Machine Learning with Javascript

17h 42m 20s
English
Paid
November 22, 2023

If you're here, you already know the truth: Machine Learning is the future of everything. In the coming years, there won't be a single industry in the world untouched by Machine Learning.  A transformative force, you can either choose to understand it now, or lose out on a wave of incredible change.  You probably already use apps many times each day that rely upon Machine Learning techniques.  So why stay in the dark any longer?

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There are many courses on Machine Learning already available.  I built this course to be the best introduction to the topic.  No subject is left untouched, and we never leave any area in the dark.  If you take this course, you will be prepared to enter and understand any sub-discipline in the world of Machine Learning.

A common question - Why Javascript?  I thought ML was all about Python and R?

The answer is simple - ML with Javascript is just plain easier to learn than with Python.  Although it is immensely popular, Python is an 'expressive' language, which is a code-word that means 'a confusing language'.  A single line of Python can contain a tremendous amount of functionality; this is great when you understand the language and the subject matter, but not so much when you're trying to learn a brand new topic.

Besides Javascript making ML easier to understand, it also opens new horizons for apps that you can build.  Rather than being limited to deploying Python code on the server for running your ML code, you can build single-page apps, or even browser extensions that run interesting algorithms, which can give you the possibility of developing a completely novel use case!

Does this course focus on algorithms, or math, or Tensorflow, or what?!?!

Let's be honest - the vast majority of ML courses available online dance around the confusing topics.  They encourage you to use pre-build algorithms and functions that do all the heavy lifting for you.  Although this can lead you to quick successes, in the end it will hamper your ability to understand ML.  You can only understand how to apply ML techniques if you understand the underlying algorithms.

That's the goal of this course - I want you to understand the exact math and programming techniques that are used in the most common ML algorithms.  Once you have this knowledge, you can easily pick up new algorithms on the fly, and build far more interesting projects and applications than other engineers who only understand how to hand data to a magic library.

Don't have a background in math?  That's OK! I take special care to make sure that no lecture gets too far into 'mathy' topics without giving a proper introduction to what is going on.

A short list of what you will learn:

  • Advanced memory profiling to enhance the performance of your algorithms

  • Build apps powered by the powerful Tensorflow JS library

  • Develop programs that work either in the browser or with Node JS

  • Write clean, easy to understand ML code, no one-name variables or confusing functions

  • Pick up the basics of Linear Algebra so you can dramatically speed up your code with matrix-based operations. (Don't worry, I'll make the math easy!)

  • Comprehend how to twist common algorithms to fit your unique use cases

  • Plot the results of your analysis using a custom-build graphing library

  • Learn performance-enhancing strategies that can be applied to any type of Javascript code

  • Data loading techniques, both in the browser and Node JS environments

Requirements:
  • Basic understanding of terminal and command line usage
  • Ability to read basic math equations
Who this course is for:
  • Javascript developers interested in Machine Learning

What you'll learn:

  • Assemble machine learning algorithms from scratch!
  • Build interesting applications using Javascript and ML techniques
  • Understand how ML works without relying on mysterious libraries
  • Optimize your algorithms with advanced performance and memory usage profiling
  • Use the low-level features of Tensorflow JS to supercharge your algorithms
  • Grow a strong intuition of ML best practices

