Data Science, Deep Learning, & Machine Learning with Python
English | Size: 3.07 GB
Category: Career
What Will I Learn?
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
Make predictions using linear regression, polynomial regression, and multivariate regression
Understand complex multi-level models
Use train/test and K-Fold cross validation to choose the right model
Build a spam classifier using Naive Bayes
Use decision trees to predict hiring decisions
Cluster data using K-Means clustering and Support Vector Machines (SVM)
Build a movie recommender system using item-based and user-based collaborative filtering
Predict classifications using K-Nearest-Neighbor (KNN)
Apply dimensionality reduction with Principal Component Analysis (PCA) to classify flowers
Understand reinforcement learning – and how to build a Pac-Man bot
Clean your input data to remove outliers
Implement machine learning, clustering, and search using TF/IDF at massive scale with Apache Spark’s MLLib
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.
Description
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 course includes over 80 lectures spanning 12 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 and data mining techniques real employers are looking for, including:
Deep Learning / Neural Networks (MLP’s, CNN’s, RNN’s)
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 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. I think you’ll enjoy it!
Who is the target audience?
Software developers or programmers who want to transition into the lucrative data science career path will learn a lot from this course.
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.
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