Machine Learning A-Z : Become Kaggle Master

36h 23m 54s
English
Paid
May 7, 2024

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.

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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

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# Title Duration
1 Introduction to the course 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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