Hi, I'm Mohamed Najm

A passionate Data analysist from Egypt

• I’m currently working on Machine Learning Techniques Implementation as you can see on this repo.

Introduction

ML Techniques are important topics to learn and implement so, I did that. Here you will found all the notebooks and all the datasets which have used.

What I want to say

AS a beginner in ML you always find implementing of all this techniques difficult. As a passionate Data analysist and a long-time self-taught learner. I do understand the hard time you spend on understanding new concepts and debugging your program. Here I released these solutions, It may help you to save some time. And I hope you implement they by yourself not just copy any part of the code.

Machine Learning Techniques Implementation

• Here you will found implementation for those topics:

• Linear Regression

• Logistic Regression

• Decision Trees and Random Forest

• K Means Clustering

• K Nearest Neighbors

• SVM

• PCA

And there are 3 revelant notebooks which are an implementation either but for NLP consepts and a simple Recomendation system.

First, NLP:

Part 1

• NLP (Natural Language Processing) Implementation
  • In this notebook we will discuss a higher level overview of the basics of Natural Language Processing, which basically consists of combining machine learning techniques with text, and using math and statistics to get that text in a format that the machine learning algorithms can understand! then apply that on SMSSpamCollection dataset to predict if the massage ham or spam.

Part 2

• NLP - Natural Language Processing Implementation 2
  • In this NLP project you will be attempting to classify Yelp Reviews into 1 star or 5 star categories based off the text content in the reviews. we will utilize the pipeline methods for more complex tasks. We will use the Yelp Review Data Set from Kaggle.

Secand, Recommender System.

• Recommender Systems Implementation
  • In this notebook, we will focus on providing a basic recommendation system by suggesting items that are most similar to a particular item, in this case, movies. Keep in mind, this is not a true robust recommendation system, to describe it more accurately,it just tells you what movies/items are most similar to your movie choice.

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