Data Science and Machine Learning with Python - Hands On

Data Science and Machine Learning with Python - Hands On





Details:

Data Science and Machine Learning with Python - Hands On
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 9 Hours | 2.50 GB
Genre: eLearning | Language: English
Become a data scientist in the tech industry! Comprehensive data mining and machine learning course with Python & Spark.



Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too!

If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists in the tech industry - and prepare you for a move into this hot career path. This comprehensive course includes 68 lectures spanning almost 9 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. Ill draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesnt.

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning and data mining techniques real employers are looking for, including:

Regression analysis
K-Means Clustering
Principal Component Analysis
Train/Test and cross validation
Bayesian Methods
Decision Trees and Random Forests
Multivariate Regression
Multi-Level Models
Support Vector Machines
Reinforcement Learning
Collaborative Filtering
K-Nearest Neighbor
Bias/Variance Tradeoff
Ensemble Learning
Term Frequency / Inverse Document Frequency
Experimental Design and A/B Tests

...and much more! There's also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to "big data" analyzed on a computing cluster.

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 pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's; the sample code will also run on MacOS or Linux desktop systems, but I can't provide OS-specific support for them.

Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. Its then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference.

If youre a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry this course will teach you the basic techniques used by real-world industry data scientists. I think you'll enjoy it!



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