## 1. Data Preparation

### 1.1. Unbalanced Classes

1.1.1. Collect more data

1.1.2. Change performance metrics

1.1.2.1. Confusion matrix

1.1.2.2. Precision

1.1.2.3. Recall

1.1.2.4. F1

1.1.2.5. Kappa

1.1.2.6. ROC Curves

1.1.3. Resampling data

1.1.3.1. Up sampling

1.1.3.1.1. 'Oversampling'

1.1.3.2. Down sampling

1.1.4. Generate Synthetic samples

1.1.4.1. SMOTE

1.1.5. Try different algorithms

1.1.6. Try Penalized Models

1.1.6.1. e.g. penalized-LDA

1.1.6.2. Weka CostSensitive wappers

1.1.7. Try different approaches

1.1.7.1. Anomaly detection

1.1.7.2. Change detection

1.1.8. Get creative

1.1.8.1. Split into smaller problems

### 1.2. Scaling/normalizing (feature scaling)

1.2.1. best for numeric variables which are on different scales (e.g. height = 178m, score = 10,000, shoesize = 5).

1.2.2. This will make gradient descent work much better! as less back and forth as it tries to find local minimum between the parameters.

1.2.3. Many variations but generally we want to get all features into approximately a -1 < x < 1 range

1.2.4. MEAN NORMALIZATION: X - Xu / Xmax - Xmin

1.2.4.1. Will have a Xu ~= 0

1.2.4.2. Can also use standard deviation as denominator (X / s)

### 1.3. Feature construction

## 2. NLP

### 2.1. Text preparation

2.1.1. Remove punctuation

2.1.2. Lower case

2.1.3. Tokenize words

2.1.4. Remove stop words

2.1.5. Remove blanks

2.1.6. Remove single letter words

2.1.7. Remove/translate non-english words

2.1.8. Stemming/Lemitization

2.1.8.1. Snowball ()

### 2.2. Tool-kits

2.2.1. NLTK (python)

2.2.2. Spacy (python)

### 2.3. Text classifiction

2.3.1. 1. Prepare text data (see text preparation)

2.3.2. 2. CountVectorize each feature (word) into a matrix

2.3.3. 3. Apply TD-IDF (Term Frequency, Inverse-Term-Frequency) to account for different length of documents

2.3.4. 4. Split data set into variables (countvector of text) and target (category label of the text)

2.3.5. 5. Deploy standard ML classification process (model, evaluate, iterate/tune)

### 2.4. Topic modelling

### 2.5. Entity recognition

## 3. Network Analysis

### 3.1. Metrics

3.1.1. Centricity

3.1.2. Betweenness

### 3.2. Data format

3.2.1. Node_df = NAME, NODE_ATTRIBUTE_1 ,NODE_ATTRIBUTE_N Relation_df = FROM, TO, EDGE_ATTRIBUTES_1, EDGE_ATTRIBUTE_N

## 4. Optimization algorithms

## 5. Front-End (Web application tools)

### 5.1. Flask (python)

### 5.2. Shiny (R)

### 5.3. Dash (Python)

### 5.4. Tableau

### 5.5. Carto

### 5.6. Angular/React (JS)

## 6. Data gathering

### 6.1. APIs

### 6.2. Web Scrapers

6.2.1. Selenium/PhantonJS

6.2.1.1. Good when info is behind JS or when you need to interact with the browser (e.g. login as a human)

6.2.2. BeautifulSoup

6.2.2.1. Simple scraper than you can use directly in a python script

6.2.3. Scrapy

6.2.3.1. Most developed and efficient scraper for large trawling. Also offers lots of functionality to customize (e.g. IP masking). Though needs to be setup with correct directory and class structures.

