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Machine Learning A-Z : Become Kaggle Master

36h 23m 54s
English
Paid

Machine Learning A-Z : Become Kaggle Master is a 257-lesson 36 hours 23 minutes self-paced course by Udemy. Want to become a good Data Scientist?

Course facts

Lessons
257
Duration
36 hours 23 minutes
Level
All levels
Language
English
Updated
Instructor
Udemy
Price
Premium

Want to become a good Data Scientist?  Then this is a right course for you. This course has been designed by IIT professionals who have mastered in Mathematics and Data Science.  We will be covering complex theory, algorithms and coding libraries in a very simple way which can be easily grasped by any beginner as well. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science from beginner to advance level.

We have solved few Kaggle problems during this course and provided complete solutions so that students can easily compete in real world competition websites.

We have covered following topics in detail in this course:

1. Python Fundamentals

2. Numpy

3. Pandas

4. Some Fun with Maths

5. Inferential Statistics

6. Hypothesis Testing

7. Data Visualisation

8. EDA

9. Simple Linear Regression

10. Multiple Linear regression

11. Hotstar/ Netflix: Case Study

12. Gradient Descent

13. KNN

14. Model Performance Metrics

15. Model Selection

16. Naive Bayes

17. Logistic Regression

18. SVM

19. Decision Tree

20. Ensembles - Bagging / Boosting

21. Unsupervised Learning

22. Dimension Reduction

23. Advance ML Algorithms

24. Deep Learning

Requirements:
  • Any Beginner Can Start this Course
  • 2+2 knowledge is more than sufficient as we have covered almost everything from scratch.
Who this course is for:
  • This course is meant for anyone who wants to become a Data Scientist

What you'll learn:

  • Master Machine Learning on Python
  • Learn to use MatplotLib for Python Plotting
  • Learn to use Numpy and Pandas for Data Analysis
  • Learn to use Seaborn for Statistical Plots
  • Learn All the Mathmatics Required to understand Machine Learning Algorithms
  • Implement Machine Learning Algorithms along with Mathematic intutions
  • Projects of Kaggle Level are included with Complete Solutions
  • Learning End to End Data Science Solutions
  • All Advanced Level Machine Learning Algorithms and Techniques like Regularisations , Boosting , Bagging and many more included
  • Learn All Statistical concepts To Make You Ninza in Machine Learning
  • Real World Case Studies
  • Model Performance Metrics
  • Deep Learning
  • Model Selection

Who teaches Machine Learning A-Z : Become Kaggle Master? Udemy

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Udemy is the largest open marketplace for online courses on the internet. Founded in 2010 by Eren Bali, Oktay Caglar, and Gagan Biyani and headquartered in San Francisco, the company went public on the Nasdaq in 2021 under the ticker UDMY. The platform hosts well over two hundred thousand courses across software development, IT and cloud, data science, design, business, marketing, and creative skills, taught by tens of thousands of independent instructors. Roughly seventy million learners use it worldwide, and the corporate arm — Udemy Business — supplies a curated subset of that catalog to enterprise customers.

Because Udemy is a marketplace rather than a single editorial publisher, the catalog is uneven by design. The strongest material lives in the long-form, project-based courses authored by working engineers — full-stack JavaScript, React, Node.js, Python data science, AWS, Docker and Kubernetes, mobile development with Flutter and React Native, and cloud certification preparation. The CourseFlix listing under this source is the slice of that catalog that has been mirrored here for offline-friendly viewing, organized by topic and updated as new releases land. Pricing on Udemy itself swings dramatically with the site's near-permanent sales, which is why the platform is best treated as a deep reference catalog: pick instructors with strong reviews and a track record of updating their material rather than buying on the headline price alone.

What lessons are included in Machine Learning A-Z : Become Kaggle Master?

