The ‘Deep Learning Prerequisites: The Numpy Stack in Python (V2+) Course’ is a project-based program that covers the Numpy stack in Python. Numpy is a Python library and is one of the foundational components of Data Science, Deep Learning and Machine Learning. The course essentially covers the basic concepts of Numpy like Numpy arrays, slicing, indexing, broadcasting and other advanced options.
The course is designed in such a way that students will learn the theoretical concepts and put them into practice. They will learn how to work with Pandas and create visualizations using Matplotlib. The course is usually available for INR 2,799 on Udemy but you can click on the link and get the ‘Deep Learning Prerequisites: The Numpy Stack in Python (V2+) Course’ for INR 449.
Who all can opt for this course?
- Students and professionals who have limited experience with Numpy and who intend to master machine learning and deep learning concepts.
- Those who are aware of machine learning and data science but are struggling to implement the concepts in code.
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 06 hours |
| Rating | 4.6/5 |
| Student Enrollment | 2,47,555 students |
| Instructor | Lazy Programmer Inc. https://www.linkedin.com/in/lazyprogrammerinc. |
| Topics Covered | Numpy, Matplotlib, Pandas, Scipy & Machine Learning basics |
| Course Level | Advanced (Candidates should be aware of basic Python coding, linear algebra, Gaussian distribution, dot product, and matrix inversion) |
| Total Student Reviews | 20,606 |
Learning Outcomes
- Using Scikit-Learn, comprehend supervised machine learning (classification and regression) with examples from the actual world.
- Use the Numpy stack to comprehend and code.
- Apply numerical algorithms using Numpy, Scipy, Matplotlib, and Pandas.
- Discover the benefits and cons of several machine learning models, including Deep Learning, Decision Trees, Random Forest, Linear Regression, Boosting, and more.
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Welcome and Logistics (11 minutes) | Introduction and Outline |
| What will you learn in this course? | ||
| What level of machine learning is taught in this course? | ||
| How will you practice what you learned in this course? | ||
| Extra Resources | ||
| 2. | Numpy (New) (01 hour 12 minutes) | Numpy Section Introduction |
| Arrays vs Lists | ||
| Dot Product | ||
| Speed Test | ||
| Matrices | ||
| Solving Linear Systems | ||
| Generating Data | ||
| Numpy Exercise | ||
| Where to Learn More Numpy | ||
| Suggestion Box | ||
| 3. | Matplotlib (New) (35 minutes) | Matplotlib Section Introduction |
| Line Chart | ||
| Scatterplot | ||
| Histogram | ||
| Plotting Images | ||
| Matplotlib Exercise | ||
| Where to Learn More Matplotlib | ||
| 4. | Pandas (New) (26 minutes) | Pandas Section Introduction |
| Loading in Data | ||
| Selecting Rows and Columns | ||
| The apply() Function | ||
| Plotting with Pandas | ||
| Pandas Exercise | ||
| Where to Learn More Pandas | ||
| 5. | Scipy (New) (17 minutes) | Scipy Section Introduction |
| PDF and CDF | ||
| Convolution | ||
| Scipy Exercise | ||
| Where to Learn More Scipy | ||
| 6. | Bonus Exercises (08 minutes) | More Exercises |
| 7. | Beginner Troubleshooting (13 minutes) | What if I don’t meet the math prerequisites? |
| 8. | Machine Learning Basics (01 hour 33 minutes) | Machine Learning: Section Introduction |
| What is Classification? | ||
| Classification in Code | ||
| What is Regression? | ||
| Regression in Code | ||
| What is a Feature Vector | ||
| Machine Learning is Nothing but Geometry | ||
| All Data is the Same | ||
| Comparing Different Machine Learning Models | ||
| Machine Learning and Deep Learning: Future Topics | ||
| Machine Learning Section Summary | ||
| 9. | Setting Up Your Environment (FAQ by Student Request) (37 minutes) | Anaconda Environment Setup |
| How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow | ||
| 10. | Extra Help With Python Coding for Beginners (FAQ by Student Request) (17 minutes) | Python 2 vs Python 3 |
| Proof that using Jupyter Notebook is the same as not using it | ||
| 11. | Effective Learning Strategies for Machine Learning (FAQ by Student Request) (27 minutes) | Machine Learning and AI Prerequisite Roadmap (pt 1) |
| Machine Learning and AI Prerequisite Roadmap (pt 2) | ||
| 12. | Appendix / FAQ Finale (05 minutes) | BONUS |
Resources Required
- Understand the Gaussian distribution and linear algebra
- Have Python coding experience
- You should already be familiar with the concepts like the dot product, matrix inversion, and Gaussian probability distributions.
