diff --git a/README.md b/README.md
index e937eff..1d65972 100644
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@@ -10,11 +10,11 @@ software engineering and machine learning interviews and jobs.
Topics to review so you don't get weeded out.
[Five essential screening questions](https://sites.google.com/site/steveyegge2/five-essential-phone-screen-questions):
- * Coding - writing simple code with correct syntax (C, C++, Java).
- * Object Oriented Design - basic concepts, class models, patterns.
- * Scripting and Regular Expressions - know your Unix tooling.
- * Data Structures - demonstrate basic knowledge of common data structures.
- * Bits and Bytes - know about bits, bytes, and binary numbers.
+* Coding - writing simple code with correct syntax (C, C++, Java).
+* Object Oriented Design - basic concepts, class models, patterns.
+* Scripting and Regular Expressions - know your Unix tooling.
+* Data Structures - demonstrate basic knowledge of common data structures.
+* Bits and Bytes - know about bits, bytes, and binary numbers.
Things you absolutely, positively **must** know:
* Algorithm complexity
@@ -137,6 +137,116 @@ A much longer and fuller list of topics:
## Machine Learning
+### Machine Learning: The Basics
+
+Topics to review so you don't get weeded out.
+* Supervised learning
+* Unsupervised learning
+* Semi-supervised learning
+* Modeling business decisions usually uses supervised and unsupervised learning.
+* Classification and regression are the most commonly seen machine learning models.
+
+### Machine Learning: The Full Topics List
+
+A longer, fuller list of topics:
+
+* Regression
+ * **Modeling relationship between variables, iteratively refined using an error measure.**
+ * Linear Regression
+ * Logistic Regression
+ * OLS (Ordinary Least Squares) Regression
+ * Stepwise Regression
+ * MARS (Multivariate Adaptive Regression Splines)
+ * LOESS (Locally Estimated Scatterplot Smoothing)
+
+* Instance Based
+ * **Build up database of data, compare new data to database; winner-take-all or memory-based learning.**
+ * k-Nearest Neighbor
+ * Learning Vector quantization
+ * Self-Organizing Map
+ * Localy Weighted Learning
+
+* Regularization
+ * **Extension made to other methods, penalizes model complexity, favors simpler and more generalizable models.**
+ * Ridge Regression
+ * LASSO (Least Absolute Shrinkage and Selection Operator)
+ * Elastic Net
+ * LARS (Least Angle Regression)
+
+* Decision Tree
+ * **Construct a model of decisions made on actual values of attributes in the data.**
+ * Classification and Regression Tree
+ * CHAID (Chi-Squared Automatic Interaction Detection)
+ * Conditional Decision Trees
+
+* Bayesian
+ * **Methods explicitly applying Bayes' Theorem for classification and regression problems.**
+ * Naive Bayes
+ * Gaussian Naive Bayes
+ * Multinomial Naive Bayes
+ * Bayesian Netowrk
+ * BBN (Bayesian Belief Network)
+
+* Clustering
+ * **Centroid-based and hierarchical modeling approaches; groups of maximum commonality.**
+ * k-Means
+ * k-Medians
+ * Expectation Maximization
+ * Hierarchical Clustering
+
+* Association Rule Algorithms
+ * **Extract rules that best explain relationships between variables in data.**
+ * Apriori algorithm
+ * Eclat algorithm
+
+* Neural Networks
+ * **Inspired by structure and function of biological neural networks, used ofr regression and classification problems.**
+ * Radial Basis Function Network (RBFN)
+ * Perceptron
+ * Back-Propagation
+ * Hopfield Network
+
+* Deep Learning
+ * **Neural networks that exploit cheap and abundant computational power; semi-supervised, lots of data.**
+ * Convolutional Neural Network (CNN)
+ * Recurrent Neural Network (RNN)
+ * Long-Short-Term Memory Network (LSTM)
+ * Deep Boltzmann Machine (DBM)
+ * Deep Belief Network (DBN)
+ * Stacked Auto-Encoders
+
+* Dimensionality Reduction
+ * **Find inherent structure in data, in an unsupervised manner, to describe data using less information.**
+ * PCA
+ * t-SNE
+ * PLS (Partial Least Squares Regression)
+ * Sammon Mapping
+ * Multidimensional Scaling
+ * Projection Pursuit
+ * Principal Component Regression
+ * Partial Least Squares Discriminant Analysis
+ * Mixture Discriminant Analysis
+ * Quadratic Discriminant Analysis
+ * Regularized Discriminant Analysis
+ * Linear Discriminant Analysis
+
+* Ensemble
+ * **Models composed of multiple weaker models, independently trained, that provide a combined prediction.**
+ * Random Forest
+ * Gradient Boosting Machines (GBM)
+ * Boosting
+ * Bootstrapped Aggregation (Bagging)
+ * AdaBoost
+ * Stacked Generalization (Blending)
+ * Gradient Boosted Regression Trees
+
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