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#1: Introduction to the course
All Course Lessons (257)
#Lesson TitleDurationAccess
1
Introduction to the course Demo
13:59
2
Introduction to Kaggle
09:02
3
Installation of Python and Anaconda
09:02
4
Python Introduction
03:34
5
Variables in Python
15:05
6
Numeric Operations in Python
05:28
7
Logical Operations
02:25
8
If else Loop
08:16
9
for while Loop
10:18
10
Functions
11:19
11
String Part1
12:43
12
String Part2
03:02
13
List Part1
03:06
14
List Part2
10:49
15
List Part3
08:53
16
List Part4
08:11
17
Tuples
08:42
18
Sets
07:28
19
Dictionaries
07:36
20
Comprehentions
07:09
21
Introduction
06:20
22
Numpy Operations Part1
19:21
23
Numpy Operations Part2
24:27
24
Introduction
06:30
25
Series
07:59
26
DataFrame
07:54
27
Operations Part1
01:24
28
Operations Part2
05:11
29
Indexes
06:07
30
loc and iloc
07:28
31
Reading CSV
05:29
32
Merging Part1
03:44
33
groupby
05:26
34
Merging Part2
04:26
35
Pivot Table
03:25
36
Linear Algebra : Vectors
43:18
37
Linear Algebra : Matrix Part1
15:44
38
Linear Algebra : Matrix Part2
16:22
39
Linear Algebra : Going From 2D to nD Part1
08:45
40
Linear Algebra : 2D to nD Part2
06:54
41
Inferential Statistics
03:02
42
Probability Theory
13:16
43
Probability Distribution
05:00
44
Expected Values Part1
04:53
45
Expected Values Part2
03:15
46
Without Experiment
06:02
47
Binomial Distribution
04:12
48
Commulative Distribution
02:25
49
PDF
04:44
50
Normal Distribution
05:01
51
z Score
04:45
52
Sampling
09:42
53
Sampling Distribution
06:17
54
Central Limit Theorem
03:08
55
Confidence Interval Part1
07:15
56
Confidence Interval Part2
03:19
57
Introduction
08:30
58
NULL And Alternate Hypothesis
06:29
59
Examples
05:47
60
One/Two Tailed Tests
08:02
61
Critical Value Method
04:19
62
z Table
07:37
63
Examples
03:18
64
More Examples
03:03
65
p Value
05:16
66
Types of Error
02:54
67
t- distribution Part1
03:28
68
t- distribution Part2
02:43
69
Matplotlib
19:55
70
Seaborn
20:26
71
Case Study
10:24
72
Seaborn On Time Series Data
04:27
73
Introduction
01:07
74
Data Sourcing and Cleaning part1
05:07
75
Data Sourcing and Cleaning part2
03:15
76
Data Sourcing and Cleaning part3
04:00
77
Data Sourcing and Cleaning part4
03:57
78
Data Sourcing and Cleaning part5
03:31
79
Data Sourcing and Cleaning part6
04:15
80
Data Cleaning part1
14:42
81
Data Cleaning part2
09:27
82
Univariate Analysis Part1
22:23
83
Univariate Analysis Part2
17:33
84
Segmented Analysis
06:47
85
Bivariate Analysis
13:00
86
Derived Columns
12:15
87
Introduction to Machine Learning
02:14
88
Types of Machine Learning
08:57
89
Introduction to Linear Regression (LR)
03:06
90
How LR Works?
09:18
91
Some Fun With Maths Behind LR
09:30
92
R Square
10:54
93
LR Case Study Part1
14:49
94
LR Case Study Part2
04:54
95
LR Case Study Part3
04:26
96
Residual Square Error (RSE)
01:04
97
Introduction
03:16
98
Case Study part1
07:38
99
Case Study part2
10:38
100
Case Study part3
06:05
101
Adjusted R Square
00:46
102
Case Study Part1
07:09
103
Case Study Part2
09:18
104
Case Study Part3
06:37
105
Case Study Part4
14:39
106
Case Study Part5
04:52
107
Case Study Part6 (RFE)
06:22
108
Introduction to the Problem Statement
05:18
109
Playing With Data
09:30
110
Building Model Part1
04:43
111
Building Model Part2
07:41
112
Building Model Part3
03:52
113
Verification of Model
03:36
114
Pre-Req For Gradient Descent Part1
15:58
115
Pre-Req For Gradient Descent Part2
09:00
116
Cost Functions
02:22
117
Defining Cost Functions More Formally
07:26
118
Gradient Descent
10:51
119
Optimisation
04:14
120
Closed Form Vs Gradient Descent
04:53
121
Gradient Descent case study
05:40
122
Introduction to Classification
12:55
123
Defining Classification Mathematically
07:31
124
Introduction to KNN
11:34
125
Accuracy of KNN
12:45
126
Effectiveness of KNN
12:54
127
Distance Metrics
12:21
128
Distance Metrics Part2
08:31
129
Finding k
09:36
130
KNN on Regression
02:53
131
Case Study
07:56
132
Classification Case1
22:16
