MLA 021 Databricks

FromMachine Learning Guide

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MLA 021 Databricks

FromMachine Learning Guide

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Length:

26 minutes

Released:

Jun 22, 2022

Format:

Podcast episode

Description

Discussing Databricks with Ming Chang from Raybeam (part of DEPT®)

Released:

Jun 22, 2022

Format:

Podcast episode

Titles in the series (48)

Teaches the high level fundamentals of machine learning and artificial intelligence. I teach basic intuition, algorithms, and math. I discuss languages and frameworks, deep learning, and more. Audio may seem inferior, but it's a great supplement during exercise/commute/chores. Where your other resources provide the machine learning trees, I provide the forest. Consider me your syllabus. At the end of every episode I provide high-quality curated resources for learning each episode’s details.

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001 Introduction: Teaches the high level fundamentals of machine learning and artificial intelligence. I teach basic intuition, algorithms, and math. I discuss languages and frameworks, deep learning, and more. ocdevel.com/mlg/1 for notes and resources by Machine Learning Guide
9 min listen
005 Linear Regression: Introduction to the first machine-learning algorithm, the 'hello world' of supervised learning - Linear Regression ocdevel.com/mlg/5 for notes and resources by Machine Learning Guide
34 min listen
012 Shallow Algos 1: Speed-run of some shallow algorithms: K Nearest Neighbors (KNN); K-means; Apriori; PCA; Decision Trees ocdevel.com/mlg/12 for notes and resources by Machine Learning Guide
53 min listen
003 Inspiration: Why should you care about AI? Inspirational topics about economic revolution, the singularity, consciousness, and fear. ocdevel.com/mlg/3 for notes and resources by Machine Learning Guide
18 min listen
MLG 002 What is AI, ML, DS: Show notes at . What is artificial intelligence and machine learning? What's the difference? How about compared to statistics and data science? AI history. by Machine Learning Guide
64 min listen
006 Certificates & Degrees: Discussion on certificates and degrees from Udacity to a Masters degree. ocdevel.com/mlg/6 for notes and resources by Machine Learning Guide
16 min listen
007 Logistic Regression: Your first classifier: Logistic Regression. That plus Linear Regression, and you're a 101 supervised learner! ocdevel.com/mlg/7 for notes and resources by Machine Learning Guide
34 min listen
010 Languages & Frameworks: Languages & frameworks comparison. Languages: Python, R, MATLAB/Octave, Julia, Java/Scala, C/C++. Frameworks: Hadoop/Spark, Deeplearning4J, Theano, Torch, TensorFlow. ocdevel.com/mlg/10 for notes and resources by Machine Learning Guide
44 min listen
018 Natural Language Processing 1: Introduction to Natural Language Processing (NLP) topics. ocdevel.com/mlg/18 for notes and resources by Machine Learning Guide
58 min listen
015 Performance: Performance evaluation & improvement. ocdevel.com/mlg/15 for notes and resources by Machine Learning Guide
41 min listen
020 Natural Language Processing 3: Natural Language Processing classical/shallow algorithms. ocdevel.com/mlg/20 for notes and resources by Machine Learning Guide
41 min listen
017 Checkpoint: Checkpoint - learn the material offline! ocdevel.com/mlg/17 for notes and resources by Machine Learning Guide
8 min listen
008 Math: Introduction to the branches of mathematics used in machine learning. Linear algebra, statistics, calculus. ocdevel.com/mlg/8 for notes and resources by Machine Learning Guide
27 min listen
004 Algorithms - Intuition: Overview of machine learning algorithms. Infer/predict, error/loss, train/learn. Supervised, unsupervised, reinforcement learning. ocdevel.com/mlg/4 for notes and resources by Machine Learning Guide
23 min listen
023 Deep NLP 2: RNN review, bi-directional RNNs, LSTM & GRU cells. ocdevel.com/mlg/23 for notes and resources by Machine Learning Guide
43 min listen
014 Shallow Algos 3: Speed run of Anomaly Detection, Recommenders(Content Filtering vs Collaborative Filtering), and Markov Chain Monte Carlo (MCMC). ocdevel.com/mlg/14 for notes and resources by Machine Learning Guide
48 min listen
009 Deep Learning: Deep learning and neural networks. How to stack our logisitic regression units into a multi-layer perceptron. ocdevel.com/mlg/9 for notes and resources by Machine Learning Guide
51 min listen
016 Consciousness: Can AI be conscious? ocdevel.com/mlg/16 for notes and resources by Machine Learning Guide
74 min listen
024 Tech Stack: TensorFlow, Pandas, Numpy, Scikit-Learn, Keras, TensorForce. ocdevel.com/mlg/24 for notes and resources by Machine Learning Guide
61 min listen
013 Shallow Algos 2: Speed run of Support Vector Machines (SVMs) and Naive Bayes Classifier. ocdevel.com/mlg/13 for notes and resources by Machine Learning Guide
55 min listen
019 Natural Language Processing 2: Natural Language Processing classical/shallow algorithms. ocdevel.com/mlg/19 for notes and resources by Machine Learning Guide
66 min listen
025 Convolutional Neural Networks: Convnets or CNNs. Filters, feature maps, window/stride/padding, max-pooling. ocdevel.com/mlg/25 for notes and resources by Machine Learning Guide
44 min listen
027 Hyperparameters 1: Hyperparameters part 1: network architecture. ocdevel.com/mlg/27 for notes and resources by Machine Learning Guide
47 min listen
MLA 001 Certificates & Degrees: Reboot on the MLG episode, with more confident recommends. by Machine Learning Guide
11 min listen
MLA 002 Numpy & Pandas: Some numerical data nitty-gritty in Python. by Machine Learning Guide
17 min listen
029 Reinforcement Learning Intro: Introduction to reinforcement learning concepts. ocdevel.com/mlg/29 for notes and resources. by Machine Learning Guide
43 min listen
026 Project Bitcoin Trader: Community project & intro to Bitcoin/crypto + trading. ocdevel.com/mlg/26 for notes and resources by Machine Learning Guide
38 min listen
022 Deep NLP 1: Recurrent Neural Networks (RNNs) and Word2Vec. ocdevel.com/mlg/22 for notes and resources by Machine Learning Guide
49 min listen
MLA 010 NLP packages: transformers, spaCy, Gensim, NLTK: NLTK: swiss army knife. Gensim: LDA topic modeling, n-grams. spaCy: linguistics. transformers: high-level business NLP tasks. by Machine Learning Guide
26 min listen
MLA 006 Salary: Salary based on location, gender, age, tech... from O'Reilly. by Machine Learning Guide
19 min listen

