Python Automation and Machine Learning for EM and ICs

Batch Gradient Descent (BGD), Stochastic Gradient Descent (SGD), Mini-Batch Gradient Descent, Batch Stochastic Gradient Descent, Momentum, (Adagrad, Adadelta, RMSprop), and Adam (Adaptive Moment Estimation)

Introduction

Neural network vs. end-to-end learning vs. black box model

Introduction

AdaBoost (Adaptive Boosting) Model

Introduction

Boosting versus Bagging

Introduction

Boosting in ML

Introduction

Regular decision trees and decision trees with bagging

Introduction

Bagging in decision trees

Introduction

Bagging (Bootstrap Aggregating)

Introduction

Gradient Boosting

Introduction

Blackbox optimization algorithms

Introduction

Comparison among Grid Search, Bayesian Optimization, Random Search and Manual Search

Introduction

Bayesian optimization

Introduction

Batch sizes

Introduction

Bayes error/Bayes risk/Bayes rate/irreducible error

Introduction

Frequentist approach versus Bayesian approach

Introduction

Bias and variance, and bias-variance trade-off in ML

Introduction

Comparison among classifier, hyperplane and decision boundary

Introduction

Hyperplane/Decision Boundary in ML

Introduction

Multivariate Bernoulli learning model

Introduction

Bernoulli distribution

Introduction

Exponential Family: Parameter, Sufficient Statistic, Natural Parameter, Base Measure and Log-Partition Function (Bernoulli distribution and Gaussian distribution)

Introduction

Bayesian Probability, Bayesian Statistics (Distribution Over a Distribution), versus Bayesian Inference

Introduction

Bandwidth parameter (τ) in LWR and KDE

Introduction

Batch gradient descent

Introduction

Practice ML projects for beginners

Introduction

Binary trees

Introduction

Big O notation

Introduction

Point-Biserial Correlation

Introduction

Brute force discretization

Introduction

Union Bound/Boole's Inequality

Introduction

Deviation Probability (Hoeffding Bound)

Introduction

Sample Size versus Bounds

Introduction

Probability bounds analysis (PBA)

Introduction

Bound in math/ML

Introduction

Similarity-based clustering method (SCM)

Introduction

Send a variable from one script (back) to another script with a function

Introduction

BERTScore/BERT (Bidirectional Encoder Representations from Transformer)

Introduction

Built-ins/Builtins Commands in Python

Introduction

Build own/customized keyword candidates

Introduction

Overfitting and underfitting

Introduction

Create a Batch File to Run a Python Script

Introduction

pyodbc for bridging SQL to Python

Introduction

.bat (batch) files for Command Prompt Windows

Introduction

Automatically restart script execution after it breaks/fails/error

Introduction

Plot graph/figure/image from CSV file/DataFrame by removing/hiding blank/empty cells with axis range (plt.xlim())

Introduction

Plot confidence bands

Introduction

Compare dates (x days after or before a date), and difference between two dates in days

Introduction

BaseException

Introduction

Extract substrings between brackets (including brackets)

Introduction

Get username and encoded password with getpass or or base64

Introduction

(Single) Naive Bayes/Gaussian Naive Bayes

Introduction

Bayes' theorem (Bayes rule or Bayes law) in machine learning

Introduction

Multinomial Naive Bayes algorithm

Introduction

Random (Bootstrap) Forests

Introduction

Box and Whisker plots

Introduction

Built-in functions in Python

Introduction

Add letter/commas/numbers/characters to the end/beginning of strings in a list

Introduction

Replace the lines between two lines “xx” and “yy” in a text file with new lines

Introduction

Remove string 0s from the back/end of a list until non-zero values

Introduction

borderwidth=

(code)

Automated Conversion of Text-Based Labels into Chrome-Compatible Bookmarks

break a functions to exit a loop

Stop the loop, e.g. while loop immediately. Introduction. Normally do not use it, but a break statement is needed if it’s possible to end a loop w/o the condition being false. code1, code2

.bold

(code)

bool()

Applies the standard truth testing procedure to the passed object/value and returns a boolean value, that is, converting a value to Boolean (True or False). The object will always return True, unless: the object is empty, like [], (), {}; the object is False; the object is 0; the object is None. code. (code).

__bool__()

If for any object x, the __bool__() method is not defined, we get True as a result.

watchdog.utils.bricks.SkipRepeatsQueue

Thread-safe event queue based on a special queue that skips adding the same event (FileSystemEvent) multiple times consecutively. Thus avoiding dispatching multiple event handling calls when multiple identical events are produced quicker than an observer can consume them.

watchdog.utils.BaseThread

Producer thread base class subclassed by event emitters that generate events and populate a queue with them.
Parameters: event_queue (watchdog.events.EventQueue) – The event queue to populate with generated events.
watch (ObservedWatch) – The watch to observe and produce events for.
timeout (float) – Timeout (in seconds) between successive attempts at reading events.

watchdog.utils.BaseThread

Consumer thread base class subclassed by event observer threads that dispatch events from an event queue to appropriate event handlers.
Parameters: timeout (float) – Event queue blocking timeout (in seconds).

class watchdog.observers.api.BaseObserver(emitter_class, timeout=1)

watchdog.observers.api.BaseObserver

Platform-independent observer that polls a directory to detect file system changes.

watchdog.observers.api.BaseObserver

File system independent observer that polls a directory to detect changes.

class watchdog.utils.BaseThread

__bool__

bit_length

Button()

Introduction

scipy.linalg.block_diag

Create a block diagonal matrix from the provided arrays.

Binary I/O, io.BytesIO

f = open("myfile.jpg", "rb"), f = io.BytesIO(b"some initial binary data: \x00\x01"). Code.

skimage.measure.block_reduce(image, block_size)

Down-sample image by applying function to local blocks.

BeautifulSoup

Introduction. Has an excellent XML- and HTML- parsing library for beginners, but it may be a bit slow.

Bob

Is developed at Idiap Research Institute in Switzerland, Bob is a free signal processing and machine learning toolbox. The toolbox is written in a mix of Python and C++. From image recognition to image and video processing using machine learning algorithms, a large number of packages are available in Bob to make all of this happen with great efficiency in a short time.

Bokeh

A Data Visualisation library for Python, Bokeh allows interactive visualisation. It makes use of HTML and Javascript to provide graphics, making it reliable for contributing web-based applications. It is highly flexible and allows you to convert visualisation written in other libraries such as ggplot or matplotlib. Bokeh makes use of straight-forward commands to create composite statistical scenarios.

bokeh.plotting.figure.circle()

v2.rectangle(image, start_point, end_point, color of border line, border thickness)

border. Compute the bounding box of the contour and then draw the bounding box on an image to represent where the ROI is. code. code. code.

.backward()

(code)

t.begin_fill()

To be called just before drawing a shape to be filled. (code)

from pynput.mouse import Button

(code)

.press(Button.left)

(code)

.release(Button.left)