### 6.3. Manual Labeling

6.3.1. Manual

6.3.2. Services

6.3.2.1. Mechanical turk (etc.)

6.3.3. Exotic sampling

### 6.4. Major file types

6.4.1. CSV

6.4.2. JSON

## 7. HL Programming Languages

### 7.1. R

### 7.2. Python

7.2.1. Vectorization

7.2.1.1. Matrix / for loops

7.2.1.1.1. Matrix multiplications applied across an entire dataset is much more efficient that a for loop as do not have to reset and find memory space for each variables each time and has pre-indexed order for column vector

### 7.3. Octave

## 8. Linear Algebra

### 8.1. Vector/Matrix operations

8.1.1. Matrix/Matrix Addition

8.1.2. Matrix/Matrix Multiplication

8.1.3. Matrix/Vector Multiplication

### 8.2. Matrix properties

8.2.1. Matrices are not commutative (A*B != B*A)

8.2.2. Matrices are associative (A*B)*C = A*(B*C)

8.2.3. Matrices with the identity matrix are commutative (AI = IA)

8.2.4. SHAPE(M) = ALWAYS Row,Columns (R,C) (e.g. 2,3)

### 8.3. Inverse & Transposed Matrices

8.3.1. Inverse: A*A^-1 = A^-1*A = I

8.3.1.1. (A^-1 is the inverse matrix of A, though not all matrices have an inverse)

8.3.2. Transpose: A -> AT (where A is a m*n matrix and AT is an n*m, where Aij = ATji) First column becomes first row basically.

8.3.2.1. X

## 9. Statistics

### 9.1. Distributions

9.1.1. Gaussian (normal) distribution

9.1.1.1. Described by the mean (u) and variance (σ2) - middle is mean, width is 95% in 2σ

9.1.1.1.1. https://upload.wikimedia.org/wikipedia/commons/7/74/Normal_Distribution_PDF.svg

9.1.1.2. 'Bell shaped curve'

9.1.1.3. probability distribution = 1

### 9.2. Statistical tests

9.2.1. t-test

9.2.2. ANOVA

## 10. Time-series analysis

## 11. Data Project Management

### 11.1. CRISP-DM

11.1.1. 1. Business understanding

11.1.2. 2. Data understanding

11.1.3. 3. Data preperation

11.1.4. 4. Modelling

11.1.5. 5. Evaluation

11.1.6. 6. Deployment

11.1.7. https://pbs.twimg.com/media/DNF5vACVQAAxOWD.jpg

### 11.2. Ceiling Analysis

11.2.1. Assess which part of the pipeline is most valuable to spend your time?

11.2.2. To do this, override each module/step with the perfect output (e.g. replace predictions with correct labels) for each module and assess where getting closer

## 12. Data Vizualisation libraries

### 12.1. GGPLOT2(R)

### 12.2. MATPLOTLIB(PYTHON)

### 12.3. SEABORN(PYTHON)

### 12.4. PLOTLY(PYTHON)

## 13. Machine Learning

### 13.1. Generic ML approaches

13.1.1. ML Diagnostics (assess algorithms)

13.1.1.1. Over-fitting (high variance)

13.1.1.1.1. The hypothesis equation is 'over fit' to the training data (e.g. complex polynomial equation that passes through each data point) meaning it performs very well in training but fails generalize well in testing

13.1.1.2. Under-fitting (high bias)

13.1.1.2.1. The hypothesis equation is 'under fit' meaning it over generalized the problem (e.g. using a basic linear separation line for a polynomial problem), meaning if cannot identify more complex cases well

13.1.1.3. Approaches

13.1.1.3.1. Cross-validation

13.1.1.3.2. Learning curves

13.1.1.3.3. General diagnostic options

13.1.2. Generic ML algorithm Methodology

13.1.2.1. Input: x, the input variable that predicts y

13.1.2.2. target: y, a labelled outcome

13.1.2.3. hypothesis: h(x), the function line that is a function of x

13.1.2.4. Parameter: θ, the parameter(s) we choose with the objective of minimising the cost function

13.1.2.5. Cost function: J(θ) a function of the parameters that we try to reduce to get a good prediction (e.g. MSE). We can plot this to see the minimum point.