133
Classification Case2
15:03
134
Classification Case3
13:35
135
Classification Case4
12:38
136
Performance Metrics Part1
21:16
137
Performance Metrics Part2
15:17
138
Performance Metrics Part3
05:09
139
Model Creation Case1
11:37
140
Model Creation Case2
07:39
141
Gridsearch Case study Part1
11:36
142
Gridsearch Case study Part2
15:03
143
Introduction to Naive Bayes
14:58
144
Bayes Theorem
10:55
145
Practical Example from NB with One Column
08:45
146
Practical Example from NB with Multiple Columns
11:31
147
Naive Bayes On Text Data Part1
08:43
148
Naive Bayes On Text Data Part2
05:11
149
Laplace Smoothing
04:11
150
Bernoulli Naive Bayes
01:38
151
Case Study 1
08:41
152
Case Study 2 Part1
06:52
153
Case Study 2 Part2
02:10
154
Introduction
07:31
155
Sigmoid Function
10:19
156
Log Odds
10:01
157
Case Study
16:29
158
Introduction
15:06
159
Hyperplane Part1
06:28
160
Hyperplane Part2
14:06
161
Maths Behind SVM
07:38
162
Support Vectors
04:04
163
Slack Variable
09:59
164
SVM Case Study Part1
06:25
165
SVM Case Study Part2
06:49
166
Kernel Part1
08:55
167
Kernel Part2
12:34
168
Case Study : 2
07:28
169
Case Study : 3 Part1
08:46
170
Case Study : 3 Part2
05:24
171
Case Study 4
16:33
172
Introduction
07:21
173
Example of DT
07:51
174
Homogenity
05:02
175
Gini Index
07:05
176
Information Gain Part1
05:24
177
Information Gain Part2
05:14
178
Advantages and Disadvantages of DT
04:11
179
Preventing Overfitting Issues in DT
09:59
180
DT Case Study Part1
10:36
181
DT Case Study Part2
09:06
182
Introduction to Ensembles
10:15
183
Bagging
13:10
184
Advantages
04:39
185
Runtime
03:53
186
Case study
05:41
187
Introduction to Boosting
06:06
188
Weak Learners
02:54
189
Shallow Decision Tree
02:31
190
Adaboost Part1
07:49
191
Adaboost Part2
06:45
192
Adaboost Case Study
04:47
193
XGBoost
04:28
194
Boosting Part1
03:10
195
Boosting Part2
06:49
196
XGboost Algorithm
08:36
197
Case Study Part1
09:40
198
Case Study Part2
10:45
199
Case Study Part3
05:34
200
Model Selection Part1
21:29
201
Model Selection Part2
12:32
202
Model Selection Part3
09:42
203
Introduction to Clustering
10:38
204
Segmentation
07:22
205
Kmeans
08:08
206
Maths Behind Kmeans
10:23
207
More Maths
02:22
208
Kmeans plus
10:11
209
Value of K
06:44
210
Hopkins test
02:32
211
Case Study Part1
10:56
212
Case Study Part2
06:48
213
More on Segmentation
04:13
214
Hierarchial Clustering
07:34
215
Case Study
05:35
216
Introduction
30:26
217
PCA
25:59
218
Maths Behind PCA
24:25
219
Case Study Part1
05:16
220
Case Study Part2
15:27
221
Introduction
07:20
222
Example Part1
05:24
223
Example Part2
09:07
224
Optimal Solution
15:23
225
Case study
03:25
226
Regularization
09:01
227
Ridge and Lasso
07:03
228
Case Study
08:51
229
Model Selection
05:32
230
Adjusted R Square
03:20
231
Expectations
02:42
232
Introduction
09:13
233
History
15:39
234
Perceptron
07:18
235
Multi Layered Perceptron
13:07
236
Neural Network Playground
10:27
237
Introduction to the Problem Statement
08:41
238
Playing With The Data
14:34
239
Translating the Problem In Machine Learning World
09:54
240
Dealing with Text Data
08:02
241
Train, Test And Cross Validation Split
10:24
242
Understanding Evaluation Matrix: Log Loss
16:56
243
Building A Worst Model
08:43
244
Evaluating Worst ML Model
05:49
245
First Categorical column analysis
12:14
246
Response encoding and one hot encoder
05:07
247
Laplace Smoothing and Calibrated classifier
12:06
248
Significance of first categorical column
06:54
249
Second Categorical column
04:08
250
Third Categorical column
06:53
251
Data pre-processing before building machine learning model
04:24
252
Building Machine Learning model :part1
13:12
253
Building Machine Learning model :part2
11:39
254
Building Machine Learning model :part3
03:18
255
Building Machine Learning model :part4
03:14
256
Building Machine Learning model :part5
03:49
257
Building Machine Learning model :part6
06:33
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Frequently asked questions

