Courses & Tutorials

Machine Learning, Data Science and Deep Learning with Python

Description

Machine learning and artificial intelligence (AI) are everywhere; if you want to understand how companies such as Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will provide you with the basic knowledge you need. According to data from Glassdoor and Indeed, data scientists enjoy one of the highest paying jobs, with an average salary of $120,000. That’s just an average! It’s not just about money-it’s also an interesting job!

If you have some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the technology industry-and prepare you for this popular career path. This comprehensive machine learning tutorial includes more than 100 lectures, 15 hours of video, and most topics include hands-on Python code examples for reference and practice. I will use my 9 years of experience at Amazon and IMDb to guide you through what is important and what is not.

Each concept is introduced in simple English to avoid confusion with mathematical symbols and jargon. Then use the Python code you can experiment and build for demonstrations, and comments that you can keep for future reference. In this course, you will not find academic, in-depth mathematical coverage of these algorithms-the focus is on their practical understanding and application. Finally, you will get a final project to apply what you have learned!

This course is suitable for:

  • Software developers or programmers who want to transition to a lucrative data science and machine learning career path will learn a lot from this course.
  • Technicians are curious about how deep learning really works
  • Data analysts in finance or other non-tech industries who want to transition to the technology industry can learn how to use codes instead of tools to analyze data through this course. However, you need some prior experience in coding or scripting to be successful.
  • If you have no previous coding or scripting experience, you should not take this course-temporarily. First take the Python introductory course.

What you will learn

  • Use Tensorflow and Keras to build an artificial neural network
  • Use deep learning to classify images, data, and emotions
  • Use linear regression, polynomial regression, and multiple regression to make predictions
  • Use MatPlotLib and Seaborn for data visualization
  • Implement machine learning at scale using Apache Spark’s MLLib
  • Learn about reinforcement learning-and how to build a Pac-Man robot
  • Use K-Means clustering, support vector machines (SVM), KNN, decision trees, naive Bayes, and PCA to classify data
  • Use training/testing and K-fold cross-validation to select and adjust your model
  • Use project-based and user-based collaborative filtering to build a movie recommendation system
  • Clean input data to remove outliers
  • Design and evaluate A/B tests using T-tests and P-values

Specifications

  • Course Length: 15.5 Hours
  • Course Size: 7.42 Go
  • Number of sections: 12

How to Get the Course

Write an email to fullcoursesondemand@gmail.com for more detail

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