Experience the need & intuition of language features from machine learning and project driven perspective. Get the self-confidence to code any machine learning, deep learning and math logics in python by working on real python mini projects.
Python got very wide acceptance among the AI community for any type of data science work. The Python Language for AI Course aims for an experiential and joyful journey with the goal of building AI software with confidence. We take an experiential approach with the following key ideas for this course:
1) People get messed up in language learning by properly byhearting the syntaxes and syntax examples without knowing the essence of features. In this course, we do not encourage syntax to byheart by any means and instead, make you realize the intent of the feature with practical experiences.
2) People learn a language with approaches like note writing, studying the theory of each feature with an example and think that they know the language. You know the language better and start enjoying the language only when you start developing projects with what you learned. In this course, we provide a series of useful projects that makes you experience the joy of software building.
3) Most people byheart FAQs to clear job tests/interviews. By luck, even if u get a job, you feel a nightmare in writing real software. In this course, you are given assignments or interview questions for each topic after you have a practical understanding of the topic. You should able to think logically and solve those questions. This gives you an enormous self-confidence in job interviews and also real software development in a company.
The course covers the following topics in-depth:
| Framework for Language Learning | 12 Min | ||
| Curriculum for Language Learning | 16 Min |
| Why Python? | 9 Min | ||
| Environment Setup | 14 Min | ||
| Jupyter Classic Notebook vs Jupyter Lab Notebook | 11 Min | ||
| Working with jupyter lab | 11 Min | ||
| Working with spyder | 6 Min | ||
| Language Basics || Interview Questions |
| Overview of Types | 8 Min | ||
| Primitive Types | 9 Min | ||
| Operations of Primitive Types | 20 Min | ||
| Custom Types | 9 Min | ||
| Type Casting | 7 Min | ||
| Static vs Dynamic Typing | 8 Min | ||
| Download Code |
| Overview of Programming Styles | 5 Min |
| Overview of Procedural Style | 5 Min | ||
| Control Statements | 18 Min | ||
| Need of Functions | 9 Min | ||
| Functions with Optional Arguments | 8 Min | ||
| Functions with Testability | 6 Min | ||
| Download Code | |||
| Type System & Procedural Programming Style || Interview Questions |
| Writing Variable Argument Functions | 12 Min | ||
| Download Code |
| Overview of Data Structures | 6 Min |
| List Overview | 4 Min | ||
| Working with List | 24 Min | ||
| List Applications in Python | 15 Min | ||
| Download Code |
| Working with Tuple | 8 Min | ||
| Download Code | |||
| List & Tuple Data Structures || Interview Questions |
| Stack & Queue Overview | 3 Min | ||
| Working with Stack & Queue | 13 Min | ||
| Stack & Queue Applications in Python | 9 Min | ||
| Download Code |
| Set Overview | 3 Min | ||
| Working with Set | 19 Min | ||
| Set Applications in Python | 6 Min | ||
| Download Code |
| Dictionary Overview | 3 Min | ||
| Working with Dictionary | 13 Min | ||
| Dictionary Applications in Python | 10 Min | ||
| Download Code | |||
| Set & Dictionary Data Structures || Interview Questions |
| Overview of 1D-Array | 3 Min | ||
| Need of Numpy Array | 18 Min | ||
| Basic Operations of Numpy Array | 23 Min | ||
| Broadcasting & Vectorization in Depth | 15 Min | ||
| Vectorized Aggregate Operations | 7 Min | ||
| Vectorized Relational Operations | 10 Min | ||
| Download Code |
| Overview of 2D-Array | 4 Min | ||
| Creation & Operations | 18 Min | ||
| Vectorized Operations | 14 Min | ||
| Download Code | |||
| Numpy Array Data Structure || Interview Questions |
| Overview of Tensor | 3 Min | ||
| Working with Tensors | 9 Min | ||
| Download Code |
| Overview of Data Frames | 4 Min | ||
| Series | 25 Min | ||
| Dataframe Basics | 36 Min | ||
| Aggregates & Grouping | 20 Min | ||
| Combining Dataframes | 22 Min | ||
| Reshape & Sorting | 18 Min | ||
| Download Code | |||
| Data Frame Data Structure || Interview Questions |
| Overview of Object Oriented Style | 11 Min | ||
| Overview of Encapsulation, Datahiding | 5 Min | ||
