Real World Machine Learning Video Edition

Real World Machine Learning Video Edition

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 7h 02m | 1.38 GB

Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. It will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you’ll build skills in data acquisition and modeling, classification, and regression. You’ll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you’re done, you’ll be ready to successfully build, deploy, and maintain your own powerful ML systems.

Machine learning systems help you find valuable insights and patterns in data, which you’d never recognize with traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior, and make fact-based recommendations. It’s a hot and growing field, and up-to-speed ML developers are in demand.
Inside:

  • Predicting future behavior
  • Performance evaluation and optimization
  • Analyzing sentiment and making recommendations

No prior machine learning experience assumed. Learners should know Python.

Henrik Brink, Joseph Richards, and Mark Fetherolf are experienced data scientists engaged in the daily practice of machine learning.

A comprehensive guide on how to prepare data for ML and how to choose the appropriate algorithms.
Michael Lund, iCodeIT

Very approachable. Great information on data preparation and feature engineering, which are typically ignored.
Robert Diana, RSI Content Solutions

Table of Contents

01 What is machine learning
02 Boosting model performance with advanced techniques
03 Using data to make decisions
04 The machine-learning approach
05 Five advantages to machine learning
06 Following the ML workflow – from data to deployment
07 Real-world data
08 Which features should be included
09 How much training data is required
10 Preprocessing the data for modeling
11 Simple feature engineering
12 Using data visualization
13 Density plots
14 Modeling and prediction
15 Finding the relationship between input and target
16 Classification – predicting into buckets
17 Classifying complex, nonlinear data
18 Regression – predicting numerical values
19 Summary
20 Model evaluation and optimization
21 The solution – cross-validation
22 Evaluation of classification models
23 Accuracy trade-offs and ROC curves
24 Evaluation of regression models
25 Model optimization through parameter tuning
26 Summary
27 Basic feature engineering
28 Basic feature-engineering processes
29 Feature selection
30 Forward selection and backward elimination
31 Summary
32 Example – NYC taxi data
33 Defining the problem and preparing the data
34 Modeling
35 Summary
36 Advanced feature engineering
37 Topic modeling
38 Content expansion
39 Image features
40 Extracting objects and shapes
41 Time-series features
42 Classical time-series features
43 Summary
44 Advanced NLP example – movie review sentiment
45 So what’s the use case
46 Extracting basic NLP features and building the initial model
47 Normalizing bag-of-words features with the tf-idf algorithm
48 Advanced algorithms and model deployment considerations
49 Scaling machine-learning workflows
50 Subsampling training data in lieu of scaling
51 Scaling ML modeling pipelines
52 Scaling predictions
53 Summary
54 Example – digital display advertising
55 Feature engineering and modeling strategy
56 Singular value decomposition
57 Modeling
58 Summary