# Law of Total Probability – Statistics Part 19

Hey Developer’s, I’m back with a new topic which is Law of Total Probability in the series of statistics foundations.

# Multiplicative and Additive Law Of Probability – Statistics Part 18

Hey Developer’s, I’m back with a new topic which is Multiplicative and Additive Law Of Probability in the series of statistics foundations.

# Conditional and Unconditional Probability – Statistics Part 16

Hey Developer’s, I’m back with a new topic which is Conditional and Unconditional Probability in the series of statistics foundations.

# Probability of Union Of Events – Statistics Part 15

Hey Developer’s, I’m back with a new topic which is Probability of Union Of Events in the series of statistics foundations.

# Multinomial Coefficients – Statistics Part 14

Hey Developer’s, I’m back with a new topic which is Multinomial Coefficients in the series of statistics foundations.

# Combinatorics – Statistics Part 12

Hey Developer’s, I’m back with a new topic which is Combinatorics in the series of statistics foundations.

# Permutations – Statistics Part 11

Hey Developer’s, I’m back with a new topic which is Permutations in the series of statistics foundations.

# Counting Sample Points – Statistics Part 10

Hey Developer’s, I’m back with a new topic which is Counting Sample Points in the series of statistics foundations.

# Discrete and Continuous Probability – Statistics Part 9

Hey Developer’s, I’m back with a new topic which is Discrete and Continuous Probability in the series of statistics foundations.

# Rules of Probability – Statistics Part 8

Hey Developer’s, I’m back with a new topic which is Rules of Probability in the series of statistics foundations.

# Introduction To Probability and Examples – Statistics Part 7

Hey Developer’s, I’m back with a new topic which is Introduction To Probability and Examples in the series of statistics foundations.

# Set Operations On Events – Statistics Part 5

Hey Developer’s, I’m back with a new topic which is Set Operations On Events in the series of statistics foundations.

# Experiments, Events, Sample Spaces & Points – Statistics Part 4

Hey Developer’s, I’m back with a new topic which is Experiments, Events, Sample Spaces & Points in the series of statistics foundations.

# Cardinality, Set Complement and Set Laws – Statistics Part 3

Hey Developer’s, I’m back with a new topic which is Cardinality, Set Complement and Set Laws in the series of statistics foundations.

# Set Membership & Set Operations – Statistics Part 2

Hey Developer’s, I’m back with a new topic which is a Sets Membership in the series of statistics foundations.

# Introduction To Sets – Statistics Part 1

Hey Developer’s, I’m back with a new topic which is an introduction to sets in the series of statistics foundations.

# Statistics Foundation Series: For Data Science & Machine Learning

Hey Developer, I’m back with a new series of blogposts on the foundation of statistics for data science and machine learning. Here will be covering all the topics that are essential for good understanding of data science and machine learning concepts.

# Introduction To Decision Trees – Part 1

Decision Tree is a type of Supervised Learning Algorithm wherein the data is continuously split on the basis of certain parameters. To understand the decision tree in a better way let’s take an example

# A-Z Machine Learning Resources

Machine learning resources containing Deep Learning, Machine Learning and Artificial Intelligent resources. A-Z Machine learning resources to learn machine learning.

# Affine Transformation- Image Processing In TensorFlow- Part 1

Affine Transformation helps to modify the geometric structure of the image, preserving parallelism of lines but not the lengths and angles. It preserves collinearity and ratios of distances. It is one type of method we can use in Machine Learning and Deep Learning for Image Processing and also for Image Augmentation.

# What is AI, ML and DL?

AI, ML and DL are related to each other. AI is a superset of ML and DL. What we do in the field of ML and DL all comes under AI. To better understand all of them, Let’s dive in…

# Hyperparameters in Machine Learning

A hyperparameter is a parameter or a variable we need to set before applying a machine learning algorithm into a dataset.These parameters express “High Level” properties of the model such as its complexity or how fast it should learn. Hyperparameters are usually fixed before the actual training process begins.

# Transfer Learning With MobileNetV2

In this notebook we will be learning how to use Transfer Learning to create the powerful convolutional neural network with a very little effort, with the help of MobileNetV2 developed by Google that has been trained on large dataset of images.

# Is There a Standard Heuristic for Model Tuning?

Training error should steadily decrease, steeply at first, and should eventually plateau as training converges.If the training has not converged, try running it for longer.

# TensorFlow Core 2.0 Guide

TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications such as neural networks.

# Rules of calculus — Multivariate

In the real world, it is very difficult to explain behavior as a function of only one variable, and economics is no different.

# What is Deep Learning?

Deep Learning is a subfield of Machine Learning because it makes use of Deep Neural Networks inspired by the structure and function of the brain called Artificial Neural Networks.

# Linear Regression In ML

Regression is basically a statistical approach of finding a relationship between the variables. Linear regression is one type of regression we use in Machine Learning.

# 15 Best Machine Learning Course in 2019

Here are 15 Best Machine Learning Course for Machine Learning. It will give you the great knowledge about Machine Learning and Deep Learning.

# How Computers Interpret Images?

We all love to see beautiful images, but have you ever thought how do computers see an image? In this tutorial, we will give an explanation of how images are stored in a computer.

# Applications Of Convolutional Neural Networks

CNN’s achieve state of the art results in the variety of problem areas including Voice User Interfaces, Natural Language Processing, and Computer Vision.

# Best Laptops For Deep Learning and Machine Learning

Though there are various fields out there which requires a laptop with good specifications and you can get it at an affordable price but that’s not the same case for deep learning.

# 15 Best Books for Machine Learning

Machine Learning today is one of the most sought-after skills in the market. Here are some of the best books which you can use to learn Machine Learning.

# Numpy Tutorial – Quick Guide

The NumPy library is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays.

# ML And AI Comprises With These Languages.

Foundation is the basement for a healthy home. So here comes with the languages too which acts like a foundation…

# Image Recognition Model to Detect Identifiable Professionals

Before, to train an AI model that can recognize whatever you want it to recognize in pictures, involves lots of expertise in Applied Mathematics and use Deep Learning Libraries. To write the code for the algorithm and fit the code to your images involves lots of time and stress.

# What is Machine Learning?

The art and science of :
Giving Computers the ability to learn,
To make decisions from data,
Without being explicitly programmed .

# Path to Machine Learning

Here goes the learning path to become an expert in machine learning.Learn any programming language (Python is highly preferable)

# Machine Learning Libraries in Python

Today,Python is a trending language in the industry and it replaced many of the other programming languages.Machine Learning got easier in Python than from any other language. Whether it is Machine learning or Artificial Intelligence or Data Science it is fun doing with Python.

# First ML Project with the Iris Dataset for Beginners

Every ML project starts with knowing what your data is all about.You should analyze and understand your data and should think of what Algorithms we should choose.

# Is ML and AI future?

In Todays era people want automation in their life.People want everything on the tip of their finger.People do not care about money they only care about advancement in their life.They want to adapt technology trendz.

# List of Youtube Channels Dedicated to AI and ML

Our main goal is to prepare people for trending technologies like Cloud, Machine Learning. We make Technology based and Educations based videos.

# 7 Steps to develop a Machine Learning Application

There are some basic steps involved to develop a machine learning application. I will guide with the basic 7 steps to get started with a machine learning application.

# Machine Learning VS Artificial Intelligence

“AI is any technology that enables a system to demonstrate human-like intelligence”. “Machine Learning is one type of AI that uses mathematical models trained on data to make decisions.

# Classification Problem

We will be taking an example of a classification problem with the help of KNearestNeighbors in Scikit-Learn.

# Types of classification algorithms in Machine Learning

In machine learning, Classification is a supervised learning approach in which the computer program learns from the data input given to it and then classify it.