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| Four/five V of big data |
Introduction |
| Evaluating a ML model with BigQuery ML |
Introduction |
| Building an effective machine learning team |
Introduction |
| Principles of ethical and responsible ML (selection bias, confirmation bias, automation bias, model fairness) |
Introduction |
| Best practices for implementing ML in semiconductor manufacturing |
Introduction |
| Identifying the business value of using ML |
Introduction |
| Big data lifecycle |
Introduction |
| XGBoost (Extreme Gradient Boosting) |
Introduction |
| BigQuery ML and Python-based ML frameworks |
Introduction |
| Python libraries for Bayesian ML techniques |
Introduction |
| BigQuery ML |
Introduction |
| Comparison between Naive Bayes algorithms and Bayesian machine learning techniques |
Introduction |
| Biological (human brain) and AI neural networks |
Introduction |
| Bisection search (binary search) |
Introduction |
| Branching |
Introduction |
| Code blocks |
Introduction |
| Mistakes that beginner machine learning (ML) students often make |
Introduction |
| Backtracking search |
Introduction |
| Binary constraint |
Introduction |
| Bayesian ML techniques |
Introduction |
| Knowledge base (repository) |
Introduction |
| Uninformed search/blind search algorithms |
Introduction |
| Breadth-First Search (BFS) |
Introduction |
| Bellman expectation and Bellman optimality equations |
Introduction |
Reinforcement learning
|
Introduction |
| Electroencephalogram cap (EEG cap) for brain |
Introduction |
| Bayesian networks |
Introduction |
| (Forward and backward) propagation equations |
Introduction |
| 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 |
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borderwidth= |
(code) |
| 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) |
(code) |
| .click(Button.left, x) |
x clicks of mouse. (code) |
| .press(Button.right) |
(code) |
| .release(Button.right) |
(code) |
| .bottomright |
(code) |
| .bind() |
Introduction |
| '<Button-1>' |
Single left click. (code) |
| highlightbackground= |
(code) |
| background= |
(code) |
| .buttons= |
(code) |
| .button_pressed() |
(code) |
| .top/.bottom/.left/.right |
Introduction |
| basicConfig |
(code). |
| background_color= |
(code). |
| driver.navigate().back() |
Navigate backward in browser history |
| os.path.basename() |
(code). |
| |
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| Color in table obtained by matplotlib.pyplot/change background color of cells in table |
Introduction |
| Mirror/reflect image from left to right/from top to bottom |
Introduction |
| Plot horizontal stacked bar/histogram |
Introduction |
| Training process in ML (with "best"-option table) |
Introduction |
| Predictive/predict model (with "best"-option table) |
Introduction |
| Median blurring and cv2.medianBlur() |
Introduction |
| .pack(side=LEFT)/.pack(side=RIGHT)/.place(x=, y=) --- position of the buttons |
(Code) |
| Locate/find the center/coordinates of a bright (maximum/highest intensity) spot in an image & find nearest white pixel to a given/specifical pixel location on an binary image |
Introduction |
| find_element(By.XPATH, "") |
Introduction |
| Add padding/black/colored edge to images |
Introduction |
| Reasons (benefits) of automation and how to start |
Introduction |
| Browser-based visualization tool |
Introduction |
| Dummy variables/binary variables |
Introduction |
| Categorical bins |
Introduction |
| tf.feature_column.bucketized_column |
Introduction |
| "Bridges" in Python coding |
Introduction |
| Make selected text bold |
Introduction |
| Open the page of the downloaded list of webpage browser |
Introduction |
| Calculation in an Excel Sheet, Style, Bold, and Color |
Introduction |
| Bring/activate an application/window to most front/foreground |
Introduction |
| Bind/link multiple commands to buttons |
Introduction |
| Bind Python functions and methods to events (similar to if loops) |
Introduction |
Turn on and off with mouse press or a process/button switch
|
Introduction |
| FLAT, RAISED, SUNKEN, GROOVE and RIDGE in Tkinter button relief styles |
Introduction |
| Bag-of-words model |
Introduction |
| Find the best word/text similarity |
Introduction |
| (Single and multiple enter/input) box for pop-up window |
Introduction |
| Calibrate and put a scale bar, and draw a line segment on an image |
Introduction |
| Merge/combine two text files into a new text file, add a new line to the beginning of a text file |
Introduction |
| Build databases with different/uncertain number of members |
Introduction |
| Wheatstone bridge and its simulation |
Introduction. code |
| Open webpages in internet browsers (Chrome, Microsoft edge, IE browser) |
Introduction |
| Set the output image to zero everywhere except my mask (color filter), and display red, green, and blue (RGB) channels of an image. |
code, code. |
| Extract three blue, green, red images from a color image or grey image, or convert RGB (color) image into three blue, green, red images |
code |
Create images with global, adaptive mean, adaptive Gaussian, binary, trunc, Tozero, and tozero thresholds. |
code |
| Bail out/terminate of a loop |
code |
| Find/search birthyear by name |
code |
| Dropdown box/option/selection/choice |
Introduction |
| Check if both files are the same file, e.g. symbolic link, shortcut |
Introduction |
| Invert the contrasts of black and white images |
(Code) |
| watchdog with conditioning break |
Introduction |
| Top (ranking, best, must know) Python libraries/modules |
Introduction |
| Measure length/distance on an image w/o calibrated bar |
Introduction |
| Break/exit/skip a function/code line after a certain time |
Introduction |
| Binary classifiers |
Introduction |
| Generate text file with the bank of collecting all words, characters and strings from news |
Introduction |
| Comparison between Python, Blue Prism, UiPath, Automation Anywhere |
Introduction |
| Binary images |
Introduction |
| Wafer bin map (WBM) |
Introduction |
| Detection procedures/processes of spatial defect patterns (bins) in wafers |
Introduction |
| Smooth images (make image blurry) |
Introduction |
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