84 Deep Learning Pre Requisites

Course Resources

What you’ll learn

Build artificial neural networks with Tensorflow and Keras
Make predictions using linear regression, polynomial regression, and multivariate regression
Build Deep Learning networks to classify images with Convolutional Neural Networks
Implement machine learning, clustering, and search using TF/IDF at massive scale with Apache Spark’s MLLib
Implement Sentiment Analysis with Recurrent Neural Networks
Understand reinforcement learning – and how to build a Pac-Man bot
Classify medical test results with a wide variety of supervised machine learning classification techniques
Cluster data using K-Means clustering and Support Vector Machines (SVM)
Build a spam classifier using Naive Bayes
Use decision trees to predict hiring decisions
Apply dimensionality reduction with Principal Component Analysis (PCA) to classify flowers
Predict classifications using K-Nearest-Neighbor (KNN)
Develop using iPython notebooks
Understand statistical measures such as standard deviation
Visualize data distributions, probability mass functions, and probability density functions
Visualize data with matplotlib
Use covariance and correlation metrics
Apply conditional probability for finding correlated features
Use Bayes’ Theorem to identify false positives
Understand complex multi-level models
Use train/test and K-Fold cross validation to choose the right model
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 Anaconda 3 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.
Description
New! Updated for Winter 2019 with extra content on feature engineering, regularization techniques, and tuning neural networks – as well as Tensorflow 2.0 support!

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!

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

Requisites