| Experience with Encapsulation - Procedural Style | 17 Min | ||
| Experience with Encapsulation - OO style | 14 Min | ||
| Overview of Polymorphism, Dynamic Binding | 8 Min | ||
| Experience with Polymorphism - Procedural Style | 6 Min | ||
| Experience with Polymorphism - OO Style | 9 Min | ||
| Download Code |
| Overview of Reuse | 5 Min | ||
| Experience with Reuse - Inheritance I | 11 Min | ||
| Experience with Reuse - Inheritance II | 14 Min | ||
| Experience with Reuse - Inheritance III | 11 Min | ||
| Experience with Reuse - Composition | 19 Min | ||
| Overview of Modeling | 4 Min | ||
| Experience with Modeling - Procedural Style | 17 Min | ||
| Experience with Modeling - OO Style | 9 Min | ||
| Download Code | |||
| Object Oriented Style || Interview Questions |
| Overview of Functional Programming | 10 Min | ||
| Pure Functions | 14 Min | ||
| Functions as Type | 22 Min | ||
| Declarative Programming-I | 11 Min | ||
| Declarative Programming-II | 11 Min | ||
| Declarative Programming-III | 10 Min | ||
| Performance Optimization-I | 11 Min | ||
| Performance Optimization-II | 14 Min | ||
| Debugging and Testing Pure Functions | 8 Min | ||
| Download Code | |||
| Functional Programming Style || Interview Questions | |||
| Programming Styles || Interview Questions |
| Overview of Modularity | 4 Min | ||
| Python Files as Modules | 12 Min | ||
| Organizing Modules | 7 Min | ||
| Importing Modules | 6 Min | ||
| Exploring Modules | 10 Min | ||
| Download Code |
| Using Random Generators | 12 Min | ||
| Download Code |
| Overview of exception handling | 3 Min | ||
| Need of exception handling | 11 Min | ||
| Working with Try & Except blocks | 12 Min | ||
| Working with Finally block | 9 Min | ||
| Custom Exceptions | 12 Min | ||
| Download Code | |||
| File IO || Interview Questions |
| Need of Data Streaming | 7 Min | ||
| Object Oriented Iterators in Python | 13:06 Min | ||
| Lazy Evaluation of Iterators | 6 Min | ||
| Internals of Object Oriented Iterators | 10 Min | ||
| Functional Iterators or Generators - I | 12 Min | ||
| Functional Iterators or Generators-II | 9 Min | ||
| Out of Box Functional Iterators | 24:48 Min | ||
| Download code | |||
| Data Streaming || Interview Questions |
| Password File Cracker | |||
| Document Search Engine |
| Currency Converter | |||
| YouTube Stats Scraper | |||
| Facebook Friend Recommender |
| Lifecycle of Compiled Programs | 23 Min | ||
| Experience Compiled Program Lifecycle | 10 Min | ||
| Lifecycle of Interpreted Programs | 20 Min | ||
| Experience Interpreted Program Lifecycle | 4 Min | ||
| Lifecycle of Hybrid(Compiled+Interpreted) Programs | 16 Min | ||
| Experience Hybrid(Compiled+Interpreted) Program Lifecycle | 5 Min | ||
| Virtual Machines with JIT Compiler | 15 Min |
| Static Linking | 16 Min | ||
| Experience Static Linking | 14 Min | ||
| Dynamic Linking | 6 Min | ||
| Experience Dynamic Linking | 12 Min | ||
| Summary of Static & Dynamic Linking | 4 Min |
| Debugging with Jupyter | 10 Min | ||
| Profiling with Jupyter | 13 Min | ||
| Download Code |
ThimmaReddy is the founder of Algorithmica and holds Master Degree from IIT-Guwahati. He believes that education means training of the mind to think and solve problems with direct experience. Prior to founding Algorithmica, ThimmaReddy worked at LSI Logic, Agami Systems, Applied Discovery & some other startups as an Engineer, Architect & AI Strategist. He strongly asserts that the current education system is outdated & needs complete overhaul to upbring the people to solve the problems posed by companies & research.
To get guranteed success in any area of skill, he advocates three needed things: scienitifc curriculum, experiential learning & mentorship. At Algorithmica, he transformed thousands of students and working professionals to quality thinkers and he also created different scientifc curriculum for different pressing problems faced by students, working professionals and companies. ThimmaReddy loves to explore every aspect of computing field and is very passionate to share his experiential knowledge too.
His Alumni works in top-notch companies like Google, Mircosoft, Facebook, Amazon, Uber, WallMart Labs, LinkedIn, Twitter, etc., spread around the world. He is now acting as mentor for some startup AI companies and in parallel leading algorithmica for THE ultimate destination for quality experiential learning in whole computational field/domain.