Featured Review
Nikhil Agarwal (5/5): This course is perfectly aligned with what I needed and the lessons are short and crisp with good knowledge. The course content and hands on coding perfectly helped me to gain required knowledge to build machine learning algorithms from scratch.
Pros
- Nakul Patel (5/5): Thank you for providing the best course with hands on example and sharing the knowledge of Numpy.
- Ilija Simonovic (5/5): This is a great course! It introduces the fundamentals of Python libraries that are used in machine learning algorithms.
- ChengHsien Chen (5/5): Although this course is marking as numpy stack, it is really perfect and helpful for guys to re-catch the concept and implementation details after any machine/deep learning courses.
- Gopal Agrawal (5/5): Btw – I found Miniconda was the best way to get started on Windows.
Cons
- H J (2/5): I have to say that this is not good at all and the reason can be summarized in one sentence: No reference material.
- Qian Zhu (1/5): I expect to learn EVERYTHING only by watching videos and reading 0 documentation, text sucks, I understand it’s not practical, therefore I’m in a bad mood, so one star.
- Eric Schlosser (1/5): No one working on any machine learning project for work or a competition website like Kaggle will ever hard code a dot product.
- Eric Schlosser (1/5): That’s what I’ve been having a hard time with and it’s why I took this course.
About the Author
The instructor of this course is Lazy Programmer Inc. who is an Artificial intelligence and machine learning engineer. With a 4.6 instructor rating and 1,48,231 reviews on Udemy, he offers 33 courses and has taught 5,26,687 students so far.
- Although he has also been recognised as a data scientist, big data engineer, and full stack software engineer, he currently spends the majority of his time as an artificial intelligence and machine learning engineer with an emphasis on deep learning.
- He earned his first master’s degree in computer engineering with a focus on machine learning and pattern recognition more than ten years ago.
- He was awarded his second master’s degree in statistics with a focus on financial engineering.
- He routinely uses big data technologies like Hadoop, Pig, Hive, MapReduce, and Spark.
- He has developed deep learning models for text modelling, image and signal processing, user behaviour prediction, and click-through rate estimation.
- In his work with recommendation systems, he has used collaborative filtering and reinforcement learning and validated the findings using A/B testing.
- He has taught students at universities like Columbia University, NYU, Hunter College, and The New School in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics.
- His web programming skills have helped numerous businesses.
- He handles all of the server-side backend work, frontend HTML/JS/CSS work, and operations/deployment work.
Comparison Table
| Parameters | Deep Learning Prerequisites: The Numpy Stack in Python (V2+) | Artificial Intelligence: Reinforcement Learning in Python | Advanced AI: Deep Reinforcement Learning in Python |
|---|---|---|---|
| Offers | INR 449 ( | INR 455 ( | INR 455 ( |
| Duration | 6 hours | 14.5 hours | 10.5 hours |
| Rating | 4.6/5 | 4.8 /5 | 4.6 /5 |
| Student Enrollments | 2,47,555 | 43,633 | 36,717 |
| Instructors | Lazy Programmer Inc. | Lazy Programmer Team | Lazy Programmer Team |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
Leave feedback about this