Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: aac, 44100 Hz
Language: English | VTT | Size: 9.38 GB | Duration: 12 sections | 111 lectures | (14h 14m)

You’ll need a desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3 or newer.

What you’ll learn

Build artificial neural networks with Tensorflow and Keras

Classify images, data, and sennts using deep learning

Make predictions using linear regression, polynomial regression, and multivariate regression

Data Visualization with MatPlotLib and Seaborn

Implement machine learning at massive scale with Apache Spark’s MLLib

Understand reinforcement learning – and how to build a Pac-Man bot

Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA

Use train/test and K-Fold cross validation to choose and tune your models

Build a movie recommender system using item-based and user-based collaborative filtering

Clean your input data to remove outliers

Design and evaluate A/B tests using T-Tests and P-Values


The course will walk you through installing the necessary free software.

Some prior coding or scripting experience is required.

At least high school level math skills will be required.


New! Updated for Winter 2019 with extra content on feature eeering, regularization techniques, and tuning neural networks – as well as Tensorflow 2.0!

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, , 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 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 practitioners in the tech industry – and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 14 hours 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 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 expent with and build upon, along with notes you can keep for future reference. You won’t find acad, 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

Data Visualization in Python with MatPlotLib and Seaborn

Transfer Learning

Sennt 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

Expental Design and A/B Tests

Feature Eeering

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. 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, 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 in 2015… 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

Who this course is for:

Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course.

Technologists curious about how deep learning really works

Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you’ll need some prior experience in coding or scripting to be successful.

If you have no prior coding or scripting experience, you should NOT take this course – yet. Go take an introductory Python course first.





Please enter your comment!
Please enter your name here