Machine Learning, Data Science and Deep Learning with Python

The course is a combination of various data science concepts such as machine learning, visualization, data mining, programming, data mugging, etc. You will be using popular scientific Python libraries such as Numpy, Scipy, Scikit-learn, Pandas throughout the course.

Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks

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Created by Sundog Education by Frank Kane, Frank Kane

Last updated 4/2019

English
English, Indonesian [Auto-generated], 5 more

Description

New! Updated for TensorFlow 1.10

Machine Learning and artificial intelligence (AI) is 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 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!

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!

What you'll learn

Build artificial neural networks with Tensorflow and Keras
Make predictions using linear regression, polynomial regression, and multivariate regression
Classify images, data, and sentiments using deep learning
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

Requirements

You'll need a desktop computer (Windows, Mac, or Linux) capable of running Enthought Canopy 1.6.2 or newer. 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.

This course walks through getting set up on a Microsoft Windows based desktop PC. While the code in this course will run on other operating systems, we cannot provide OS-specific support for them.

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Machine Learning, Data Science and Deep Learning with Python