Machine Learning, Data Science and Deep Learning with Python
Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks
What you’ll learn
- Build artificial neural networks with Tensorflow and Keras
- Classify images, data, and sentiments 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
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 2020 with extra content on feature engineering, 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, 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!
…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.
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.
