Data Science: Machine Learning and Deep Learning with Python


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Course overview

Course type
Paid course
All Levels
15 hours
60 lessons
Available on completion
Course author
Teach Premium
  • 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


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

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.

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