Machine Learning with Javascript

Name: Machine Learning with Javascript
Price: 9 USD
Availability: InStock

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?

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

Machine Learning with Javascript

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