-
Notifications
You must be signed in to change notification settings - Fork 0
/
Roadmap.html
177 lines (152 loc) · 50.4 KB
/
Roadmap.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
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" integrity="sha384-xOolHFLEh07PJGoPkLv1IbcEPTNtaed2xpHsD9ESMhqIYd0nLMwNLD69Npy4HI+N" crossorigin="anonymous">
<link rel="stylesheet" href="Roadmap.css">
<title>Roadmap</title>
</head>
<body>
<nav>
<div class="main-nav container flex">
<a id="aa" href="web.html" >Back To Home
</a>
</div>
</nav>
<h3> Roadmap to Master ML </h3>
<div >
<img id="roadmap" src="Roadmap-ml2.jpg" alt="..">
</div>
<h3> Here the free Resources To Master ML...</h3>
<br>
<br>
<div id="resources">
<div class="card" style="width: 18rem;">
<img src="https://i.ytimg.com/vi/ADzhz1Wely8/hqdefault.jpg" class="card-img-top" alt="...">
<div class="card-body">
<h5 class="card-title">Essential Mathematics for Machine Learning</h5>
<p class="card-text">YouTube series like "Essential Mathematics for Machine Learning" teach the core math (linear algebra, calculus, probability, statistics) needed to grasp machine learning algorithms.
This builds a strong foundation for understanding and implementing them effectively.</p>
<a href="https://www.youtube.com/playlist?list=PLLy_2iUCG87D1CXFxE-SxCFZUiJzQ3IvE" class="btn btn-primary">Visit</a>
</div>
</div>
<div class="card" style="width: 18rem;">
<img src="https://digialpsltd.b-cdn.net/wp-content/uploads/2024/04/Feature2.png" class="card-img-top" height="215px" alt="...">
<div class="card-body">
<h5 class="card-title">ML Crash Course with TensorFlow APIs</h5>
<p class="card-text">This free online course teaches machine learning basics and building neural networks with TensorFlow.
Through video lectures, real-world examples, and hands-on exercises, you'll gain practical skills in building and evaluating machine learning models in Python.
</p>
<a href="https://developers.google.com/machine-learning/crash-course" class="btn btn-primary">visit</a>
</div>
</div>
<div class="card" style="width: 18rem;">
<img src="https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRKaSMxsLMT_22utoFXu7ZckTQKgeI7BHOBZg&s" class="card-img-top" height="230px" alt="...">
<div class="card-body">
<h5 class="card-title">Master Python </h5>
<p class="card-text">Duration: Approximately 4 hours.
Content:
Learn Python basics, including variables, lists, and functions.<br>
Explore Python packages like NumPy for data science.<br>
Start your Python journey on DataCamp and gain valuable skills!</p>
<a href="https://www.datacamp.com/courses/intro-to-python-for-data-science" class="btn btn-primary">Visit</a>
</div>
</div>
<div class="card" style="width: 18rem;">
<img src="https://pyimagesearch.com/wp-content/uploads/2019/01/python_ml_header.png" class="card-img-top" height="255px" alt="...">
<div class="card-body">
<h5 class="card-title">Machine Learning Specialization</h5>
<p class="card-text">This webpage is about supervised machine learning. It discusses what supervised machine learning is and gives details of two common algorithms: linear regression and logistic regression.
Some of the important points from this website are the different applications of machine learning and the fact that this course is for beginners with no prior coding experience required.</p>
<a href="https://www.coursera.org/learn/machine-learning" class="btn btn-primary">Visit</a>
</div>
</div>
<div class="card" style="width: 18rem;">
<img src="https://madewithml.com/static/images/logos.png" class="card-img-top" alt="...">
<div class="card-body">
<h5 class="card-title">Learn ML Applications</h5>
<p class="card-text">This a course is about information that teaches people how to build and deploy machine learning applications. It discusses what the course covers, who it is for, and the benefits of taking the course. Some of the important points are that the course covers the entire machine learning process, from design to production,
and that it is designed for people with a variety of backgrounds.
</p>
<a href="https://madewithml.com/ " class="btn btn-primary">Visit</a>
</div>
</div>
<div class="card" style="width: 18rem;">
<img src="https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRrF-Kgbq0e_Wpxp3XtDT7Z8BtPrwPezGmdyA&s" class="card-img-top" height="255px" alt="...">
<div class="card-body">
<h5 class="card-title">Data Preprocessing in Machine learning</h5>
<p class="card-text">This webpage is about data preprocessing in machine learning.<br>
Data preprocessing is the first step in creating a machine learning model.<br>
It involves cleaning and formatting the data.
