Machine Learning

Created by Saif Abid, Chief Technology Officer at bitstrapped
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Used NLP
Used Machine Learning
Wrote a technical article
Machine translation
Repost Blog
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Abid Ali Awan is back with a great tutorial on a simple machine translation of Yorùbá to English! 🤩🚀 Check out how he got his top 20 ranking on the leaderboard, and try it out yourself!
#KeepLearning👉https://bit.ly/310Ah3O
Wrote a technical article
Used Machine Learning
Building MLOps pipelines
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Writer Creators Club, Analytics Vidhya
Started 2 days ago
Studying Machine Learning
Currently reading and studying Markov Decision Processes. 

After seeing search problems as a deterministic way to solve problems, we know that the world is not so certain, and here it comes the stochastic/randomness of the world. 

Ideally, a Markov decision process is pretty similar to a search problem, with some differences such as:

  • Set of states 
  • Set of actions that will help you go to one state to another. 
  • Transition model, as the probability that given you are in state s and perform action A you end up in state s’
  • Policy, is the recommended action in any state 
  • A reward function, is the reward you obtain  after transitioning from one state to another. 
There is more to it and I‘ll write more about it as I keep learning. 

It’s a fascinating topic and additionally the fundamentals of Reinforcement Learning 
Studying Machine Learning
Studying Graph theory
I've been crazy busy with my new course 😁 

Currently I'm learning how you can define a search problem and use any search algorithm and it's like magic happens it will return the best path for the solution you need. 

A search problem definition:

- A set of states, that will help you to model th current state of the world and all its possible combinations

- A set of actions, that you might perform at any state to help you reach the goal

- A successor function, that based on certain initial and end state will help you transition from one state to the next state, and it has a cost associated to it

- An initial state, where are you starting in your problem. The initial representation of where you are. 

- An end state, will help you validate if you have reach the goal. It's also called the goal test 

This topic is really interesting as it will help you define any problem you might have and work through a solution using search algorithms. 

In between search algorithms we have seen in class so far:

  • DFS or Deep first search.
  • BFS or Breath First Search.
  • DFS with iterative deepening, a combination of DFS with BFS.
  • UCS, or Universal Cost Search 
  • A* , similar to UCS but use heuristic functions and move in the direction towards a goal.

Used Machine Learning
Building MLOps pipelines
Wrote a Blog Post
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A Guide to Machine Learning Pipelines and Orchest


Learn how machine learning pipelines are used in productions and design your first pipeline using simple steps on disaster tweets classification datasets. You will also learn how to ingest the data, preprocess, train, and eventually evaluate the results.

In this guide, we will learn the importance of Machine Learning (ML) pipelines and how to install and use the Orchest platform. We will be also using Natural Language Processing beginner problem from Kaggle by classifying tweets into disaster and non-disaster tweets. The ML pipelines are independently executable code to run multiple tasks which include data preparation and training machine learning models. The figure below shows how each step has a specific role and how tracking those steps are easy. Azure Machine Learning



A Guide to Machine Learning Pipelines and Orchest | by Abid Ali Awan | Oct, 2021 | Towards AI
Technical Writer , Towards AI
Data Analysis
Used Machine Learning
Wrote a Blog Post
DataCamp Assesment
Deepnote Notebook
+ 3
The main goal is to find the next ten locations similar to Saint Petersburg using unsupervised learning.

Designing a Promotional Strategy for Alcoholic Drinks in Russia | by Abid Ali Awan | Nov, 2021 | Towards AI
Technical Writer , Towards AI
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