13.1.2.5.1. https://raw.githubusercontent.com/ritchieng/machine-learning-stanford/master/w1_linear_regression_one_variable/2_params.png

13.1.2.5.2. e.g. RMSE

13.1.2.6. Goal: minimize J(θ), the goal of the algorithm to minimize the error of the cost function through changing the parameters

13.1.2.7. Gradient decent (cost reduction mechanism): Repeat θj := θj - α dθj/d J(θ)

13.1.2.7.1. := assignment operator, take a and make it b

13.1.2.7.2. α = learning rate = how big steps to take, if it is too small then baby-steps will take a lot of time, if too big can fail to converge, or even diverge. The learning rate impact varys depending of slope of the derivative - This means that closer to convergence the steps will be smaller anyway.

13.1.2.7.3. Simultaneously updates all parameters!

13.1.2.7.4. dθj/d J(θ) = derivative function, the slope of the straight line at the tangent of the curve at each point (derivative). If slope is positive then it is θ - positive number makes θ less, if slope is negative then makes θ more until we get to a point where derivative is 0 (local minimum).

13.1.2.7.5. Sometimes called "Batch" gradient decent as it looks at all the available examples in the training set (compared to cross-validation where we look at a sub-set of samples)

13.1.2.7.6. Pros: works well even when you have a large number of features - so scales well.

13.1.2.7.7. Cons: you need to choose a learning rate (α) and you need to do lots of iterations

13.1.2.7.8. There are however other ways of solving this problem

13.1.2.8. Prediction: a predict value of y using a new x sample and a θ trained by reducing the cost function for the training set

13.1.3. The phenomenon of increasing training data

13.1.3.1. X 2001

13.1.3.2. This only holds if the features X hold enough information to predict y (i.e. predicting missing word from a specific sentence compared to trying to predict house prices from only having the square feet ... not possible even for human experts)

### 13.2. Supervised (predictive models)

13.2.1. Classification models

13.2.1.1. Performance Metrics

13.2.1.1.1. Confusion matrix http://www.dataschool.io/content/images/2015/01/confusion_matrix2.png

13.2.1.1.2. Simple Metrics

13.2.1.1.3. Advanced Metrics

13.2.1.1.4. Other considerations

13.2.1.2. Classification Model Types

13.2.1.2.1. Logistic Regression

13.2.1.2.2. SVMs

13.2.1.2.3. KNN

13.2.1.2.4. Decision Trees

13.2.1.2.5. Random Forest

13.2.1.2.6. XGBoost

13.2.1.3. Classification types

13.2.1.3.1. Binary class

13.2.1.3.2. Multi class

13.2.2. Regression models

13.2.2.1. Performance Metrics / Cost function

13.2.2.1.1. We can measure the accuracy of our hypothesis function by using a cost function. This takes an average difference (actually a fancier version of an average) of all the results of the hypothesis with inputs from x's and the actual output y's.

13.2.2.1.2. We can measure the accuracy of our hypothesis function by using a cost function. This takes an average difference (actually a fancier version of an average) of all the results of the hypothesis with inputs from x's and the actual output y's.

13.2.2.1.3. Cost functions

13.2.2.2. Regression Model Types

13.2.2.2.1. Linear Regression

13.2.2.2.2. Decision Trees for Regression

13.2.2.2.3. Random Forest for Regression

13.2.3. Reinforcement models

13.2.3.1. Performance Metrics

13.2.3.2. Neural Networks

13.2.3.2.1. Architectures

13.2.4. Ensemble modeling

13.2.4.1. Definition

13.2.4.1.1. Ensembling is a technique of combining two or more algorithms of similar or dissimilar types called base learners

13.2.4.2. Types

13.2.4.2.1. Averaging:

13.2.4.2.2. Majority vote:

13.2.4.2.3. Weighted average:

13.2.4.3. Methods

13.2.4.3.1. Bagging

13.2.4.3.2. Boosting

13.2.4.3.3. Stacking

13.2.4.4. Advantages/Disadvantages of ensembling

13.2.4.4.1. Advantages

13.2.4.4.2. Disadvantages

### 13.3. Unsupervised (descriptive models)

13.3.1. Clustering

13.3.1.1. KNN

13.3.1.1.1. Process

13.3.1.2. DBscan

13.3.1.3. Auto-encoders (Neural Nets)

13.3.2. Dimensionality reduction

13.3.2.1. PCA

13.3.2.1.1. Reduce the dimensions of a dataset by finding a plane between similar variables than can be used to express the original variables in a lower-dimensional space

