-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathbook.html
344 lines (311 loc) · 22.4 KB
/
book.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
<!DOCTYPE HTML>
<html>
<head>
<title>Mukesh Manral - Books & Courses</title>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=no" />
<link rel="stylesheet" href="assets/css/main.css" />
</head>
<body class="is-preload">
<!-- Wrapper -->
<div id="wrapper">
<!-- Main -->
<div id="main">
<div class="inner">
<!-- Header -->
<header id="header">
<a href="index.html" class="logo"><strong>Mukesh Manral</strong>, ex-Data Science Specialist @ Guvi,IITM </a>
<ul class="icons">
<li><a href="https://www.linkedin.com/in/mukesh-manral" class="icon fa-linkedin"><span class="label">Linkedin</span></a></li>
<li><a href="https://github.com/MvMukesh" class="icon fa-github"><span class="label">Github</span></a></li>
<li><a href="https://www.youtube.com/@manralai" class="icon fa-youtube"><span class="label">Youtube</span></a></li>
<li><a href="https://medium.com/@manralai/lists" class="icon fa-medium"><span class="label">Medium</span></a></li>
<li><a href="https://www.linkedin.com/newsletters/6872441622500585472/" class="icon fa-newspaper-o"><span class="label">NewsLetter</span></a></li>
<!--
<li><a href="https://www.facebook.com/" class="icon fa-facebook"><span class="label">Facebook</span></a></li>
<li><a href="https://scholar.google.com/citations?user=" class="icon fa-google"><span class="label">Google</span></a></li>
<li><a href="https://twitter.com/" class="icon fa-twitter"><span class="label">Twitter</span></a></li>
-->
</ul>
</header>
<!-- Content -->
<section>
<header class="main">
<h2>Apply First - Books & Tutorials</h2>
</header>
<h4 id="aiCarrerMaster">Manralai - Ai Career Master </h4>
<p>2022/03/01 - Work in progress</p>
<div class="row">
<div class="col-8 col-12-medium">
<p><strong><u>Manralai-Ai Career Master:</u> Less Theory and more Python Implementation — The apply first lern from experts tutorial book covering all implementations. <br/>Statistics Frameworks 一Problem Solving Frameworks for Data Science 一 Required Python — Data Structures(Strings,Array) and Algorithms in Python 一 Data Analysis & Chart Selection Frameworks 一 Machine Learning Frameworks — Data Preprocessing Pipelines — Feature Engineering Pipelines — Feature Selection Frameworks 一MLOP's — Model in Production — Monitoring and Testing Deployments 一 Deep Learning Basics — TensorFlow2 & PyTorch — Computer Vision Frameworks — Image Classification, Object Detection Frameworks — NLP Frameworks and many more...</strong>
</p>
<p><i>Manralai-Ai Career Master: Less Theory and more Python Implementation</i> is a apply first learn from experts tutorial book for Ai world, with explanation of theory using Python implementations.</p>
<ul>
<li>Theory: Starting from a uniform mathematical framework, this book derives the theory and algorithms for learning Ai, including all major algorithms of Ai world, such as Machine Learning, Computer Vision and NLP algorithms.</li>
<li>Practice: Every chapter is accompanied by high quality implementation based on Python 3, Scikit-learn, TensorFlow and other required libs, fully compatible with Windows, MacOS, and Linux.</li>
</ul>
<h5>Version</h5>
<ul>
<li>First English version of this book is being written by <strong>Mukesh Manral</strong>, and live on git.</li>
</ul>
<ul class="actions">
<li><a href="https://github.com/MvMukesh/Manralai-AiCareerMaster" class="button">Free Version</a></li>
<!-- <li><a href="http://www.amazon.com/TO-BE-UPPDATED" class="button">Buy in Amazon</a></li> -->
</ul>
<h5>Source Codes</h5>
<ul class="actions">
<li><a href="https://github.com/MvMukesh/Manralai-AiCareerMaster" class="button">Source Codes(GitHub)</a></li>
</ul>
</div>
<div class="col-4 col-12-medium">
<span class="image fit">
<img src="images/book/manralai-book.png" alt="book_coveruky" />
<!--<img src="images/book/rlzh2023.jpg" alt="" />
<img src="images/book/rl.jpg" alt="" /> -->
</span>
</div>
</div>
<hr class="major" />
<h4 id="problemSolving">Ai Problem Solving Frameworks: <br/> Cracking complex Business problems into Data Science Problems</h4>
