Course Content
Introduction to Machine Learning
0/3
Introduction to Machine Learning
02:59
Machine learning types
08:35
Machine learning workflow
03:39
Mathematics in Machine Learning
0/4
Data an linear algebra
03:47
Calculus
03:23
Descriptive and inferential statistics
05:24
Probability
07:39
Python (Practice)
0/2
Introduction to setting up Python
06:27
Basic coding skills
05:52
Machine Learning Libraries (Practice)
0/6
NumPy – Python
07:34
Pandas – Python
10:05
Pandas [2] – Python
05:10
Matplotlib – Python
13:31
Natural Language Processing (NLP)
08:52
Scikit – Python
09:04
Supervised Learning (Practice)
0/13
Linear regression – Theory
10:42
Logistic regression – Theory
04:07
KNN – Theory
04:48
KNN – Practice 1
12:19
KNN – Practice 2
09:23
Naive Bayes – Theory
05:48
Naive Bayes – Practice 1
13:52
Naive Bayes – Practice 2
05:07
Naive Bayes – Practice 3
03:20
SVM – Theory
09:45
SVM – Practice
10:07
Decision Tree – Theory
07:04
Decision Tree – Practice
17:01
Unsupervised Learning (Practice)
0/5
Kmeans – Theory
04:38
Kmeans – Practice
11:48
Hierachical Clustering – Theory
03:14
Hierachical Clustering – Practice
12:51
DBScan – Theory
03:15
Project Fake News Detection
0/4
Part 1
14:36
Part 2
14:50
Part 3
14:45
Part 4
10:03
Complete Machine Learning Course for Beginners – in Python
00:00
00:00
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