Data preprocessing is necessary to improve the accuracy of the machine learning model.
you will gain detailed information from this about Data Processing From this page</p>
<a href="https://www.javatpoint.com/data-preprocessing-machine-learning" class="btn btn-primary">Visit</a>
</div>
</div>
<div class="card" style="width: 18rem;">
<img src="https://miro.medium.com/v2/resize:fit:693/1*1ouD8HMkmJffNSAMfvBSkw.png" class="card-img-top" height="200px" alt="...">
<div class="card-body">
<h5 class="card-title">scikit-learn</h5>
<p class="card-text">scikit-learn is an open source machine learning library for Python.
It is built on top of the NumPy and SciPy libraries, and provides a range of algorithms for tasks including classification,
regression, clustering and dimensionality reduction.</p>
<a href="https://scikit-learn.org/stable/" class="btn btn-primary">Visit</a>
</div>
</div>
<div class="card" style="width: 18rem;">
<img src="https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSl1-EEGS-0qKj9Z4kmf6ZXeHg3pvGw594qjQ&s" class="card-img-top" height="200px" alt="...">
<div class="card-body">
<h5 class="card-title">TensorFlow</h5>
<p class="card-text">TensorFlow is an end-to-end platform for machine learning. It can be used to create machine learning models that can run in any environment.
TensorFlow.js is a library that can be used to train and run machine learning models directly in the browser using JavaScript or Node.js.</p>
<a href="https://www.tensorflow.org/" class="btn btn-primary">Visit</a>
</div>
</div>
<div class="card" style="width: 18rem;">
<img src="https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRFyvMzhxmNsDp4FmsQ4guL2Plb8pGEexggiQ&s" class="card-img-top" height="200px" alt="...">
<div class="card-body">
<h5 class="card-title">kaggle</h5>
<p class="card-text">Kaggle is a valuable platform for machine learning enthusiasts. competitions, and discussions to hone your ML skills.
A vast library of public datasets and pre-trained models for project inspiration.A community to share knowledge, learn from others, and participate in competitions.</p>
<a href="https://www.kaggle.com/" class="btn btn-primary">Visit</a>
</div>
</div>
<div class="card" style="width: 18rem;">
<img src="data:image/jpeg;base64,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" class="card-img-top" height="200px" alt="...">
<div class="card-body">
<h5 class="card-title">Machine Learning for everybody</h5>
<p class="card-text"> The “Machine Learning for Everybody – Full Course” by Kylie Ying is a beginner-friendly video course lasting approximately 3 hours and 53 minutes.
It covers fundamental machine learning concepts, practical implementations using TensorFlow, and various algorithms such as k-nearest neighbors, Naive Bayes, and neural networks.
Learners work with real-world datasets and receive a certificate upon completion.
You can watch the full course on YouTube . </p>
<a href="https://www.youtube.com/watch?v=i_LwzRVP7bg" class="btn btn-primary">Visit</a>
</div>
</div>
<div class="card" style="width: 18rem;">
<img src="https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRAf30fEtCnZQcROerynxMN1jOjmyyh7i1kOvL6AWeG1NFXl2qNCTEToGupZDuafNa98kI&usqp=CAU" class="card-img-top" height="200px" alt="...">
<div class="card-body">
<h5 class="card-title">ML for begginers:GreatLearning</h5>
<p class="card-text">This free course starts by providing a brief introduction to Machine Learning.
This webpage offers a machine learning tutorial. It covers the basics of machine learning, including different types and common algorithms.
you will learn about the mathematical space where Machine Learning occurs. It also explains the steps involved in a typical machine learning process.complete the quiz at the end to earn a free certificate.
</p>
<a href="https://www.mygreatlearning.com/blog/machine-learning-tutorial/" class="btn btn-primary">Visit</a>
</div>
</div>
<div class="card" style="width: 18rem;">
<img src="data:image/jpeg;base64,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" class="card-img-top" height="200px" alt="...">
<div class="card-body">
<h5 class="card-title">ML course with practical</h5>
<p class="card-text">The “Machine Learning Full Course with Practical” on YouTube is a beginner-friendly tutorial lasting approximately
6 hours and 45 minutes. Presented by WsCube Tech, it covers Python basics, data preprocessing, regression, classification, clustering, NLP,
deep learning, model evaluation, and real-world applications.
You can explore the course on YouTube to enhance your machine learning skills!</p>
<a href="https://www.youtube.com/watch?v=O0Ka_nBRtN0" class="btn btn-primary">Visit</a>
</div>
</div>
</div>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/jquery.slim.min.js" integrity="sha384-DfXdz2htPH0lsSSs5nCTpuj/zy4C+OGpamoFVy38MVBnE+IbbVYUew+OrCXaRkfj" crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.bundle.min.js" integrity="sha384-Fy6S3B9q64WdZWQUiU+q4/2Lc9npb8tCaSX9FK7E8HnRr0Jz8D6OP9dO5Vg3Q9ct" crossorigin="anonymous"></script>
</body>
</html>