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.