Coding for #AI and #machinelearning – online course/workshop

Coding for #AI and #machinelearning – online course/workshop

We are exploring a new way to learning how to code for AI and Machine Learning by applying ideas of  deliberate practise

Starting Oct 2018 – the workshop has limited places

Please contact [email protected] if you want to sign up for our online workshop (300 USD)

 

Deliberate practice is a technique which probably originated in the former Soviet Union to train world class athletes.

Deliberate practise is also used in learning complex skills like playing the violin – which require mastering many small steps and then putting the steps together in a complex whole (like a violin concert)

Deliberate practice always follows the same pattern: break the overall process down into parts, identify your weaknesses, test new strategies for each section, and then integrate your learning into the overall process.

We apply deliberate practise to learning coding for AI

To try this, we work in small sections of code which you should master bit by bit

We divide the code into four sections
1) Pandas, NumPy and MathPlotLib

2) Data manipulation and feature engineering

3) Machine Learning models and validation·

4) Deep learning

Each of these topics is divided into detail (subtopics) as below

(note the code itself is taken from the public domain and from existing books under Apache v2 license)

Pandas, NumPy and MathPlotLib
· initializing NumPy array
· Creating NumPy array
· NumPy datatypes
· Field access
· Basic slicing
· Advanced indexing
· Array math: Sum function, Transpose function
· Broadcasting
· Creating a pandas series
· Creating a pandas dataframe
· Reading / writing data from csv, text, Excel
· Basic statistics on dataframe
· Creating covariance on dataframe
· Creating correlation matrix on dataframe
· Concat or append operation, Merge, join dataframes
· Grouping operation, Pivot tables on Dataframe
· Plots, Bar charts, Pie charts etc for DataFrames

Data manipulation and feature engineering
· Converting categorical variable to numerical
· Normalization and scaling
· Univariate analysis
· Pandas dataframe visualization
· Multivariate analysis
· Correlation matrix
· Pair plot
· Scatter plots
· Find outliers
· Load data
· Normalize data
· Split data into train and test

Machine Learning models and validation·
Linear regression
· Linear regression model accuracy matrices
· Polynomial regression
· Regularization
· Nonlinear regression
· Logistic regression
· Confusion matrix
· Area Under the Curve
· Under-fitting, right-fitting, and over-fitting
· Logistic regression model training and evaluation
· Generalized Linear Model
· Decision tree model
· Support vector machine (SVM) model
· Plotting SVM decision boundaries
· k Nearest Neighbors model
· k-means clustering

Deep Learning·
sklearn perceptron code
· loading MNIST data for training MLP classifier
· sklearn MLP classifier
· Bernoulli RBM with classifier
· grid search with RBM + logistic regression
· Keras MLP
· Compile model
· Train model and evaluate
· dimension reduction using autoencoder
· de-noising using autoencoder
· Keras LSTM
·

Please contact [email protected] if you want to sign up for our online workshop (300 USD)

Image source: Vanessa Mae – Violin prodigy/ player

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