Data Science, Deep Learning and Machine Learning with Python

Course details

New!Updated for TensorFlow 1.10

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 and machine learning practitionersin the tech industry - and prepare you for a move into this hot career path. This comprehensive course includes over 80lectures spanning 12hours 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.

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.You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned!

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, AI, and data mining techniques real employers are looking for, including:

• Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras

• Sentiment analysis

• Image recognition and classification

• 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. And you'll also get access to this course's Facebook Group, where you can stay 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 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.

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!