<p>2022/08/01 - Work in progress</p>
<div class="row">
<div class="col-8 col-12-medium">
<p><strong> — The first Ai Problem Solving book for Data Science<br/> Cracking complex Business Problems into Data Science Problems</strong></p>
<p><i>Ai Problem Solving Frameworks</i> is an apply first learn from experts book for Data Science and Ai. It covers the fundamental theory of problem solving in Data Science and Ai, helping companies to achive results leveraging data.</p>
<ul>
<li>Theory: Without preparatory knowledge of advanced mathmatics, this book leverages the core Ai concept to efficiently crack complex business problems.</li>
<li>Practice: This book leads you to solve any complex business problem, for your application in a super easy way.</li>
</ul>
<h5>Version</h5>
<ul>
<li>Simplified English version of this book is being written by <strong>Mukesh Manral</strong>, and live on git.</li>
</ul>
<ul class="actions">
<li><a href="https://github.com/MvMukesh/ProblemSolving-FrameWork-ML" class="button">Free Version</a></li>
</ul>
<h5>Source Codes</h5>
<ul class="actions">
<li><a href="https://github.com/MvMukesh/ProblemSolving-FrameWork-ML" class="button">SOURCE CODES(GITHUB)</a></li>
</ul>
</div>
<div class="col-4 col-12-medium">
<span class="image fit">
<img src="images/book/problemSolving-book.png" alt="book_cover" />
</span>
</div>
</div>
<hr class="major" />
<h4 id="dataPreprocessing">Data Preprocessing Frameworks: <br/> Least discussed Secrets of Machine Learning Projects</h4>
<p>2023/02/10 - Work in progress</p>
<div class="row">
<div class="col-8 col-12-medium">
<p><i>Data Preprocessing Frameworks with apply first learn from experts approach</i> is a guide for beginner to intermediate-level Machine Learning developers looking to take the next leap forward in Ai field: realtime.<br/>
With Realtime Machine Learning pipelines, you'll be able to quickly get up to speed on how Data Preprocessing pielines are being done in industry, how it is going to affect the future projects.<br/>
Only lightly touching on the heavy theory and instead focusing on a practical approach, Realtime Data Preprocessing Frameworks will guide you through building and refining your first ML Project. After your initial Frameworks given in the book, you'll immediately jump into the process of creating some realtime Industry grade Data Preprocessing pipelines.<br/>
The future of the Ai is realtime. Grab your hoverboard.
</p>
<h5>Versions</h5>
<ul>
<li>Simplified English version of this book is being written by <strong>Mukesh Manral</strong>, and live on git.</li>
</ul>
<ul class="actions">
<li><a href="https://github.com/MvMukesh/DataPreprocessing-Framework-ML" class="button">Free Version</a></li>
</ul>
<h5>Source Codes</h5>
<ul class="actions">
<li><a href="https://github.com/MvMukesh/DataPreprocessing-Framework-ML" class="button">SOURCE CODES(GITHUB)</a></li>
</ul>
</div>
<div class="col-4 col-12-medium">
<span class="image fit">
<img src="images/book/preprocess.png" alt="" />
</span>
</div>
</div>
<hr class="major" />
<h4 id="featureEngineering">Feature Engineering Frameworks: <br/>Secrets to Building Better Machine Learning Models</h4>
<p>2023/04/01</p>
<div class="row">
<div class="col-8 col-12-medium">
<p><i>Feature Engineering Frameworks: Secrets to Building Better Machine Learning Models</i> is designed by keeping in mind both new bee and professionals. This text takes a logical approach to the presentation of core points, moving step-by-step from the basics to more advanced approaches, with proper industry grade frameworks being introduced at the appropriate time. The book is divided into three parts:</p>
<ul>
<li><strong>Part One</strong> covers the basics of Features say : Features Type, Features Characteristics, Feature Imputations, Multivariate Imputations, Categorical Features Encoding.</li>
<li><strong>Part Two</strong> introduces to Feature Simple Transformations, Feature Mathematical Transformations, Discretisation, Outliers, Feature Scaling, Mixed Features Operations, DateTime Features.</li>
