Machine Learning, Data Science and Deep Learning with Python

• 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

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, AI, and data mining techniques real employers are looking for, including:

• Deep Learning / Neural Networks (MLP’s, CNN’s, RNN’s) with TensorFlow and Keras
• Creating synthetic images with Variational Auto-Encoders (VAE’s) and Generative Adversarial Networks (GAN’s)
• Data Visualization in Python with MatPlotLib and Seaborn
• Transfer Learning
• Sentiment analysis
• Image recognition and classification
• Regression analysis
• K-Means Clustering
• Principal Component Analysis
• Train/Test and cross validation
• Bayesian Methods
• Decision Trees and Random Forests
• Multiple 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
• Feature Engineering
• Hyperparameter Tuning