In the Learn Python for Data Science & Machine Learning from A-Z course, students will learn how to program using Python for Data Science and Machine Learning. The course includes how to analyze and visualize data as well as how to use it in a useful way.
The major goal of the instructor is to provide the education needed to become a professional Data Scientist with Python and understand the fundamentals of Python programming for Data Science and Machine Learning. The courses are usually available at INR 3,499 on Udemy but you can click now to get 87% off and get Learn Python for Data Science & Machine Learning from A-Z Course for INR 449.
Who all can opt for this course?
- Students interested in learning about Python for Machine Learning & Data Science
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 22 hours |
| Rating | 4.4/5 |
| Student Enrollment | 112,438 students |
| Instructor | Juan E. Galvan https://www.linkedin.com/in/juane.galvan |
| Topics Covered | Data Science, Machine Learning, Python and Statistics for Data Science, NumPy, Pandas, etc. |
| Course Level | Beginner |
| Total Student Reviews | 1,639 |
Learning Outcomes
- Become a certified consultant, data scientist, data engineer, or data analyst
- Learn how to manipulate, clean, process, and wrangle data
- Creating a résumé and getting your first job as a Data Scientist
- Python for Data Science
- How to develop sophisticated Python applications for real-world business scenarios
- Python plotting tutorial (graphs, charts, plots, histograms, etc)
- Use NumPy to handle numerical data
- Practical applications of Machine Learning
- Differentiate between Supervised and Unsupervised Machine Learning
- Understand Regression, Classification, Clustering and Sci-kit learn
- Concepts of Machine Learning & Algorithms
- K-Means clustering
- Use Python to organize, examine, and display data
- Building custom Data Solutions
- Statistics for Data Science
- Probability & hypotheses testing
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Introduction (01 hour 11 minutes) | Who is This Course For? |
| Data Science + Machine Learning Marketplace | ||
| Data Science Job Opportunities | ||
| Data Science Job Roles | ||
| What is a Data Scientist? | ||
| How To Get a Data Science Job | ||
| Data Science Projects Overview | ||
| 2. | Data Science & Machine Learning Concepts (01 hour 06 minutes) | Why We Use Python? |
| What is Data Science? | ||
| What is Machine Learning? | ||
| Machine Learning Concepts & Algorithms | ||
| What is Deep Learning? | ||
| Machine Learning vs Deep Learning | ||
| 3. | Python For Data Science (03 hours 35 minutes) | What is Programming? |
| Why Python for Data Science? | ||
| What is Jupyter? | ||
| What is Google Colab? | ||
| Python Variables, Booleans and None | ||
| Getting Started with Google Colab | ||
| Python Operators | ||
| Python Numbers & Booleans | ||
| Python Strings | ||
| Python Conditional Statements | ||
| Python For Loops and While Loops | ||
| Python Lists | ||
| More about Lists | ||
| Python Tuples | ||
| Python Dictionaries | ||
| Python Sets | ||
| Compound Data Types & When to use each one? | ||
| Python Functions | ||
| Object Oriented Programming in Python | ||
| 4. | Statistics for Data Science (01 hour 08 minutes) | Intro To Statistics |
| Descriptive Statistics | ||
| Measure of Variability | ||
| Measure of Variability Continued | ||
| Measures of Variable Relationship | ||
| Inferential Statistics | ||
| Measure of Asymmetry | ||
| Sampling Distribution | ||
| 5. | Probability & Hypothesis Testing (20 minutes) | What Exactly is Probability? |
| Expected Values | ||
| Relative Frequency | ||
| Hypothesis Testing Overview | ||
| 6. | NumPy Data Analysis (52 minutes) | Intro NumPy Array Data Types |
| NumPy Arrays | ||
| NumPy Arrays Basics | ||
| NumPy Array Indexing | ||
| NumPy Array Computations | ||
| Broadcasting | ||
| 7. | Pandas Data Analysis (33 minutes) | Introduction to Pandas |
| Introduction to Pandas Continued | ||
| 8. | Python Data Visualization (46 minutes) | Data Visualization Overview |
| Different Data Visualization Libraries in Python | ||
| Python Data Visualization Implementation | ||
| 9. | Machine Learning (26 minutes) | Introduction To Machine Learning |
| 10. | Data Loading & Exploration (13 minutes) | Exploratory Data Analysis |
| 11. | Data Cleaning (15 minutes) | Feature Scaling |
| Data Cleaning | ||
| 12. | Feature Selecting and Engineering (06 minutes) | Feature Engineering |
| 13. | Linear and Logistic Regression (49 minutes) | Linear Regression Intro |
| Gradient Descent | ||
| Linear Regression + Correlation Methods | ||
| Linear Regression Implementation | ||
| Logistic Regression | ||
| 14. | K Nearest Neighbors (01 hour 36 minutes) | KNN Overview |
| parametric vs non-parametric models | ||
| EDA on Iris Dataset | ||
| The KNN Intuition | ||
| Implement the KNN algorithm from scratch | ||
| Compare the result with the sklearn library | ||
| Hyperparameter tuning using the cross-validation | ||
| The decision boundary visualization | ||
| Manhattan vs Euclidean Distance | ||
| Feature scaling in KNN | ||
| Curse of dimensionality | ||
| KNN use cases | ||
| KNN pros and cons | ||
| 15. | Decision Trees (02 hours 51 minutes) | Decision Trees Section Overview |