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MLA 015 SageMaker 1: Part 1 of deploying your ML models to the cloud with SageMaker (MLOps) MLOps is deploying your ML models to the cloud. See for an overview of tooling (also generally a great ML educational run-down.) And I forgot to... by Machine Learning Guide
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MLA 014 Machine Learning Server: Server-side ML. Training & hosting for inference, with a goal towards serverless. AWS SageMaker, Batch, Lambda, EFS, Cortex.dev by Machine Learning Guide
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MLA 013 Customer Facing Tech Stack: Client, server, database, etc. by Machine Learning Guide
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MLA 012 Docker: Use Docker for env setup on localhost & cloud deployment, instead of pyenv / Anaconda. I recommend Windows for your desktop. by Machine Learning Guide
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032 Cartesian Similarity Metrics: L1/L2 norm, Manhattan, Euclidean, cosine distances, dot product by Machine Learning Guide
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MLA 009 Charting tools: matplotlib, Seaborn, Bokeh, D3, Tableau, Power BI, QlikView, Excel by Machine Learning Guide
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MLA 008 Exploratory Data Analysis: EDA + charting. DataFrame info/describe, imputing strategies. Useful charts like histograms and correlation matrices. by Machine Learning Guide
24 min listen
MLA 007 Jupyter Notebooks: Run your code + visualizations in the browser: iPython / Jupyter Notebooks. by Machine Learning Guide
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MLA 006 Salary: Salary based on location, gender, age, tech... from O'Reilly. by Machine Learning Guide
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MLA 005 Shapes & Sizes: Dimensions, size, and shape of Numpy ndarrays / TensorFlow tensors, and methods for transforming those. by Machine Learning Guide
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MLA 001 Certificates & Degrees: Reboot on the MLG episode, with more confident recommends. by Machine Learning Guide
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MLA 002 Numpy & Pandas: Some numerical data nitty-gritty in Python. by Machine Learning Guide
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MLA 003 Storage: HDF, Pickle, Postgres: Comparison of different data storage options when working with your ML models. by Machine Learning Guide
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029 Reinforcement Learning Intro: Introduction to reinforcement learning concepts. ocdevel.com/mlg/29 for notes and resources. by Machine Learning Guide
43 min listen
028 Hyperparameters 2: Hyperparameters part 2: hyper-search, regularization, SGD optimizers, scaling. ocdevel.com/mlg/28 for notes and resources by Machine Learning Guide
50 min listen
027 Hyperparameters 1: Hyperparameters part 1: network architecture. ocdevel.com/mlg/27 for notes and resources by Machine Learning Guide
47 min listen
026 Project Bitcoin Trader: Community project & intro to Bitcoin/crypto + trading. ocdevel.com/mlg/26 for notes and resources by Machine Learning Guide
38 min listen
025 Convolutional Neural Networks: Convnets or CNNs. Filters, feature maps, window/stride/padding, max-pooling. ocdevel.com/mlg/25 for notes and resources by Machine Learning Guide
44 min listen
024 Tech Stack: TensorFlow, Pandas, Numpy, Scikit-Learn, Keras, TensorForce. ocdevel.com/mlg/24 for notes and resources by Machine Learning Guide
61 min listen
023 Deep NLP 2: RNN review, bi-directional RNNs, LSTM & GRU cells. ocdevel.com/mlg/23 for notes and resources by Machine Learning Guide
43 min listen

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MLA 021 Databricks

MLA 021 Databricks

Description

Titles in the series (48)

More Episodes from Machine Learning Guide

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