What are the things I learned about Data Science in 2018? Here we go:

The difference between Data Science, Machine Learning, Deep Learning and AI

A picture says more than a thousand words.

The difference between supervised and unsupervised learning

Supervised Learning

You have training and test data with labels. Labels tell You to which e.g. class a certain data item belongs. Image you have images of pets and the labels are the name of the pets.

Unsupervised Learning

Your data doesn’t have labels. Your algorithm e.g. k-means clustering need to figure out a structure given only the data

The areas of applied machine learning

are described here: The Essence of Machine Learning and Data Science Overview

Bayes Theorem

In my article Bayes theorem I elaborated about the base rate fallacy and in naive bayes I recapped the second lesson from udacity’s UD120 Intro to Machine Learning

Precision and Recall and ROC

In my article classification: precision and recall I wrote about different useful measures to evaluate the quality of a supervised learning algorithm.

In Receiver Operating Characteristic I wrote about another useful measures the ROC.

Visualization with matplotlib

Matplotlib is a really good starting point for visualization. I wrote about it in Introduction to matplotlib, Matplotlib – Part 2, Scatterplot with matplotlib

Math with numpy

I wrote some articles about the usage of numpy but only scraped the surface of this mighty library

• Linear Algebra with numpy – Part 1
• numpy random choice
• Numpy linspace function

Image manipulation with OpenCV

Intro to OpenCV with Python

JuPyter Notebooks

Sometimes I love them sometimes I hate them. I wrote an Introduction to JuPyter Notebook

Podcasts

In 2018 I’ve listened to a bunch of great podcasts on iTunes:

• Linear digressions
• MIT Lex Fridman
• Dr. Lance Eliot

The post 10 things I didn’t know about Data Science a year ago appeared first on Creatronix.

Feature Scaling is an important pre-processing step for some machine learning algorithms.

Imagine you have three friends of whom you know the individual weight and height.

You would like to deduce Christian’s  t-shirt size from David’s and Julia’s by looking at the height and weight.

One way You could determine the shirt size is to just add up the weight and the height of each friend. You would get:

Because Christian’s height + weight number is nearer to Julia’s number than to David’s, Christian should wear a small T-Shirt. What?

Feature Scaling Formula

x’ = (x – x_min) / (x_max – x_min)

If we look at the combined scaled properties we see that Christian’s value now is closer to David’s so we deduce that Christian shall wear a large shirt as well.

Implementing feature scaling in python

As a little coding practice we can implement a feature scaling algorithm in Python:

def feature_scaling(arr):
    ret_arr = []
    min_val = min(arr)
    max_val = max(arr)
    if min_val == max_val:
        raise ZeroDivisionError()
    for f in arr:
        f = (f - min_val) / float((max_val - min_val))
        ret_arr.append(f)
    return ret_arr

MinMaxScaler from sklearn

Instead of writing our own feature scaler we can we should use the MinMaxScaler from sklearn. It works with numpy arrays by default.

from sklearn.preprocessing import MinMaxScaler
import numpy as np

weights = np.array([[52.0], [79.0], [64.0]])
scaler = MinMaxScaler()
rescaled_weight = scaler.fit_transform(weights)
print(rescaled_weight)

Affected Algorithms

Which algorithms are affected by non-properly scaled features?

SVM and k-means are algorithms which are affected. SVM for example calculates distances and when two features differ dramatically in value range, the feature with the greater range will dominate the other. (As seen when adding kilograms and meters)

The post Feature Scaling appeared first on Creatronix.

Mini project

For the mini project you should fork https://github.com/udacity/ud120-projects and clone it. It is recommended to install a python 2.7 64bit version because ML is heavy data processing and can easily rip up more than 2GB of memory.

Dependecies

After cloning the repo I would recommend setting up a venv and install the requirements:

• sklearn
• numpy
• scipy
• matplotlib

The Code

The code itself is pretty straightforward:

• Instantiate the classifier
• Train (fit) the Classifier
• Predict
• Calculate accuracy

# training
print("Start training")
t0 = time()
clf = GaussianNB()
clf.fit(features_train, labels_train)
print("training time:", round(time() - t0, 3), "s")

# prediction
print("start predicting")
t0 = time()
prediction = clf.predict(features_test)
print("predict time:", round(time() - t0, 3), "s")

# accuracy
print("Calculating accuracy")
accuracy = accuracy_score(labels_test, prediction)
print("Accuracy calculated, and the accuracy is", accuracy)

The output on my machine:

training time: 1.762 s
start predicting
predict time: 0.286 s
Calculating accuracy
Accuracy calculated, and the accuracy is 0.9732650739476678

The simple Gaussian Naive Bayes is pretty accurate with 97.3%

The post Lesson 2: Naive Bayes appeared first on Creatronix.

Data Science tries to answer one of the following questions:

• Classification -> “Is it A or B?”
• Clustering -> “Are there groups which belong together?”
• Regression -> “How will it develop in the future?”
• Association -> “What is happening very often together?”

There are two ways to tackle these problem domains with machine learning:

1. Supervised Learning
2. Unsupervised Learning

Supervised Learning

You have training and test data with labels. Labels tell You to which e.g. class a certain data item belongs. Image you have images of pets and the labels are the name of the pets.

Unsupervised Learning

Your data doesn’t have labels. Your algorithm e.g. k-means clustering need to figure out a structure given only the data

The post Data Science Overview appeared first on Creatronix.

As an engineer I like to put some structure to the chaos. Inspired by Roadmap: How to Learn Machine Learning in 6 Months and Tetiana Ivanova – How to become a Data Scientist in 6 months a hacker’s approach to career planning I build my own learning road map for this year:
So 2018 will be all about Data Science. Hearing about the Personal Knowledge Mastery concept at SWEC17 I am going to tackle the learning process on different levels.

Watch the Pros

Thanks to open course ware there are a ton of awesome university courses online e.g.:

MIT 6.0002 Introduction to Computational Thinking and Data Science

Learn the tools

There is already a whole bunch of tools we can consider belonging to a standard data science stack. Because my main language is Python the focus is of course on mostly python modules.

• JuPyter Notebook
• numpy
• pandas
• seaborn
• bokeh
• holoviews
• scikit-learn
• keras / TensorFlow
• Tableau

Finishing Udacity / Udemy courses

To brush up my python skills and my knowledge of basic computer science I will finish some already started online courses:

• • [  ] Introduction to Machine Learning
  • [  ] Python Bootcamp
  • [  ] Algorithms and Data Structures
  • [  ] Introduction to Artificial Intelligence
  • [  ] Introduction to computer vision
  • [  ] Artificial Intelligence for Robotics

Reading data science books

To get a broad overview I bought two books on DS / ML

• [  ] Data Science from Scratch
• [  ] Hands on Machine Learning

Do Exercises on Kaggle

• [x] Create Account at Kaggle
• [  ] Do first exercise
• [  ] Participate in a contest

Visit Meetups about Data Science

[  ] Visit Big Data Meetup Events

Add some Peer Pressure

My brother in law and I teemed up and build a Whatsapp learn & exchange group. We are currently four members.

Write Blog Articles

I will try to incorporate some of the stuff I’ve learned into blog articles.

I already did

• Bayes’ Theorem Part 1
• Data Science Overview
• Classification: Precision and Recall
• Confusion Matrix
• UD120 Intro to Machine Learning
• Lesson 2: Naive Bayes
• Lesson 3: Support Vector Machines

So stay tuned!

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