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
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
Variance: measure how "spread out" the data is
Standard Deviation: to identify the outliers
Correlation: 0 no correlation Correlation: 1 perfect correlation