Complete Advanced Python with Projects— Mega Compilation Part 6
Connect the dots…
Welcome back peeps. This post( part 6) is all about Advanced Python, Data Science and Machine Learning Projects that you can build to practically understand the concepts.
Complete Python And Projects — Mega Compilation
All the Complete System Design Series Parts —
6. Networking, How Browsers work, Content Network Delivery ( CDN)
Projects Videos —
All the projects, data structures, SQL, algorithms, system design, Data Science and ML , Data Analytics, Data Engineering, , Implemented Data Science and ML projects, Implemented Data Engineering Projects, Implemented Deep Learning Projects, Implemented Machine Learning Ops Projects, Implemented Time Series Analysis and Forecasting Projects, Implemented Applied Machine Learning Projects, Implemented Tensorflow and Keras Projects, Implemented PyTorch Projects, Implemented Scikit Learn Projects, Implemented Big Data Projects, Implemented Cloud Machine Learning Projects, Implemented Neural Networks Projects, Implemented OpenCV Projects,Complete ML Research Papers Summarized, Implemented Data Analytics projects, Implemented Data Visualization Projects, Implemented Data Mining Projects, Implemented Natural Leaning Processing Projects, MLOps and Deep Learning, Applied Machine Learning with Projects Series, PyTorch with Projects Series, Tensorflow and Keras with Projects Series, Scikit Learn Series with Projects, Time Series Analysis and Forecasting with Projects Series, ML System Design Case Studies Series videos will be published on our youtube channel ( just launched).
Subscribe today!
System Design Case Studies — In Depth
Design Instagram
Design Messenger App
Design Twitter
Design URL Shortener
Design Dropbox
Mega Compilation : Solved System Design Case studies
Github —
Day 1: Intermediate Python with Code Implementation — Part 1
In this post we covered end to end Intermediate Python ( Part 1) that you should know. Topics like First Class functions, Private Variables, Global and Non Local Variables, __import__ function, Magic Functions, Tuple Unpacking, Static Variables and Methods in Python are covered in detail.
Highly Recommended Data Science and Machine Learning Courses that you MUST take ( with certificate) —
Find best data science and data engineering courses here
Find best Machine Learning and Deep Learning courses here
Day 2: Intermediate Python with Code Implementation — Part 2
In this post we covered end to end Intermediate Python( Part 2) that you should know. Topics like Lambda Functions, Magic methods, Inheritance and Polymorphism, Errors and Exception Handling, User-defined functions, Python garbage collection, and debugger are covered in detail.
Day 3 : Advanced Python with Code Implementation
In this post we covered end to end Advanced Python that you should know. Topics like Decorators, Memoization using Decorators, Generators, Ordered and Defaultdict, Coroutine with Code implementation are covered in detail.
Day 4:Python Iterators, Generators And Decorators Made Easy
In this post we covered Iterators, Generators And Decorators in detail.
Day 5:Advanced Python Made Easy — Part 1
In this post we covered List comprehensions, default dict, Named Tuple etc
Day 6:Advanced Python Made Easy Part — 2
In this part we covered some advanced python constructs that will help you write efficient code, improve code readability and performance.
Day 7:Advanced Python Made Easy — Part 3
In this post we covered function annotations, Dictionaries, Chain Map etc in detail.
Day 8:Python — Map, Classes, Functions and Arguments with Implementation
In this post we covered maps, classes, functions and Arguments etc in detail.
Day 9:Advanced Python Made Easy — Part 4
In this post we covered iteratools and chainmap in detail.
Day 10:Python’s F-Strings
Python F-String are used to embed python expressions inside string literals for formatting, using a minimal syntax. It’s an expression that’s evaluated at the run time. They have the f prefix and use {} brackets to evaluate values. f-strings are faster than %-formatting and str.format().
Day 11:Underscores(_) in Python
Underscores are unique characters in Python and helps users write code productively. In this post we explored how you can use single and double underscore in your code.
Day 12:Writing Efficient Python Code — Part 1
In this post we covered how to write efficient python code.
Day 13 :Writing Efficient Python Code — Part 2
In this post we covered how to write efficient python code.
Writing Efficient Python Code — Part 2
Use these hacks and techniques…
medium.datadriveninvestor.com
Day 14:Must Learn Python Libraries — Part 1
In this post we covered some very useful Python libraries in detail.
Day 15:Advanced Python Made Easy — Part 5
In this post we covered Filter, Map , Reduce etc in detail.
Day 16:Complete Python Strings — Part 1
In this post we covered Python strings in detail.
Day 17:Python Advanced Modules Made Easy — Part 1
In this post we covered import functions, magic methods, Keyword Arguments etc in detail.
Day 18:Advanced Python Made Easy — Part 6
Day 19:Advanced Python Made Easy — Part 7
In this post we covered Python Regular Expressions etc in detail.
Day 20:60 days of Data Science and Machine Learning Series — Day 1
In this post we covered Python for Data Science and ML in detail.
Day 21:Python Crash Course
Day 22:Efficient Code and Optimization techniques for Python
In this post we covered some really good tips and techniques to write efficient Python Code.
Day 23 :Four “Lesser Known” Python Libraries for Data Science
Day 24 :Projects —
Complete Python And Projects — Mega Compilation
Cluster Analysis using Python — Part 1
Analyzing Video using Python, OpenCV and NumPy
Data Science and ML series —
Part 1 of this mega series ( Day 0 — Day 20) can be found here —
Part 2 of this mega series ( Day 21 — Day 35) can be found here —
Part 3 of this mega series ( Day 36 — Day 48) can be found here —
Part 4 of this mega series ( Day 49 — Day 62) can be found here —
Part 5 —
Some of the other best Series —
100 days : Your Data Science and Machine Learning Degree Series with projects
Complete Data Visualization and Pre-processing Series with projects
Tech Newsletter —
If you are interested, you can join my newsletter through which I send tech interview tips, techniques, patterns, hacks — Software Development, ML, Data Science, Startups and Technology projects to more than 30K readers. You can subscribe to Tech Brew :
More Projects —
Complete Python And Projects — Mega Compilation
Complete Data Preprocessing and Data Visualization with Projects — Mega Compilation Part 2
Maths —
Statistics for Data Science and Machine Learning with Code Implementation
Maths for Data Science and Machine learning
In this post we covered Maths for ML . Topics like Linear Algebra, Calculus, Matrix and Vectors, Bayes Theorem and Cheatsheets etc are covered in detail.
For other projects, tune to —
Build Machine Learning Pipelines( With Code)
Recurrent Neural Network with Keras
Custom Layers in Keras
Some of the links are affiliates.
Follow for more updates, stay tuned and of-course let me end this post with a quote by Steve Jobs ;)
“Your time is limited, so don’t waste it living someone else’s life.”
