Machine Learning and Data Science Hands-on with Python and R Programming

Link : Machine Learning and Data Science Hands-on with Python and R Programming

Learn Statistics, Python, Artificial Intelligence AI, Tensorflow, AWS. Learn Deep Learning, R Programming, NLP, Bayesian Methods, A/B Testing, Business Intelligence BI, Regression. ... Machine learning is a scientific discipline that explores the construction and study of algorithms ...BESTSELLER

4.5 (14,790 ratings)
94,414 students enrolled
Created by Sundog Education by Frank Kane, Frank Kane

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.

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 13 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.

Share this post