| EDA on Adult Dataset | ||
| What is Entropy and Information Gain? | ||
| The Decision Tree ID3 algorithm from scratch Part 1 | ||
| The Decision Tree ID3 algorithm from scratch Part 2 | ||
| The Decision Tree ID3 algorithm from scratch Part 3 | ||
| ID3 – Putting Everything Together | ||
| Evaluating our ID3 implementation | ||
| Compare with the Sklearn implementation | ||
| Visualizing the tree | ||
| Plot the features importance | ||
| Decision Trees Hyper-parameters | ||
| Pruning | ||
| [Optional] Gain Ration | ||
| Decision Trees Pros and Cons | ||
| [Project] Predict whether income exceeds $50K/yr – Overview | ||
| 16. | Ensemble Learning and Random Forests (01 hour 44 minutes) | Ensemble Learning Section Overview |
| What is Ensemble Learning? | ||
| What is Bootstrap Sampling? | ||
| What is Bagging? | ||
| Out-of-Bag Error (OOB Error) | ||
| Implementing Random Forests from scratch Part 1 | ||
| Implementing Random Forests from scratch Part 2 | ||
| Compare with sklearn implementation | ||
| Random Forests Hyper-Parameters | ||
| Random Forests Pros and Cons | ||
| What is Boosting? | ||
| AdaBoost Part 1 | ||
| AdaBoost Part 2 | ||
| 17. | Support Vector Machines (01 hour 41 minutes) | SVM Outline |
| SVM intuition | ||
| Hard vs Soft Margins | ||
| C hyper-parameter | ||
| Kernel Trick | ||
| SVM – Kernel Types | ||
| SVM with Linear Dataset (Iris) | ||
| SVM with Non-linear Dataset | ||
| SVM with Regression | ||
| [Project] Voice Gender Recognition using SVM | ||
| 18. | K-means (01 hour 00 minutes) | Unsupervised Machine Learning Intro |
| Unsupervised Machine Learning Continued | ||
| Data Standardization | ||
| 19. | PCA (02 hours 00 minutes) | PCA Section Overview |
| What is PCA? | ||
| PCA Drawbacks | ||
| PCA Algorithm Steps (Mathematics) | ||
| Covariance Matrix vs SVD | ||
| PCA – Main Applications | ||
| PCA – Image Compression | ||
| PCA Data Preprocessing | ||
| PCA – Biplot and the Screen Plot | ||
| PCA – Feature Scaling and Screen Plot | ||
| PCA – Supervised vs Unsupervised | ||
| PCA – Visualization | ||
| 20. | Data Science Career (35 minutes) | Creating A Data Science Resume |
| Data Science Cover Letter | ||
| How to Contact Recruiters | ||
| Getting Started with Freelancing | ||
| Top Freelance Websites | ||
| Personal Branding | ||
| Networking Do’s and Don’ts | ||
| Importance of a Website |
Resources Required
- Basic computer literacy
- Although it is not required, students would benefit from prior Python experience
Featured Review
Kumesh Rana (5/5): Excellent! One of the Best course for those who want to learn Data Science and make career in data Science. I highly recommend this course. thanks!
Pros
- Moeen Khan (5/5): it was amazing experience best trainer taught with thorough details and excellent way of teaching
- Muhammad Fazeel Uddin (5/5): the best instructor and the way of his explanation is simply amazing.
- Brandon Freeman (4/5): This course is perhaps best for it’s introduction to supervised machine learning methods, and that it performs phenomenally well.
- Muhammad Asif (4/5): This course is best suit for absolutely beginner to data science field.
Cons
- Ini O. (2.5/5): The speaker repeats himself/the same information in different ways multiple times. It get repetitive and wastes time.
- AISHA AHMED H. (2/5): In the beginning the course was good but when we dived into the details of machine learning, the lecturer was just explaining a bunch of line codes which for me is not the best way to go about when learning coding.
- A B. (1/5): Explains too much on non-complex aspects, but only explains a little/briefly through the complex and important parts. Disappointing. Only using Google Collab as well. Some datasets and resources are not in sync with shown in lectures (especially section 15 onwards).
- Kamal B. (1/5): Section 4 till Section 13, all were explained in a hurry. Very less content. And more of a slide reading. Any course without projects is incomplete and of no use. Multiple sections where Instructor had mentioned that ..”next lesson we’ll do project”…, the next lession was skipped / missing in the course.
About the Author
The instructor of this course is Juan E. Galvan who is a Digital Entrepreneur and Business Coach. With a 4.5 instructor rating and 18,285 reviews on Udemy, he offers 15 courses and has taught 513,290 students so far.
- Juan runs a business since he was in elementary school.
- His background is in technology, including programming, web development, digital marketing, and e-commerce.
- He supports lifelong learning that offers the benefits of a university degree without the drawbacks of high expenses and ineffective teaching techniques.
Comparison Table
| Parameters | Learn Python for Data Science & Machine Learning from A-Z | Python in Practice: 15 Projects to Master Python | Learn Data Science & Machine Learning with R from A-Z |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 23 hours | 21 hours | 28.5 hours |
| Rating | 4.4 /5 | 4.7 /5 | 4.8 /5 |
| Student Enrollments | 112,438 | 88,893 | 94,925 |
| Instructors | Juan E. Galvan | Rahul Mula | Juan E. Galvan |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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