Machine Learning Guide

Primary clustering tools for practical applications include K-means using scikit-learn or Faiss, agglomerative clustering leveraging cosine similarity with scikit-learn, and density-based methods lik…

The landscape of Python natural language processing tools has evolved from broad libraries like NLTK toward more specialized packages such as Gensim for topic modeling, SpaCy for linguistic analysis,…

Python charting libraries - Matplotlib, Seaborn, and Bokeh - explaining, their strengths from quick EDA to interactive, HTML-exported visualizations, and clarifies where D3.js fits as a JavaScript al…

Exploratory data analysis (EDA) sits at the critical pre-modeling stage of the data science pipeline, focusing on uncovering missing values, detecting outliers, and understanding feature distribution…

Jupyter Notebooks, originally conceived as IPython Notebooks, enable data scientists to combine code, documentation, and visual outputs in an interactive, browser-based environment supporting multipl…

O'Reilly's 2017 Data Science Salary Survey finds that location is the most significant salary determinant for data professionals, with median salaries ranging from $134,000 in California to under $30…

Explains the fundamental differences between tensor dimensions, size, and shape, clarifying frequent misconceptions—such as the distinction between the number of features (“columns”) and true data di…

Practical workflow of loading, cleaning, and storing large datasets for machine learning, moving from ingesting raw CSVs or JSON files with pandas to saving processed datasets and neural network weig…

NumPy enables efficient storage and vectorized computation on large numerical datasets in RAM by leveraging contiguous memory allocation and low-level C/Fortran libraries, drastically reducing memory…

While industry-respected credentials like Udacity Nanodegrees help build a practical portfolio for machine learning job interviews, they remain insufficient stand-alone qualifications—most roles requ…

Notes and resources:  ocdevel.com/mlg/29 

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Reinforcement Learning (RL) is a fundamental component of artificial intelligence, different fro…

Notes and resources:  ocdevel.com/mlg/28 

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More hyperparameters for optimizing neural networks. A focus on regularization, optimizers, fea…

Full notes and resources at  ocdevel.com/mlg/27 

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Hyperparameters are crucial elements in the configuration of machine learning models. Un…

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Ful notes and resources at  ocdevel.com/mlg/26 

NOTE. This episode is no longer relevant, and tforce_btc_trader no longer maintained. The…

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Notes and resources at  ocdevel.com/mlg/25 

• Filters and Feature Maps: Filters are small matrices used to detect visual features from an…

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Notes and resources at  ocdevel.com/mlg/24 

Desktop if you're stationary, as you'll get the best performance bang-for-buck and i…

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Notes and resources at  ocdevel.com/mlg/23 

• Vanilla Neural Networks (Feedforward Networks):

  • Used for gener…

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Notes and resources at  ocdevel.com/mlg/22 

Deep NLP Fundamentals

Deep learning has had a profound impact on natural language processing …

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Notes and resources at  ocdevel.com/mlg/20 

NLP progresses through three main layers: text preprocessing, syntax tools, and high-level go…

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Notes and resources at  ocdevel.com/mlg/19 

Classical NLP Techniques:

• Origins and Phases in NLP History: Initially reliant on hardcoded…