Machine Learning in Python (Data Science and Deep Learning)

Description

In this course, you will :

• 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.