Machine Learning Interview

In this blog, we have discussed the idea of Big Data, which addresses the challenge of managing vast amounts of complex and diverse data. We discussed what constitutes Big Data, its types, characteristics, examples, use cases, the various technologies used, advantages, and the challenges faced by engineers working with Big Data.

Here are 7 key steps to master data science: 1) Learning Python 2) Understanding big data frameworks like Hadoop and PySpark 3) Learning concepts of APIs, databases and SQL 4) Hands-on experience in data analysis and visualization 5) Learning statistics, probability, and machine learning 6) Building data science projects 7) Making resume and applying for data scientist positions

Artificial Neural Networks (ANNs) are used to forecast the stock market price. Based on that, Traders take a decision on whether to buy or sell any stock. In this blog, we will be building a forecasting technique for Amazon stock prices using 1 and 2 hidden-layer neural networks. We will evaluate and compare the performance of ANN with the traditional SVM model.

Customer churn is the term used to describe customers who might stop using the company's products or services. Netflix, telecom, or other subscription-based companies use machine learning algorithms to predict the customer churn rate. In this article, we have used a decision tree to build a machine learning model to predict customer churn rate and achieved an accuracy of 83%.

Perceptron is the most fundamental unit of Neural Network architecture in Machine Learning. In this article, we will learn to design a perceptron from scratch in Python to make it learn the properties of AND, OR and XOR logic gates. We will observe the need for multiple layer perceptron (MLP) over single layer perceptron.

Feature engineering is the process of selecting, correcting, and generating new features from existing attributes in a dataset to optimize the performance of machine learning models. The main steps involved are feature creation, transformation, extraction, and selection. To improve results, we can employ techniques such as imputation, transformation, scaling, and encoding, which will be discussed in this blog.