Data Science and Machine Learning with Python Masterclass
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21:18:43
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01-data science and machine learning course intro.mp4
03:19
02-data science and machine learning marketplace.mp4
06:55
03-data science job opportunities.mp4
04:24
04-data science job roles.mp4
10:23
05-what is a data scientist.mp4
17:00
06-how to get a data science job.mp4
18:39
07-data science projects overview.mp4
11:52
08-why we use python.mp4
03:14
09-what is data science.mp4
13:24
10-what is machine learning.mp4
14:22
11-machine learning concepts and algorithms.mp4
14:42
12-machine learning vs deep learning.mp4
11:09
13-what is deep learning.mp4
09:44
14-what is python programming.mp4
06:03
15-why python for data science.mp4
04:35
16-what is jupyter.mp4
03:54
17-what is google colab.mp4
03:27
18-getting started with colab.mp4
09:07
19-python variables and booleans .mp4
11:47
20-python operators.mp4
25:26
21-python numbers and booleans.mp4
07:47
22-python strings.mp4
13:12
23-python conditional statements.mp4
13:53
24-python for loops and while loops.mp4
08:07
25-python lists.mp4
05:10
26-more about python lists.mp4
15:08
27-python tuples.mp4
11:25
28-python dictionaries.mp4
20:19
29-python sets.mp4
09:41
30-compound data types.mp4
12:58
31-python object oriented programming.mp4
18:47
32-intro to statistics.mp4
07:11
33-descriptive statistics.mp4
06:35
34-measure of variability.mp4
12:19
35-measure of variability continued.mp4
09:35
36-measures of variable relationship.mp4
07:37
37-inferential statistics.mp4
15:18
38-measures of asymmetry.mp4
01:57
39-sampling distribution.mp4
07:34
40-what exactly probability.mp4
03:44
41-expected values.mp4
02:38
42-relative frequency.mp4
05:15
43-hypothesis testing overview.mp4
09:09
44-numpy array data types.mp4
12:58
45-numpy arrays.mp4
08:22
46-numpy array basics.mp4
11:36
47-numpy array indexing.mp4
09:10
48-numpy array computations.mp4
05:53
49-broadcasting.mp4
04:32
50-intro to pandas.mp4
15:52
51-pandas continued.mp4
18:05
52-data visualization overview.mp4
24:49
53-different data visualization libraries.mp4
12:48
54-python data visualization implementation.mp4
08:27
55-intro to machine learning.mp4
26:03
56-exploratory data analysis.mp4
13:06
57-feature scaling.mp4
07:41
58-data cleaning.mp4
07:43
59-feature engineering.mp4
06:11
60-linear regression intro.mp4
08:17
61-gradient descent.mp4
05:59
62-linear regression and correlation methods.mp4
26:33
63-linear regression implementation.mp4
05:06
64-logistic regression.mp4
03:22
65-knn overview.mp4
03:01
66-parametic vs non-parametic models.mp4
03:28
67-eda on iris dataset.mp4
22:08
68-knn intuition.mp4
02:16
69-implement the knn algorithm from scratch.mp4
11:45
70-compare the result with sklearn library.mp4
03:47
71-knn hyperparameter tuning using the cross-validation.mp4
10:47
72-the decision boundary visualization.mp4
04:55
73-knn-manhattan vs euclidean distance.mp4
11:21
74-knn scaling.mp4
06:01
75-curse of dimensionality.mp4
08:09
76-knn use cases.mp4
03:32
77-knn pros and cons.mp4
05:32
78-decision trees section overview.mp4
04:11
79-eda on adult dataset.mp4
16:53
80-what is entropy and info gain.mp4
21:50
81-the decisions tree id3 algorithm part 1.mp4
11:33
82-the decisions tree id3 algorithm part 2.mp4
07:35
83-the decisions tree id3 algorithm part 3.mp4
04:07
84-putting everything together.mp4
21:23
85-evaluating our id3 implementation.mp4
16:53
86-compare with sklearn implementation.mp4
08:52
87-visualization the tree.mp4
10:15
88-plot the features importance.mp4
05:51
89-decision tree hyper-parameters.mp4
11:39
90-pruning.mp4
17:11
91-optional gain ration.mp4
02:49
92-what is ensemble learning.mp4
13:06
93-what is bootstrap sampling.mp4
08:25
94-what is bagging.mp4
05:20
95-out-of-bag error (oob error).mp4
07:47
96-implementing random forests from scratch part 1.mp4
22:34
97-implementing random forests from scratch part 2.mp4
06:10
98-random forests hyper-parameters.mp4
04:23
99-what is boosting.mp4
04:41
100-adaboost part 1.mp4
04:10
101-adaboost part 2.mp4
14:33
102-svm outline.mp4
05:16
103-svm intuition.mp4
11:39
104-hard vs soft margins.mp4
13:25
105-c hyper-parameter.mp4
04:17
106-kernel trick.mp4
12:18
107-svm-kernel types.mp4
18:13
108-svm with linear dataset (iris).mp4
13:35
109-svm with non-linear dataset.mp4
12:50
110-svm with regression.mp4
05:51
111-project voice gender recognition using svm.mp4
04:26
112-unsupervised machine learning intro.mp4
20:22
113-unsupervised machine learning continued.mp4
20:48
114-data standardization.mp4
19:05
115-pca section overview.mp4
05:12
116-what is pca.mp4
09:37
117-covariance matrix vs svd.mp4
04:58
118-image compression scratch.mp4
27:00
119-data preprocessing scratch.mp4
14:31
120-creating a data science resume.mp4
06:45
121-data science cover letter.mp4
03:33
122-how to contact recruiters.mp4
04:20
123-getting started with freelancing.mp4
04:13
124-top freelance websites.mp4
05:35
125-personal branding.mp4
04:02
126-networking dos and donts.mp4
03:45
127-importance of a website.mp4
02:56
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- language english
- Training sessions 127
- duration 21:18:43
- Release Date 2024/03/06
