Complete Machine Learning Course for Beginners - in Python

00:00

Course Content

Introduction to Machine Learning

Introduction to Machine Learning

02:59
Machine learning types

08:35
Machine learning workflow

03:39

Mathematics in Machine Learning

Python (Practice)

Machine Learning Libraries (Practice)

Supervised Learning (Practice)

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)

Project Fake News Detection

Student Ratings & Reviews

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Course Content

Introduction to Machine Learning

Introduction to Machine Learning

Machine learning types

Machine learning workflow

Mathematics in Machine Learning

Data an linear algebra

Calculus

Descriptive and inferential statistics

Probability

Python (Practice)

Introduction to setting up Python

Basic coding skills

Machine Learning Libraries (Practice)

NumPy – Python

Pandas – Python

Pandas [2] – Python

Matplotlib – Python

Natural Language Processing (NLP)

Scikit – Python

Supervised Learning (Practice)

Linear regression – Theory

Logistic regression – Theory

KNN – Theory

KNN – Practice 1

KNN – Practice 2

Naive Bayes – Theory

Naive Bayes – Practice 1

Naive Bayes – Practice 2

Naive Bayes – Practice 3

SVM – Theory

SVM – Practice

Decision Tree – Theory

Decision Tree – Practice

Unsupervised Learning (Practice)

Kmeans – Theory

Kmeans – Practice

Hierachical Clustering – Theory

Hierachical Clustering – Practice

DBScan – Theory

Project Fake News Detection

Part 1

Part 2

Part 3

Part 4

Student Ratings & Reviews

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