Machine learning

The Machine Learning & Data Science A-Z: Hands-on Python 2023 course is designed for individuals who are interested in data science and machine learning but have limited background knowledge or struggle with understanding the concepts. The course also aims to teach Python programming to those who are intimidated by coding.

The course is divided into categories and starts with the basics, covering all necessary setups and useful machine-learning libraries like NumPy, Pandas, and Matplotlib. The course also covers supervised and unsupervised learning, model tuning, and how to use real datasets to create models. Each section includes Python code templates and resources that can be downloaded. The courses are usually available for INR 3,499 on Udemy but you can click on the link to get 87% off and get the Machine Learning & Data Science A-Z: Hands-on Python 2023 Course for INR 449.

Who all can opt for this course?

  • Anyone with at least high school-level arithmetic skills who are interested in data science and machine learning
  • Students who are novices, intermediates, or even experts in the fields of machine learning, data science, and artificial intelligence
  • College students who want to secure their future employment
  • Employees that are eager to master machine learning and advance in their positions
  • Anyone interested in machine learning concepts yet hesitant to code in Python
  • Anyone who wants to use machine learning to launch a new business
  • Researchers and graduate students who want to use machine learning models in their theses and projects

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 3,49987% off
Duration14.5 hours
Student Enrollment68,715 students
InstructorNavid Shirzadi
Topics CoveredMachine learning, classification, regression, clustering techniques, Python programming
Course LevelBeginner
Total Student Reviews1,076

Learning Outcomes

  • Understanding the fundamental concepts
  • Complete tutorial about tools like Numpy and Pandas
  • Data Visualization
  • Data Preprocessing
  • Recognizing the algorithms’ underlying principle
  • Creating several machine learning model types
  • Understanding how to maximize the hyperparameters in your models
  • Learn to create models depending on the needs of your future company

Course Content

S.No.Module (Duration)Topics
1.Introduction (31 minutes)Course Content
What is Machine Learning? Some Basic Terms
Python Installation
Python IDE
IDE Installation
Installation of Required Libraries
Spyder Interface
2.Machine Learning Useful Packages (Libraries) (03 hours 45 minutes)Python Source Codes
Visualization with Matplotlib1
Visualization with Matplotlib2
Visualization with Matplotlib3
Visualization with Matplotlib4
Visualization with Matplotlib5
Chapter 2 Quiz
3.Data Preprocessing (02 hours 40 minutes)Reading and Modifying a Dataset
Statistics3 – Covariance
Missing Values1
Missing Values2
Outlier Detection1
Outlier Detection2
Outlier Detection3
Dummy Variable
Chapter3 Quiz
4.Machine Learning Introduction (07 minutes)Learning Types
Chapter 4 Quiz
5.Supervised Learning – Classification (02 hours 51 minutes)Supervised Learning Models – Introduction and Understanding the Data
k-NN Concepts
k-NN Model Development
k-NN Training-Set and Test-Set Creation
Decision Tree Concepts
Decision Tree Model Development
Decision Tree – Cross Validation
Naive Bayes Concepts
Naive Bayes Model Development
Logistic Regression Concepts
Logistic Regression Model Development
Model Evaluation Concepts
Model Evaluation – Calculating with Python
Chapter 5 Quiz
6.Supervised Learning – Regression (02 hours 19 minutes)Simple and Multiple Linear Regression Concepts
Multiple Linear Regression – Model Development
Evaluation Metrics – Concepts
Evaluation Metrics – Implementation
Polynomial Linear Regression Concepts
Polynomial Linear Regression Model Development
Random Forest Concepts
Random Forest Model Development
Support Vector Regression Concepts
Support Vector Regression Model Development
Chapter 6 Quiz
7.Unsupervised Learning – Clustering Techniques (01 hour 27 minutes)Introduction
K-means Concepts1
K-means Concepts2
K-means Model Development1
K-means Model Development2
K-means – Model Evaluation
DBSCAN Concepts
DBSCAN Model Development
Hierarchical Clustering Concepts
Hierarchical Clustering Model Development
Chapter 7 Quiz
8.Hyper Parameter Optimization (Model Tuning) (42 minutes)Introduction
Support Vector Regression – Model Tuning
K-Means – Model Tuning
k-NN – Model Tuning
Overfitting and Underfitting
9.Bonus (10 seconds)Bonus Lecture

Resources Required

  • Python’s basic syntax

Featured Review

Soheil Ghahremani (5/5): It is really a fantastic course. One of the best video classes about machine learning and data science. Thanks, dear Navid


  • Mohammad Javad Heidari (5/5): it was a great course, one of the best course that i passed.
  • Vijesh N K (5/5): It was a great tutorial, we can just start our data science journey.
  • Narmin Ariannia (5/5): The teaching style is also excellent and makes you interested in machine learning and data science.
  • Kabir Singla (5/5): This course is great as it explains basic concepts in a wonderful way!!!


  • Shubhajit C. (3.5/5): It could have been better if you could clearly made us understand a few concepts . for eg. in first video of Supervised learning why do we use the iloc[:,n].
  • Teimuraz J. (3/5): It is not a bad introduction to DS and ML field but it feels somewhat superficial. All the concepts can be learned on one’s own and similar free lectures can be found on Youtube. That said if you can grab this course with a gift coupon or high discount, it may be worth it, though I wouldn’t call the course A-Z.
  • NIKHIL M. (2.5/5): very little concepts covered
  • Ronald A. (1.5/5): Poor discussion and explanation. no proper flow resulting to confusions

About the Author

The instructor of this course is Navid Shirzadi who is a Data Analyst/Optimization Expert. With a 4.5 instructor rating and 1,570 reviews on Udemy, he offers 5 courses and has taught 71,530 students so far.

  • He has more than 7 years of research experience in the field of integrated energy system control, and he has a strong command of mathematical optimization techniques.
  • He is also skilled at writing Python code and creating deep learning and machine learning models for various applications.
  • He has written a number of articles about applying artificial intelligence, deep learning, and machine learning to build and control energy system methods.
  • To sum up, he would love to share his knowledge with the students and he is very excited about the applications of data science, machine learning, and optimization to real-world situations!

Comparison Table

ParametersMachine Learning & Data Science A-Z: Hands-on Python 2023Time Series Analysis Real World Projects in PythonNatural Language Processing Real-World Projects in Python
OffersINR 449 (INR 3,499) 87% offINR 449 (INR 3,499) 87% offINR 449 (INR 3,499) 87% off
Duration14.5 hours4 hours5.5 hours
Student Enrollments68,71562,94172,826
InstructorsNavid ShirzadiShan SinghShan Singh
Register HereApply Now!Apply Now!Apply Now!

Leave feedback about this

  • Rating