Mindful Machines

  What is a Decision Tree A Decision Tree is a type of supervising algorithm which indicates that in the training set you know the input variable ( features ) and their corresponding target. Decision Tree can operate as both classifiers or regressors. In this method, we split the dataset into two or more uniform sets based on the most significant splitter/differentiator in the dataset features. Example:- Let’s say we have a sample of 50 studen t s with three variables Gender (Boy/ Girl), Class( X/ XI) and Height (5 to 6 ft). 20 out of these 50 play cricket in rest time. Suppose you want to find on unknown dataset which contains all the features(Gender, class, height) that he/she will play or not in rest time. This is where decision tree supports, it will separate the students based on all values of three variable and identify the variable, which creates the best uniform sets of students like below: Terms you should know Root Node:  It describes the complete dataset and this ad...

  Random Forest Random Forest is a Machine Learning Algorithm based on Decision Trees. Random forest works on the ensemble method which is very common these days. The ensemble method means that to make a decision collectively based on the decision trees. Actually, we make a prediction, not simply based on One Decision Tree, but by an unanimous Prediction, made by ‘ K’  Decision Trees. Why should we use There are four reasons why should we us e  the random forest algorithm. The one is that it can be used for both  classification and regression  businesses. Overfitting is one critical problem that may make the results worse, but for the Random Forest algorithm, if there are enough trees in the forest, the classifier  won’t overfit  the model. The third reason is the classifier of Random Forest can handle  missing values , and the last advantage is that the Random Forest classifier can be modeled for  categorical values. How does the Random...

  Bird’s eye view To become good at something we need to start from the beginning so if you want to expert in Machine learning / Data Science / Artificial Intelligence you will have to start from the beginning. Some words from the experts: “ Everyone is learner so don't panic go with flow”. For newcomers, programming languages like python, statistics, machine learning algorithms. For transitional, advanced machine learning algorithms are required to be understood. Practicing with datasets and an online kaggle profile are helpful in practicing your skills. Learning Path — Schedule for you Getting started →Statistics → Programming language →Basic of ML →Practice. It will take you 6–7 months to be an expert or as an expert then you can compete anywhere in the competition. Let’s talk about the steps of how you should proceed Getting Started: For this, you have to know why do you want to learn ML or data science, for doing that just connect with experienced people in the field of ML or ...

  So in my opinion to be an expert in machine learning like the other expert geeks one should know the listed below algorithms : Naive Bayes Algorithm Linear Regression Algorithm Logistics Regression Algorithm KNN algorithm Decision Tree Random Forest SVM K means Algorithm Artificial Neural Networks Learn about the above algorithms and you can also be an expert in machine learning like others in a few days.

  If you all guys out there looking for tips and tricks about how to get a data science job or internship you want to apply for? Before telling you how to get you should know how to be an expert in it so for that please read my  article . You have come to the right place! Nowadays reports and publications consistently name ‘data scientist’ as one of the preferable jobs. While there are many articles about the set of skills you need to get the data scientist position, we wanted to focus on the students who crave working in this prosperous field. The data science will be   a good job if you want to get chill in your life because according to many articles it will be the best job in 2020. Just follow the below steps for getting jobs/ internship Learn about basic stuff you do not need to research topics. so read-only about regression or classification and from top neural network. That will be enough study for fresher to know, another thing you can learn on the job. After know...

  One should know how to play with data First of all the main thing, we need to be focused on before stepping into machine learning is that we should know how to play with data. Because believe all the geeks sitting out there doing machine learning and other cool stuff is all because they know how to play with data. Note : Hereby playing with data means is you should know how to manipulate the data or reshape the data into something that model can understand and make sense from it and able to predict the new value. So what one   fresher or maybe who know something but not pro should need for playing with data is only some tools like Pandas — For Python By pandas, one can do anything that he/she can do with data. literally anything. Applying ML Models After you need the data you wanted to feed the model you will have to do only 2% of the work you did for reshaping the data. Because now you have to use  2 golden words of machine learning FP .F it() .P redict() It will be go...

  As in the previous article   Airflow for beginners — I   we learn about the airflow and how to install the airflow on local. In this article, we will learn about the DAG and how to use the web airflow for scheduling the work and managing it. Let’s start with the DAG and then we will learn about the web airflow. DAG (Directed Acyclic Graph) DAG is a directed graph without a cycle in it. It has a finite number of edges and nodes. In which we can know which thing(node) will come first and we will know the sequence of the nodes. so just think as each node is a work( that you want to execute ) so we can represent the sequencing of the work by the DAG and that’s why DAG is an essential part of the airflow. WEB AIRFLOW So now as you saw that on localhost:8080 if you open it will look like: now, these are the example DAG or work that is added already so for adding your own script(DAG) just follow simple steps mentioned below: Just go to the airflow directory in terminal (it wil...