Data Science: Machine Learning and Deep Learning with Python

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14:19:51

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1. Introduction and Overview

1.1 1 - Problem statement.zip

1. problem statement.mp4

03:35

2.1 2 - Possible solutions.zip

2. Solution to a Problem.mp4

09:53

3.1 3 - An overview.zip

3. An overview.mp4

04:36

2. Python

1.1 4 - Python setups.zip

1. Intro to python.mp4

05:29

2.1 5 - Python crash course(1).zip

2. Python crash course(1).mp4

18:32

3.1 6 - Python crash course(2).zip

3. Python crash course(2).mp4

14:30

4. Python crash course(3).mp4

13:09

5. Python crash course(4).mp4

14:55

6. Python Quiz Solution.mp4

24:49

3. Data Science

1. Intro to datascience.mp4

13:51

2.1 8 - types of data.zip

2. Types of data in DS.mp4

06:52

4. Data Parsing

1. Introduction to Scrapy.mp4

15:40

2.1 9 - Dealing with unstructured data.zip

2. Spider to convert one quote into structured data.mp4

13:08

3. Spider to convert the whole page into structured data.mp4

19:39

4. Spider to scrape the paginations(1).mp4

09:00

5. Spider to scrape the paginations(2).mp4

14:00

6. Spider to scrape scrolling pages(1).mp4

08:37

7. Spider to scrape scrolling pages(2).mp4

14:00

8. Spider to scrape data by submitting form.mp4

17:33

5. Libraries to deal with data

1.1 10 - Libraries.zip

1. Numpy(1).mp4

16:43

2. Numpy(2).mp4

07:53

3. Pandas(1).mp4

15:44

4. Pandas(2).mp4

22:15

6. Data Visualizations

1. Matplotlib.mp4

20:00

2. Seaborn(1).mp4

16:01

3. Seaborn(2).mp4

08:00

4. Plotly.mp4

15:57

7. Data Prepocessing

1. Missing Values(1).mp4

13:33

2. Missing Values(2).mp4

13:35

3. Outlier removal.mp4

14:35

4. Data Normalization.mp4

14:09

5. Encoding(1).mp4

15:19

6. Encoding(2).mp4

13:25

8. Data Science Project

1. Data Science Project(1).mp4

20:31

2. Data Science Project(2).mp4

16:48

3. Data Science Project(3).mp4

07:57

9. Machine Learning

1. Intro to ML(1).mp4

15:42

2. Intro to ML(2).mp4

10:32

10. Linear Regression

1. Linear regression(theory).mp4

13:45

2. Linear regression (implementation - 1).mp4

11:41

3. Linear regression (implementation - 2).mp4

11:36

4. Gradient Decent (1).mp4

17:56

5. Gradient Decent (2).mp4

21:24

11. Sklearn

1. Logistic regression.mp4

17:34

2. SVM.mp4

19:03

12. K Nearest Neighbors

1.1 14 - KNN.zip

1. KNN(theory).mp4

08:41

2. KNN(implementation-1).mp4

22:21

3. KNN(implementation-2).mp4

18:12

4. KNN(sklearn-implementation).mp4

17:23

13. Unsupervised Machine Learning

1.1 15 - Kmean.zip

1. Unsupervised-Kmean.mp4

16:08

2. Kmean(implementation).mp4

22:24

3. Kmean(sklearn-implementation).mp4

13:26

14. Deep Learning

1.1 16 - Introduction to deep learning.zip

1. Introduction to Deep Learning(1).mp4

14:16

2. Introduction to Deep Learning(2).mp4

10:06

15. Tensorflow

1. TensorFlow(dataset reading and preprocessing).mp4

20:15

2. TensorFlow(setting up layers and nodes).mp4

24:54

3. TensorFlow(setting up weights and biases).mp4

07:14

16. Tensorflow Project

1. TensorFlow(implementation completed).mp4

15:11

2. TensorFlow(visualization of mse and accuracy).mp4

07:24

17. Parameter Tuning

1. TensorFlow(parameter tuning).mp4

08:30

Description

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

What You'll Learn?

From beginner level to advanced level understanding of :
Data Science:(Online Data Parsing, Data visualization, Data Preprocessing, Preparing data for machine learning)
Machine Learning:(Supervised Machine Learning, Unsupervised Machine Learning, Implementation of algorithms form scratch, Built-in algorithms usages.)
amitDeep Learning:(Tensorflow, Hyperparameter tunings)
Working with some data sets which are benchmarks in industry like : Titanic, Seeds, Rock and Mine

Who is this for?

Those who are interested in Artificial Intelligence

Those who have basic level of understanding of english

Those who have basic knowledge of any programming language

Those who have basic knowledge of OOP

hose who wants to write programs for predictions

Those who are interested in making automated computer programs

Those who wants to unlock the future of IT that is AI

What You Need to Know?

Basic Knowledge of any programming language

Passion of learning

More details

Description
This course focuses on the fundamentals of Data Science, Machine learning, and deep learning in the beginning and with the passage of time, the content and lectures become advanced and more practical. But before everything, the introduction of python is discussed. Python is one of the fastest-growing programming languages and if we specifically look from the perspective of Data Science, Machine learning and deep learning, there is no other choice then â€œpythonâ€ as a programming language.
First of all, there is a crash course on python for those who are not very good with python and then there is an exercise for python that is supposed to be solved by you but if you feel any difficulty in solving the exercise, the solution is also provided.
Then we moved on towards the Data Science and we start from data parsing using Scrapy then the data visualizations by using several libraries of python and finally we end up learning different data preprocessing techniques. And in the end, there is a complete project that weâ€™ll do together.
After that, weâ€™ll be learning a few classical and a few advanced machine learning algorithms. Some of them will be implemented from scratch and the others will be implemented by using the builtin libraries of python. At the end of every algorithm, there will be a mini-project.
Finally, Deep learning will be discussed, the basic structure of an artificial neural network and itâ€™s the implementation in TensorFlow followed by a complete deep learning-based project. And in the end, some hyperparameter tuning techniques will be discussed thatâ€™ll improve the performance of the model.
About The Instructor:
Below is an introduction to Mr. Sajjad Mustafa, the instructor of this course.
He an expert in Web Programming, Data Science, and Machine Learning. He has been working on different topics including the above-mentioned ones for almost 3 years and has been teaching on these projects for more than a year. He has attained mastery over understanding the requirements and making a way to the most unique and proper solutions to the given task.
He is well acquainted with and has deep knowledge of Python, Ruby, JavaScript. Django, ReactJS, React Native, JQuery, HTML, CSS, Bootstrap, C, C++, SQL (MySQL, mySQLite) are also my passion and interest.
He is passionate about new technologies and likes to have a good professional connection. Let's meet with him on the course.
Who this course is for:
Those who are interested in Artificial Intelligence
Those who have basic level of understanding of english
Those who have basic knowledge of any programming language
Those who have basic knowledge of OOP
hose who wants to write programs for predictions
Those who are interested in making automated computer programs
Those who wants to unlock the future of IT that is AI

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