<li><strong>Part Three</strong> explores key aspects of Text Features, Transactions & Time Series Features, Feature Combinations, Industry Grage End to End Feature Engineering pipelines.</li>
</ul>
<h5>Versions</h5>
<ul>
<li>Simplified English version of this book is being written by <strong>Mukesh Manral</strong>, and live on git.</li>
</ul>
<ul class="actions">
<li><a href="https://github.com/MvMukesh/FeatureEngineering-Framework-ML" class="button">Free Version</a></li>
</ul>
<h5>Source Codes</h5>
<ul class="actions">
<li><a href="https://github.com/MvMukesh/FeatureEngineering-Framework-ML" class="button">Source Codes(GitHub)</a></li>
</ul>
</div>
<div class="col-4 col-12-medium">
<span class="image fit">
<img src="images/book/features-book.png" alt="" />
</span>
</div>
</div>
<hr class="major" />
<h4 id="featureSelection">Feature Selection Frameworks: <br/>Secrets to Building Better Machine Learning Models</h4>
<p>2023/06/18</p>
<div class="row">
<div class="col-8 col-12-medium">
<p><i>Feature Selection Frameworks: Guide to Boosting Machine Learning Model Performance with Images</i> is designed by keeping in mind both new bee and professionals. This text takes a logical approach to the presentation of core points, moving step-by-step from the basics to more advanced approaches, with proper industry grade frameworks being introduced at the appropriate time. The book is divided into four parts:</p>
<ul>
<li><strong>Part One</strong> covers the Filter Methods say Mutual Information, Chi-Squre Distribution, ANOVA, Method used in KDD competition - 2009, Basic Selection Methods + Statistical Methods Pipelines.</li>
<li><strong>Part Two</strong> covers the Wrapper Methods say Forward Feature Selection, Backward Feature Selection, Exhaustive Feature Selection.</li>
<li><strong>Part Three</strong> covers the Embeddd Methods say Linear Model Coefficients(Logistic-Linear Regression Coefficients), Effect of Regularization on Coefficients, Selection Methods + Correlation + Embedded Pipelines, Selection Methods + Correlation + Tree Importance Pipelines.</li>
<li><strong>Part Four</strong> covers the Hybrid Methods say Feature Shuffling, Recursive Elimination, Recursive Feature Addition.</li>
</ul>
<h5>Versions</h5>
<ul>
<li>Simplified English version of this book is being written by <strong>Mukesh Manral</strong>, and live on git.</li>
</ul>
<ul class="actions">
<li><a href="https://github.com/MvMukesh/FeatureSelection-Framework-ML" class="button">Free Version</a></li>
</ul>
<h5>Source Codes</h5>
<ul class="actions">
<li><a href="https://github.com/MvMukesh/FeatureSelection-Framework-ML" class="button">Source Codes(GitHub)</a></li>
</ul>
</div>
<div class="col-4 col-12-medium">
<span class="image fit">
<img src="images/book/selection-book.png" alt="" />
</span>
</div>
</div>
<hr class="major" />
<!-- New Code for Accord -->
<center><h1>Questions and Answers</h1></center>
<div class="accordion">
<div class="accordion-item">
<div class="accordion-item-header">
<h3>Who specifically, is this course for?</h3>
</div>
<div class="accordion-item-body">
<p><h5>Any professional or soon-to-be who wants to grow their end to end Data Science projects knowledge, get some attention from recruiter, and earn some income.</h5></p>
</div>
</div>
<div class="accordion-item">
<div class="accordion-item-header">
<h3>Why the heck should I listen to you? Who are you?</h3>
</div>
<div class="accordion-item-body">
<p><h5>My name is Mukesh Manral. I'm a ex Data Science Specialist who went from 0 knowledge of data science to Specialist. I have placed more than 30 candidates for free in the same domain with max packages of 13LPA (Tester to Data Science Engineer). I have tested my all Frameworks before, which can help you learn 6-8 month of content in 2-3 months max. Every thing will be Apply First. I will help you end to end, understanding industry grade code to deployments to updating your profiles and helping you crack jobs if required.</h5></p>
</div>
</div>
<div class="accordion-item">
<div class="accordion-item-header">
<h3>What will I learn in this course?</h3>
</div>
