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 Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 14.5 hours |
| Rating | 4.6/5 |
| Student Enrollment | 68,715 students |
| Instructor | Navid Shirzadi https://www.linkedin.com/in/navidshirzadi |
| Topics Covered | Machine learning, classification, regression, clustering techniques, Python programming |
| Course Level | Beginner |
| Total Student Reviews | 1,076 |
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 |
| NumPy1 | ||
| NumPy2 | ||
| NumPy3 | ||
| NumPy4 | ||
| NumPy5 | ||
| NumPy6 | ||
| Pandas1 | ||
| Pandas2 | ||
| Pandas3 | ||
| Pandas4 | ||
| 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 |
| Statistics1 | ||
| Statistics2 | ||
| Statistics3 – Covariance | ||
| Missing Values1 | ||
| Missing Values2 | ||
| Outlier Detection1 | ||
| Outlier Detection2 | ||
| Outlier Detection3 | ||
| Concatenation | ||
| Dummy Variable | ||
| Normalization | ||
| 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
Pros
- 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!!!
Cons
- 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
| Parameters | Machine Learning & Data Science A-Z: Hands-on Python 2023 | Time Series Analysis Real World Projects in Python | Natural Language Processing Real-World Projects in Python |
|---|---|---|---|
| Offers | INR 449 ( | INR 449 ( | INR 449 ( |
| Duration | 14.5 hours | 4 hours | 5.5 hours |
| Rating | 4.6/5 | 4.8/5 | 4.2/5 |
| Student Enrollments | 68,715 | 62,941 | 72,826 |
| Instructors | Navid Shirzadi | Shan Singh | Shan Singh |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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