Data Science: Machine Learning and Deep Learning with Python

سرفصل های دوره

Learn Data Science with Data Parsing, Data Visualization, Data Processing, Supervised & Unsupervised Machine Learning

1. Introduction and Overview

1.1 1 - Problem statement.zip

1. problem statement

2.1 2 - Possible solutions.zip

2. Solution to a Problem

3.1 3 - An overview.zip

3. An overview

2. Python

1.1 4 - Python setups.zip

1. Intro to python

2.1 5 - Python crash course(1).zip

2. Python crash course(1)

3.1 6 - Python crash course(2).zip

3. Python crash course(2)

4. Python crash course(3)

5. Python crash course(4)

6. Python Quiz Solution

3. Data Science

1. Intro to datascience

2.1 8 - types of data.zip

2. Types of data in DS

4. Data Parsing

1. Introduction to Scrapy

2.1 9 - Dealing with unstructured data.zip

2. Spider to convert one quote into structured data

3. Spider to convert the whole page into structured data

4. Spider to scrape the paginations(1)

5. Spider to scrape the paginations(2)

6. Spider to scrape scrolling pages(1)

7. Spider to scrape scrolling pages(2)

8. Spider to scrape data by submitting form

5. Libraries to deal with data

1.1 10 - Libraries.zip

1. Numpy(1)

2. Numpy(2)

3. Pandas(1)

4. Pandas(2)

6. Data Visualizations

1. Matplotlib

2. Seaborn(1)

3. Seaborn(2)

4. Plotly

7. Data Prepocessing

1. Missing Values(1)

2. Missing Values(2)

3. Outlier removal

4. Data Normalization

5. Encoding(1)

6. Encoding(2)

8. Data Science Project

1. Data Science Project(1)

2. Data Science Project(2)

3. Data Science Project(3)

9. Machine Learning

1. Intro to ML(1)

2. Intro to ML(2)

10. Linear Regression

1. Linear regression(theory)

2. Linear regression (implementation - 1)

3. Linear regression (implementation - 2)

4. Gradient Decent (1)

5. Gradient Decent (2)

11. Sklearn

1. Logistic regression

2. SVM

12. K Nearest Neighbors

1.1 14 - KNN.zip

1. KNN(theory)

2. KNN(implementation-1)

3. KNN(implementation-2)

4. KNN(sklearn-implementation)

13. Unsupervised Machine Learning

1.1 15 - Kmean.zip

1. Unsupervised-Kmean

2. Kmean(implementation)

3. Kmean(sklearn-implementation)

14. Deep Learning

1.1 16 - Introduction to deep learning.zip

1. Introduction to Deep Learning(1)

2. Introduction to Deep Learning(2)

15. Tensorflow

1. TensorFlow(dataset reading and preprocessing)

2. TensorFlow(setting up layers and nodes)

3. TensorFlow(setting up weights and biases)

16. Tensorflow Project

1. TensorFlow(implementation completed)

2. TensorFlow(visualization of mse and accuracy)

17. Parameter Tuning

1. TensorFlow(parameter tuning)

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تولید کننده: Udemy