Machine Learning, making it the most complete on the internet. I have spent more than a year compiling what I believe to be the most effective method for beginners in Python to become proficient in data science and machine learning, having taught over 2 million students.

This course is intended for students with prior knowledge of Python who are interested in further developing their skills in data science and machine learning. This course aims to equip students with the in-demand skills needed to increase their employability in today's workforce, with the potential for data scientists to earn a starting salary of over $150,000.

Our course will provide comprehensive coverage of the data science and machine learning tech stack required by top companies worldwide. Our students have been hired by a variety of top tech companies, including McKinsey, Facebook, Amazon, Google, Apple, and Asana. Our course is designed to provide a clear and structured approach, incorporating our online and in-person teaching experience. It aims to guide you through not only the use of data science and machine learning libraries, but also their purpose. The course includes both real-world case studies and mathematical theory for machine learning algorithms, providing a balanced approach.

Our course provides instruction on advanced machine learning algorithms that are not commonly covered in other courses. The methods include advanced regularization techniques and state-of-the-art unsupervised learning methods, such as DBSCAN.

This course is intended for:

This course is perfect for beginner Python developers who are wanting to explore the world of Machine Learning and Data Science. It will teach the essential programming skills used in today’s ML and DS applications. The course is designed to be comprehensive, starting with foundational concepts of Python code and how to construct programs that can interpret datasets. Subsequently, topics such as data analysis, machine learning algorithms and proper execution techniques will be discussed. Practical applications of techniques used in real life scenarios will also be covered throughout the course.

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