Coursera Ibm Machine Learning

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Want to start a career in deep learning? Look no further. This course will introduce you to the field of deep learning and help you answer many of the questions people are asking today, such as what is deep learning and how do deep learning models compare with artificial neural networks? You will learn about different deep learning models and use the Keras library to build your first deep learning model.

Coursera Ibm Machine Learning

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world’s most advanced servers and supercomputers. IBM uses its business consulting, technology and research and development expertise to help clients become “smarter” as the planet becomes more digitally connected. IBM, which invests more than $6 billion a year in research and development, has just completed its 21st year as a leader in patents. IBM Research is more recognized than any commercial technology research institution, with 5 Nobel Laureates, 9 National Medals of Technology, 5 National Medals of Science, 6 Turing Awards and 10 members of the American Inventors Hall of Fame.

Applied Machine Learning In Python

Over 10 years of initiating and delivering sustained results and effective changes for companies in various industries, including during quarantine I tried a lot of things and explored some new topics in data science, one of my discoveries was that the entry level certificate was given. Presented by IBM, I highly recommend it to anyone trying to get into data science or who has already started their journey!

I personally started my journey like most people trying to learn stuff from different sources, doing projects and reading Chili, but I really missed some key points!

IBM’s professional certificate is for anyone interested in developing the skills and experience for a career in data science or machine learning. It is designed to help you build a solid foundation and refine your data scientist mindset. Also useful for advanced practitioners!

The program consists of 9 courses covering a wide range of data science topics including: open source tools and libraries, methodology, Python, databases, SQL, data visualization, data analysis and machine learning. You’ll get hands-on in the IBM Cloud using real-world data science tools and real-world datasets.

Supervised Machine Learning: Regression And Classification

In this course, we will meet some data science practitioners and get an overview of what data science is today.

What are the most popular data science tools, how do you use them and what are their characteristics? In this course you will learn about:

You will learn what each tool does, the programming languages ​​they can implement, their features and limitations. With tools hosted in the Cognitive Classroom Lab cloud, you’ll be able to test each tool and follow instructions to run simple code in Python, R, or Scala. At the end of the course, you will use Jupyter Notebook to create a final project.

The course has a goal, which is to share an approach that can be used in data science to ensure that the data used to solve a problem are relevant and can be processed appropriately to solve the problem. Therefore, in this course you will learn to:

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An Introduction to Python will begin your learning of Python for data science and programming in general. This beginner-friendly Python course will take you from scratch to programming in Python in hours.

The purpose of this course is to introduce relational database concepts and help you learn and apply the fundamentals of the SQL language. It is also designed to help you get started with SQL Access in a data science environment. The focus of this course is practical learning. As such, you will be working with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs, you learn to build and run SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and Python. No prior knowledge of databases, SQL, Python or programming is required.

Learn how to analyze data with Python. This course will take you from the basics of Python to exploring many different types of data. You’ll learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more!

You will work more with the Pandas, Numpy and Scipy libraries on example datasets. Then the course will introduce you to another open source library, sikit-learn, and you’ll use some of its machine learning algorithms to build intelligent models and make cool predictions.

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“A picture is worth a thousand words”. Data visualization plays a vital role in the representation of small and large data.

A key skill for a data scientist is the ability to tell a compelling story, visualize data and discoveries in an approachable and uplifting way. Learning how to visualize data with software tools also allows you to extract information, better understand the data and make more effective decisions. The main goal of this Data Visualization and Python course is to teach you how to take data that doesn’t make sense at first glance and present it in a form that people understand.

This capstone project course will give you a taste of what data scientists go through in real life when working with data.

You will learn about location data and different location data providers, such as Foursquare. You will learn how to make RESTful API calls to the Foursquare API to retrieve data about venues in different neighborhoods around the world. You’ll also learn how to get creative by scraping web data and parsing HTML code when data isn’t available. You will use Python and its pandas library to manipulate data, which will help you improve your skills in exploring and analyzing data. Finally, you will need to use the Folium library to draw beautiful maps of your geospatial data and communicate your results and findings.

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After completing the specialization, you will earn additional credentials for two other specializations, as courses in these specializations are included in the IBM Data Science specialization course list, which are:

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