Virtual course of: Udemy |
This course was ranked in the Top 100 of the best courses on Udemy, within a catalog of more than 135.000 courses.
New! Updated for 2020 with additional content on function engineering, regularization techniques, and neural network tuning, as well as Tensorflow .0! Machine learning and artificial intelligence (AI) are everywhere; If you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need.
Data scientists enjoy one of the highest paying jobs, with a median salary of $120,000 according to Glassdoor and Indeed.
That's just average! And it's not just about money, it's also interesting work! If you have some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning professionals in the technology industry, preparing you to move into this career path.
This comprehensive machine learning tutorial includes over 100 lessons spanning 14 hours of video, and most topics include hands-on Python code examples that you can use for reference and practice.
I'll draw on my 9 years of Amazon and IMDb experience to guide you through what matters and what doesn't.
Each concept is presented in plain language, avoiding the confusion of mathematical notation and jargon.
It is then demonstrated using Python code that you can experiment with and build on, along with notes that you can save for future reference.
You won't find scholarly, deeply mathematical coverage of these algorithms in this course; the focus is on the practical understanding and application of them.
At the end, you will be given a capstone project to apply what you have learned.
Topics in this course come from an analysis of the actual requirements in data scientist job listings from leading technology employers.
We'll cover the machine learning, artificial intelligence, and data mining techniques that real employers are looking for, including: Deep Learning / Neural Networks (MLPs, CNNs, RNN's) with TensorFlow and Keras Data Visualization in Python with MatPlotLib and Seaborn Transfer Learning sentimentImage Recognition and ClassificationRegression AnalysisK-Means ClusteringPrincipal Components AnalysisTrain/Test and Cross-ValidationBayesian MethodsDecision Trees and Random ForestsMultiple Regression/Inverse Frequency of DocumentsExperimental Design and A/B TestingFeature EngineeringHyperparameter Tuning.
and much more! There is also a comprehensive section on machine learning with Apache Spark, which allows you to scale these techniques to "big data" analyzed on a compute cluster.
And you will also have access to the Facebook group of this course, where you can keep in touch with your classmates.
If you're new to Python, don't worry: the course starts with a crash course.
If you've done some programming before, you should get started quickly.
This course shows you how to set up your Microsoft Windows PC, Linux desktop, and Mac.
Whether you are a programmer looking to switch to a new and exciting career, or a data analyst looking to transition into the technology industry, this course will teach you the basic techniques used by industry data scientists around the world. real.
These are topics that any successful technologist needs to know about, so what are you waiting for? Sign up now! "I started doing your course in 201. Over time I got interested and I never thought I would work for a company before a friend offered me this job.
I am learning a lot that was impossible to learn in the academy and thoroughly enjoying it.
For me, your course is the one that helped me understand how to work with corporate problems.
How to think for success in corporate AI research.
I find you the most impressive instructor in ML, simple yet compelling."
Kanad Basu, Ph.D.
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