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

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.

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.

• 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.

• 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.

• 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.

• 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.