Data Science: Machine Learning and Deep Learning with Python
4.2 (39 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
3,069 students enrolled

Data Science: Machine Learning and Deep Learning with Python

Learn Data Science with Data Parsing, Data Visualization, Data Processing, Supervised & Unsupervised Machine Learning
4.2 (39 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
3,069 students enrolled
Last updated 9/2019
English
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Current price: $12.99 Original price: $19.99 Discount: 35% off
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This course includes
  • 14.5 hours on-demand video
  • 12 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Course content
Expand all 60 lectures 14:19:51
+ Introduction and Overview
3 lectures 18:04

What is the problem that we are going to solve?

Preview 03:35

Solution of the problem/ Introduction to Course

Preview 09:53

Overview of the course

An overview
04:36
+ Python
6 lectures 01:31:24

Python introduction

Intro to python
05:29

Python crash course part one

Python crash course(1)
18:32

Python crash course part two

Python crash course(2)
14:30

Python crash course part three

Python crash course(3)
13:09

Python crash course part four

Python crash course(4)
14:55

Solution of python quiz

Python Quiz Solution
24:49
+ Data Science
2 lectures 20:43

Getting started with data science

Intro to datascience
13:51

Types of Data Science

Types of data in DS
06:52
+ Data Parsing
8 lectures 01:51:37

Introduction to Scrapy(library)

Introduction to Scrapy
15:40

Getting started with scrapy

Spider to convert one quote into structured data
13:08

Parsing complete page by using scrapy

Spider to convert the whole page into structured data
19:39

Parsing data with pagination part one

Spider to scrape the paginations(1)
09:00

Parsing data with pagination part two

Spider to scrape the paginations(2)
14:00

Parsing data with scrolling part one

Spider to scrape scrolling pages(1)
08:37

Parsing data with scrolling part two

Spider to scrape scrolling pages(2)
14:00

Submitting form by using scrapy

Spider to scrape data by submitting form
17:33
+ Libraries to deal with data
4 lectures 01:02:35

Numpy library Part 1

Numpy(1)
16:43

Numpy library Part 2

Numpy(2)
07:53

Pandas library Part 1

Pandas(1)
15:44

Pandas library Part 2

Pandas(2)
22:15
+ Data Visualizations
4 lectures 59:58

Matplotlib for data visualization

Matplotlib
20:00

Seaborn for data visualization part 1

Seaborn(1)
16:01

Seaborn for data visualization part 2

Seaborn(2)
08:00

Plotly for data visualization

Plotly
15:57
+ Data Prepocessing
6 lectures 01:24:36

Dealing with missing values in data part 1

Missing Values(1)
13:33

Dealing with missing values in data part 2

Missing Values(2)
13:35

Dealing with outliers in data part

Outlier removal
14:35

Normalizing the behavior of data

Data Normalization
14:09

Encoding the categorical values in data part 1

Encoding(1)
15:19

Encoding the categorical values in data part 2

Encoding(2)
13:25
+ Data Science Project
3 lectures 45:16

A final project of Data Science part 1

Data Science Project(1)
20:31

A final project of Data Science part 2

Data Science Project(2)
16:48

A final project of Data Science part 3

Data Science Project(3)
07:57
+ Machine Learning
2 lectures 26:14

Introduction to machine learning part 1

Intro to ML(1)
15:42

Introduction to machine learning part 2

Intro to ML(2)
10:32
+ Linear Regression
5 lectures 01:16:22

Linear regression explained theoretically

Linear regression(theory)
13:45

Linear regression implementation part 1

Linear regression (implementation - 1)
11:41

Linear regression implementation part 2

Linear regression (implementation - 2)
11:36

Gradient decent implementation part 1

Gradient Decent (1)
17:56

Gradient decent implementation part 2

Gradient Decent (2)
21:24
Requirements
  • Basic Knowledge of any programming language
  • Passion of learning
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 “python” 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 we’ll do together.

After that, we’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 it’s the implementation in TensorFlow followed by a complete deep learning-based project. And in the end, some hyperparameter tuning techniques will be discussed that’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