
What is the problem that we are going to solve?
Solution of the problem/ Introduction to Course
Overview of the course
Python introduction
Python crash course part one
Python crash course part two
Python crash course part three
Python crash course part four
Solution of python quiz
Getting started with data science
Types of Data Science
Introduction to Scrapy(library)
Getting started with scrapy
Parsing complete page by using scrapy
Parsing data with pagination part one
Parsing data with pagination part two
Parsing data with scrolling part one
Parsing data with scrolling part two
Submitting form by using scrapy
Numpy library Part 1
Numpy library Part 2
Pandas library Part 1
Pandas library Part 2
Matplotlib for data visualization
Seaborn for data visualization part 1
Seaborn for data visualization part 2
Plotly for data visualization
Dealing with missing values in data part 1
Dealing with missing values in data part 2
Dealing with outliers in data part
Normalizing the behavior of data
Encoding the categorical values in data part 1
Encoding the categorical values in data part 2
A final project of Data Science part 1
A final project of Data Science part 2
A final project of Data Science part 3
Introduction to machine learning part 1
Introduction to machine learning part 2
Linear regression explained theoretically
Linear regression implementation part 1
Linear regression implementation part 2
Gradient decent implementation part 1
Gradient decent implementation part 2
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.