What prerequisites are needed for enrolling in this course?
The course is designed to be accessible to beginners, so there are no strict prerequisites. However, familiarity with basic programming concepts can be helpful. Early lessons cover Python basics, including variable manipulation, loops, and functions, which will be beneficial for those with some prior coding experience.
What projects or skills will I develop during this course?
Throughout the course, you will gain skills in Python programming, data manipulation using libraries like Numpy, and working with data structures such as DataFrames. You will also learn to apply machine learning concepts on platforms like Kaggle, improving your ability to handle real-world data science challenges.
Who is the target audience for this course?
The course is aimed at beginners who are interested in entering the field of data science, as well as those with a basic understanding of programming looking to specialize in machine learning. It is also suitable for professionals seeking to enhance their skills in data analysis and predictive modeling.
How does this course compare in depth and scope to other machine learning courses?
This course offers a comprehensive introduction to machine learning, covering a range of topics from Python programming to statistics and probability. It provides a step-by-step approach, making it suitable for beginners, while still delving into complex theories and algorithms, which can also benefit those with some prior knowledge.
What specific tools or platforms will I learn to use in this course?
You will learn to use Python and Anaconda for coding and data analysis. The course covers various Python libraries, such as Numpy, for numerical operations and Pandas for data manipulation. It also includes content on using the Kaggle platform for applying machine learning techniques to real-world datasets.
What topics are not covered in this course?
While the course covers a broad range of machine learning topics, it does not delve into deep learning frameworks like TensorFlow or PyTorch. It focuses on foundational machine learning concepts and tools, stopping short of more advanced topics such as neural networks and deep learning architectures.
How much time should I expect to commit to complete this course?
With 257 lessons, the course is extensive and requires a significant time investment. The lessons are structured to gradually build your skills from beginner to advanced levels. While the exact duration may vary based on your pace, setting aside regular study time will help you keep up with the course material and gain the most from the content.