New!Updated with extra content on generative models:variational auto-encoders (VAE's) and generative adversarial models (GAN's)

Machine Learning and artificial intelligence (AI)is everywhere; if you want to know how companies like Google, Amazon, and evenUdemy 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 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 machine learning tutorial includes over 100lectures spanning 15hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t.

Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s 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 A-Z of 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

• Creating synthetic images with Variational Auto-Encoders (VAE's) and Generative Adversarial Networks (GAN's)

• Data Visualization in Python with MatPlotLib and Seaborn

• Transfer Learning

• 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

• Multiple 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

• Feature Engineering

• Hyperparameter Tuning

...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, Linux desktops, and Macs.

If you’re 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. These are topics any successful technologist absolutely needs to know about, so what are you waiting for?Enroll now!

• "I started doing your course... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD