Learn Data Science, Machine Learning and Artificial Intelligence
In this we will learn from scratch how to implement decision tree using python. We will solve one classification problem and build the model from scratch. Following are the points we will be covering in this post:
Exploratory Data Analysis – EDA
Data Visualization
Data Pre-processing
Data Spliting- Stratified Sampling
Oversampling – SMOTE
Model Training
Fine Tuning
Hyper parameter Tuning
ML FAQ Part 1
What is ROC-AUC curve and how to interpret it?
Explain Type I and Type II errors.
What is Precision and Recall?
What is F1 score?
What is the difference between r-square and adjusted r-square values?
This post is part 2 in the series of frequently asked Machine Learning Interview Questions and Answers. Machine Learning Frequently asked Interview Questions and Answers Part 2
1. What is Feature Scaling and why and where it is needed?
2. Normalization vs Standardization
3. What is the bias-variance trade-off?
4. Define the Overfitting problem and why it occurs?
5. What are the methods to avoid Overfitting in ML?
As of today, FastAPI is the most popular web framework for building microservices with python 3.6+ versions. By deploying machine learning models as microservice-based architecture, we make code components re-usable, highly maintained, ease of testing, and of-course the quick response time. FastAPI is built over ASGI (Asynchronous Server Gateway Interface) instead of flask’s WSGI (Web Server Gateway Interface). This is the reason it is faster as compared to flask-based APIs.
In the field of Machine Learning, logistic regression is still the top choice for classification problems. It is simple yet efficient algorithm which produces accurate models in most of the cases. In its basic form, it uses the logistic function to calculate the probability score which helps to classify the binary dependent variable to its respective class. Logistic regression is the transformed form of the linear regression. In this post I have explained the end to end step involved in the classification machine learning problems using the logistic regression and also performed the detailed analysis of the model output with various performance parameters.
Artificial neural networks (ANNs), usually called neural networks (NNs), are computing systems vaguely inspired by the biological neural networks that constitute animal brains. There is lot of hype these days regarding the Artificial Intelligence and its technologies.
In this article, we will talk about the Hype vs Reality on AI technologies and also will explain about the various terminology associated with Artificial Intelligence (AI), Transitioning from Machine Learning to Deep Learning, Basic building blocks for the study of AI and Artificial Neural Network (ANN).
