Machine Learning A-Z : Become Kaggle Master

Name: Machine Learning A-Z : Become Kaggle Master
Price: 9 USD
Availability: InStock

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.

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

Machine Learning A-Z : Become Kaggle Master

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