<div class="accordion-item-body">
<p><h5>You'll learn very basics to my tried and tested industry grade Pipelines and Frameworks which will help you does not matter you are a new bee or a pro, to deliver client project in Data Analytics, Machine Learning, Computer Vision and NLP.</h5></p>
</div>
</div>
<div class="accordion-item">
<div class="accordion-item-header">
<h3>Ok. Got it. This must be expensive right?</h3>
</div>
<div class="accordion-item-body">
<p><h5>No, It's only ₹1999.</h5></p>
</div>
</div>
<div class="accordion-item">
<div class="accordion-item-header">
<h3>Most Ai courses are like ₹3 Lack or ₹2.5 Lack or ₹80 thousand. What's the deal?</h3>
</div>
<div class="accordion-item-body">
<h5><p>This isn't my primary source of income and my main goal is to give back to the community. How many people can actually afford those courses?.</h5></p>
</div>
</div>
<div class="accordion-item">
<div class="accordion-item-header">
<h3>What's missing in this course that those other courses charge so much for?</h3>
</div>
<div class="accordion-item-body">
<h5><p>You won't be watching any Oscar-winning cinematography and it isn't shot on some big budget. I'm not sitting on a private plane or in some rented beach house.<br>
It's me in my home office, giving you the simple information you need to succeed..</h5></p>
</div>
</div>
<div class="accordion-item">
<div class="accordion-item-header">
<h3>Ok, but still...Is it worth ₹1999?</h3>
</div>
<div class="accordion-item-body">
<h5><p>I spent 200+ hours working on this & providing the best resources and framework, so you don't have to. You'll get instant access to the frameworks to help you grow in Data Science/Ai field. I've designed this course to be actionable and apply first. It's not some book or other course you read/watched once & shelve. It's a apply first video-based course with a system that you can use every day, and revisit frequently.</h5></p>
</div>
</div>
<div class="accordion-item">
<div class="accordion-item-header">
<h3>How long will it take to see results?</h3>
</div>
<div class="accordion-item-body">
<h5><p>It depends on how active and focus you are. If you follow the book's, frameworks, modules expect to see some good traction in 30-60 days, and you can likely update your learning in 3-5 months. If you do absolutely nothing, you won't see any results..</h5></p>
</div>
</div>
<div class="accordion-item">
<div class="accordion-item-header">
<h3>Does the course come with email or call support?</h3>
</div>
<div class="accordion-item-body">
<h5><p>Yes 24/7, Course is designed to be self-guided and self-paced, still we provide 1:1 24/7 support by Chat or Emails</h5></p>
</div>
</div>
<div class="accordion-item">
<div class="accordion-item-header">
<h3>Do I get lifetime access?</h3>
</div>
<div class="accordion-item-body">
<h5><p>Absolutely. You can access it for as long as you'd like with no extra payment. Upcomming updates are free.</h5></p>
</div>
</div>
<div class="accordion-item">
<div class="accordion-item-header">
<h2>Ok. What do I do next if I want to enroll??</h2>
</div>
<div class="accordion-item-body">
<h5><p>Click the "Get Instant Access" button or mail to "mukeshmanral777@gmail.com", and get started today..</h5></p>
</div>
</div>
</div>
</section>
</div>
</div>
<!-- Sidebar -->
<div id="sidebar">
<div class="inner">
<!-- Menu -->
<nav id="menu">
<ul>
<li><a href="index.html">Homepage</a></li>
<li><a href="book.html">Books-Tutorials</a></li>
<li><a href="article.html">Ai (Articles+Letters)</a></li>
<li><a href="honor.html">OpenSource Projects</a></li>
<li><a href="certificate.html">Certificates</a></li>
<li><a href="news.html">Updates</a></li>
</ul>
</nav>
<!-- Footer -->
<footer id="footer">
<p class="copyright">Mukesh Manral © All rights reserved.<br/>
Updated On: <a>29-10-2023</a>.
</p>
</footer>
</div>
</div>
</div>
<!-- Scripts -->
<script src="assets/js/jquery.min.js"></script>
<script src="assets/js/browser.min.js"></script>
<script src="assets/js/breakpoints.min.js"></script>
<script src="assets/js/util.js"></script>
<script src="assets/js/main.js"></script>
</body>
</html>