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# Title Duration
1 Getting Started - How to Get Help 00:58
2 Solving Machine Learning Problems 06:05
3 A Complete Walkthrough 09:54
4 App Setup 02:02
5 Problem Outline 02:54
6 Identifying Relevant Data 04:12
7 Dataset Structures 05:48
8 Recording Observation Data 04:00
9 What Type of Problem? 04:36
10 How K-Nearest Neighbor Works 08:24
11 Lodash Review 09:57
12 Implementing KNN 07:17
13 Finishing KNN Implementation 05:54
14 Testing the Algorithm 04:49
15 Interpreting Bad Results 04:13
16 Test and Training Data 04:06
17 Randomizing Test Data 03:49
18 Generalizing KNN 03:42
19 Gauging Accuracy 05:19
20 Printing a Report 03:30
21 Refactoring Accuracy Reporting 05:14
22 Investigating Optimal K Values 11:39
23 Updating KNN for Multiple Features 06:37
24 Multi-Dimensional KNN 03:57
25 N-Dimension Distance 09:51
26 Arbitrary Feature Spaces 08:28
27 Magnitude Offsets in Features 05:37
28 Feature Normalization 07:33
29 Normalization with MinMax 07:15
30 Applying Normalization 04:23
31 Feature Selection with KNN 07:48
32 Objective Feature Picking 06:11
33 Evaluating Different Feature Values 02:54
34 Let's Get Our Bearings 07:28
35 A Plan to Move Forward 04:32
36 Tensor Shape and Dimension 12:05
37 Elementwise Operations 08:19
38 Broadcasting Operations 06:48
39 Logging Tensor Data 03:48
40 Tensor Accessors 05:25
41 Creating Slices of Data 07:47
42 Tensor Concatenation 05:29
43 Summing Values Along an Axis 05:14
44 Massaging Dimensions with ExpandDims 07:48
45 KNN with Regression 04:57
46 A Change in Data Structure 04:05
47 KNN with Tensorflow 09:19
48 Maintaining Order Relationships 06:31
49 Sorting Tensors 08:01
50 Averaging Top Values 07:44
51 Moving to the Editor 03:27
52 Loading CSV Data 10:11
53 Running an Analysis 06:11
54 Reporting Error Percentages 06:27
55 Normalization or Standardization? 07:34
56 Numerical Standardization with Tensorflow 07:38
57 Applying Standardization 04:02
58 Debugging Calculations 08:15
59 What Now? 04:01
60 Linear Regression 02:40
61 Why Linear Regression? 04:53
62 Understanding Gradient Descent 13:05
63 Guessing Coefficients with MSE 10:20
64 Observations Around MSE 05:57
65 Derivatives! 07:13
66 Gradient Descent in Action 11:47
67 Quick Breather and Review 05:47
68 Why a Learning Rate? 17:06
69 Answering Common Questions 03:49
70 Gradient Descent with Multiple Terms 04:44
71 Multiple Terms in Action 10:40
72 Project Overview 06:02
73 Data Loading 05:18
74 Default Algorithm Options 08:33
75 Formulating the Training Loop 03:19
76 Initial Gradient Descent Implementation 09:25
77 Calculating MSE Slopes 06:53
78 Updating Coefficients 03:12
79 Interpreting Results 10:08
80 Matrix Multiplication 07:10
81 More on Matrix Multiplication 06:41
82 Matrix Form of Slope Equations 06:22
83 Simplification with Matrix Multiplication 09:29
84 How it All Works Together! 14:02
85 Refactoring the Linear Regression Class 07:41
86 Refactoring to One Equation 08:59
87 A Few More Changes 06:14
88 Same Results? Or Not? 03:20
89 Calculating Model Accuracy 08:38
90 Implementing Coefficient of Determination 07:45
91 Dealing with Bad Accuracy 07:48
92 Reminder on Standardization 04:37
93 Data Processing in a Helper Method 03:39
94 Reapplying Standardization 05:58
95 Fixing Standardization Issues 05:37
96 Massaging Learning Rates 03:16
97 Moving Towards Multivariate Regression 11:45
98 Refactoring for Multivariate Analysis 07:29
99 Learning Rate Optimization 08:05
100 Recording MSE History 05:22
101 Updating Learning Rate 06:42
102 Observing Changing Learning Rate and MSE 04:18
103 Plotting MSE Values 05:22
104 Plotting MSE History against B Values 04:23
105 Batch and Stochastic Gradient Descent 07:18
106 Refactoring Towards Batch Gradient Descent 05:07
107 Determining Batch Size and Quantity 06:03
108 Iterating Over Batches 07:49
109 Evaluating Batch Gradient Descent Results 05:42
110 Making Predictions with the Model 07:38
111 Introducing Logistic Regression 02:28
112 Logistic Regression in Action 06:32
113 Bad Equation Fits 05:32
114 The Sigmoid Equation 04:32
115 Decision Boundaries 07:48
116 Changes for Logistic Regression 01:12
117 Project Setup for Logistic Regression 05:52
118 Importing Vehicle Data 04:28
119 Encoding Label Values 04:19
120 Updating Linear Regression for Logistic Regression 07:09
121 The Sigmoid Equation with Logistic Regression 04:28
122 A Touch More Refactoring 07:47
123 Gauging Classification Accuracy 03:28
124 Implementing a Test Function 05:17
125 Variable Decision Boundaries 07:17
126 Mean Squared Error vs Cross Entropy 05:47
127 Refactoring with Cross Entropy 05:09
128 Finishing the Cost Refactor 04:37
129 Plotting Changing Cost History 03:25
130 Multinominal Logistic Regression 02:20
131 A Smart Refactor to Multinominal Analysis 05:08
132 A Smarter Refactor! 03:46
133 A Single Instance Approach 09:51
134 Refactoring to Multi-Column Weights 04:40
135 A Problem to Test Multinominal Classification 04:38
136 Classifying Continuous Values 04:42
137 Training a Multinominal Model 06:20
138 Marginal vs Conditional Probability 09:57
139 Sigmoid vs Softmax 06:09
140 Refactoring Sigmoid to Softmax 04:43
141 Implementing Accuracy Gauges 02:37
142 Calculating Accuracy 03:16
143 Handwriting Recognition 02:11
144 Greyscale Values 05:12
145 Many Features 03:30
146 Flattening Image Data 06:07
147 Encoding Label Values 05:45
148 Implementing an Accuracy Gauge 07:27
149 Unchanging Accuracy 01:56
150 Debugging the Calculation Process 08:13
151 Dealing with Zero Variances 06:16
152 Backfilling Variance 02:37
153 Handing Large Datasets 04:15
154 Minimizing Memory Usage 04:51
155 Creating Memory Snapshots 05:15
156 The Javascript Garbage Collector 06:50
157 Shallow vs Retained Memory Usage 05:51
158 Measuring Memory Usage 08:30
159 Releasing References 03:15
160 Measuring Footprint Reduction 03:51
161 Optimization Tensorflow Memory Usage 01:32
162 Tensorflow's Eager Memory Usage 04:41
163 Cleaning up Tensors with Tidy 02:49
164 Implementing TF Tidy 03:32
165 Tidying the Training Loop 03:58
166 Measuring Reduced Memory Usage 01:35
167 One More Optimization 02:36
168 Final Memory Report 02:45
169 Plotting Cost History 04:04
170 NaN in Cost History 04:19
171 Fixing Cost History 04:47
172 Massaging Learning Parameters 01:41
173 Improving Model Accuracy 04:28
174 Loading CSV Files 02:07
175 A Test Dataset 02:01
176 Reading Files from Disk 03:09
177 Splitting into Columns 02:55
178 Dropping Trailing Columns 02:31
179 Parsing Number Values 03:37
180 Custom Value Parsing 04:20
181 Extracting Data Columns 05:36
182 Shuffling Data via Seed Phrase 05:14
183 Splitting Test and Training 07:45

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