## 14. Anomaly detection

### 14.1. https://raw.githubusercontent.com/ritchieng/machine-learning-stanford/master/w9_anomaly_recommender/anomaly_detection8.png

### 14.2. Can be an unsupervised problem (looking for points with high p(x) standard deviation away from the mean of many of the features), from but mostly setup as a supervised problem with a training set with labels of anomalies

14.2.1. Premise

14.2.1.1. Premise: assume features follow normal distribution. Find the u, sd & p(x) for each feature and use this to create new derived p(x) features. Then use these to predict anomalies

14.2.2. Process

14.2.2.1. Create a 'good training' set with 60% of all non-anomaly (y=0) examples and use this to create p(x) derived features from each of the original features (see formula p(x) below).

14.2.2.1.1. If you complete this process and still find anomaly y=1 samples which are not detected then it is a good idea to look into these specific example to see if there are new derived features that can be create to help detect it

14.2.2.2. Put the remaining 20% of non-anomalously records with 50% of the anomalously records (y=1) into a training set, and the last 20% of non-anomalously and last 50% of anomalously records into a test set

14.2.2.3. Use 'good training' set to create the p(x) derived features, use the training set to predict y=0 good, y=1 anomaly, and optimize the model, then finally use test set to do cross-validation performance

14.2.2.4. We can then use standard supervised performance metrics to evaluate the model - though due to imbalanced classes must use a more robust metric (like F1) rather than accuracy!

14.2.3. Pros (supervised / anomaly detection)

14.2.3.1. AD preferable when we have a very small set of positive (y=1) examples (as we want to save this just for training and test set and can 'expend' many y=0 examples to fit the p(x) model)

14.2.3.2. When anomalies may follow many different 'patterns' so fitting a standard supervised model may not be able to find a good separation boundary, but the pattern of their probability distribution (i.e. the fact they are very different from normal) will be a constant pattern

14.2.4. Examples

14.2.4.1. Spam detection

14.2.4.2. Manufacturing checks

14.2.4.3. Machine/data monitoring

14.2.5. Formula for p(x)

14.2.5.1. Using set of y=0 data points create new derived features which model the original features as a normal distribution and calculate the sample mean, sd, and p(x) as new derived features

14.2.5.1.1. https://yyqing.me/2017/2017-08-09/anomaly-detection.png

14.2.5.2. Assumes features are Normally distributed (x~(u,s2)

14.2.5.2.1. To check this assumption more-or-less holds true it is highly recommended to graph the features first

14.2.5.2.2. Even if this does not hold true AD algorithms generally work OK

14.2.6. Multivariate Gaussian Distribution (AD)

14.2.6.1. Premise

14.2.6.1.1. Standard AD uses single-variance Gaussian distribution - essentially creating a circle radius of p(x) around the mean. However often it may be better to have a more complex shape around the mean - to do this we simply use a multi-var gaussian formula to calculate p(x)

14.2.6.2. Formula

14.2.6.2.1. https://notesonml.files.wordpress.com/2015/06/ml51.png

14.2.6.3. Advantages

14.2.6.4. Disadvantages

## 15. Recommendation engines

### 15.1. Content based

### 15.2. Collaborative filter

## 16. Big Data

### 16.1. Big Data technologies

16.1.1. Hadoop

16.1.2. Spark

### 16.2. ML on large datasets

16.2.1. Gradient descent

16.2.1.1. Stochastic gradient descent

## 17. Labeling Data

### 17.1. Manual Labeling

17.1.1. Calculate approximate time it would take (e.g. 10s to label one, ergo...)

### 17.2. Crowd Source

17.2.1. E.g. Amazon Mechanical Turk / Chiron

### 17.3. Synthetic Labeling

17.3.1. Introducing distortions to smaller training set to amplify it (but only if distortions are what we would expect to find in real training set not just random noise)