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Data Visualization

Basic concepts for forecasting models in machine learning with example

In this article, we will discuss time series concepts with machine learning examples that deal with the time component in the data.

Forecasting is so much important in the banking sector, weather, population prediction, and many more that directly deals with real-life problems.

Time series models are based on a function of time. The measurements are in regular intervals of time where time be an independent variable for modeling.

Z = f(t)

Z is the values Z1, Z2……Zn and “t” are the times at T1, T2….Tn intervals.

Topics to be covered:

  1. Components of Time Series
  2. White Noise
  3. Stationary and Non-Stationary
  4. Rolling Statistics and Dickey-Fuller…


Data Science

A robust method to make data ready for machine learning estimators

In this article, we will study some important data preprocessing methods. It is a very important step to visualize the data and make it in a suitable form so that the estimators (algorithm) fit well with good accuracy.

Topics to be covered:

  1. Standardization
  2. Scaling with sparse data and outliers
  3. Normalization
  4. Categorical Encoding
  5. Imputation

Standardization

Standardization is a process that deals with the mean and standard deviation of the data points. As raw data, the values are varying from very low to very high. So, to avoid the low performance in the model we use standardization. …


Unsupervised centroid based algorithm learning

In this article, we cover the unsupervised learning algorithm in machine learning i.e. mean shift or mode-seeking algorithm. This clustering on the centroid-based algorithm in which the centroid finds the higher density center in dense smooth data points.

Where we can use the Mean Shift algorithm?

The various applications of this algorithm in image processing to smoothing of images, object tracking, etc.

It is good in finding the maxima of the data points and it is an iterative method to get the convergence in the density function. The advantage of mean shift over k-means clustering is that it doesn’t require several clusters in the parameters.

The parameters in the mean shift are described below:

  • Bandwidth: It is…

Programming

Series and DataFrame creation in python

In this article, we will deal with categorical data analyses in pandas with python examples.

Category data is a cluster of different variants as a part of information. The data collection is very much important to know the statistics of particular analysis of the product.

The category data may be classified into two groups as shown below:

  • Nominal category: It deals with the data that have different categories. The categories can be in string or numerical form but we can not do mathematical operations on these types of data.

For Example any binary values, zip-code, gender, etc.

  • Ordinal category: This…


Business Science

Marketing strategies with digital media

What is Digital Marketing

Digital marketing is any marketing that utilizes electronic devices to convey a promotional message. it can be in any structure or form like videos, display ads, online media posts & many more. Digital channels such as search engines like Google, websites, online media, email, and mobile apps are used for Advertising. Using these online media channels, companies promote goods, services, and brands. As an example, Think with Google marketing insights found that 48% of consumers start their search on search engines, while 33% actually look into brand websites and 26% search within mobile applications.

Marketing has…


Deep Learning

To remove noises from the images

Morphological Methods

When images are pre-processed for enhancement and performance operations like threshold, then the image has a chance to get some noise. As a result, improper balance in the pixel information exists in the image.

The operation of morphological is to remove the noise that mainly affects the shape and information of images. Morphological operations are very useful in image segmentation to get the noiseless binary image.

The basic morphological operations are erosion and dilation. The explanation of these two operations is discussed below:

Dilation

In the dilation operation if the object is white then the pixel around the…


Artificial Intelligence

Innovation to make life easier

For those who are new to this topic, you might be wondering: are Robots: AI? Or are AI: robots?

So let us clarify: Robots and AI are entirely two different topics.

Robotics

In simple words, Robotics is a technology that handles physical robots. They are machines that can be programmed to carry out several actions autonomously or non-autonomously. Sensors and actuators are some of the factors that use to create automated or semi-automated robots.

Everything around us is a type of robot: a washing machine, a mixer, a remote-controlled toy car, etc. …


Deep Learning

Techniques to improve the accuracy of the algorithm

Hyper-parameter Tuning

In this article, we will discuss hyper-parameter tuning. When we talk about improving the accuracy of the machine and deep learning model the first thing is to come’s to our mind is the tuning parameter.

Topics to be covered

1. What is hyper-parameter tuning?
2. Why do we need hyper-parameter tuning?
3. Hyper-parameter Types
4. Techniques of hyper-parameter tuning
a. GridSearchCV
b. RandomizedSearchCV
5. Bayesian Optimization -Automate Hyper-parameter Tuning(Hyper-
optimization)

What is hyper-parameter tuning?

“Hyper-parameter tuning is choosing a set of optimal hyper-parameter for learning algorithm”. we use different input parameters for different machine learning models. These input…


Machine Learning

Preprocessing techniques for feature extraction and object detection

Image Processing

Image Processing initiates by capturing images. The amount of information contained in the image depends on the quality of images acquired. Therefore, input images to the systems are ensured to be of appropriate standards. The visual detection of fruits implies digital image or digital video processing by intelligent robotic or computer vision systems.

In any field image processing deals with two basic principles that require color and shape. The proposed system includes digital image collection by the pi camera and clicking fruit areas. …


Image processing concept with OpenCV library

In this article, we will discuss object segmentation with the help of the OpenCV library and pre-processing techniques in image processing. We will try to mark the contours with the number to get the total number of objects.

What is OpenCV?

It is an open-source library to process images and videos for various applications in real life, like segmentation, object detection, and many more. The main benefit of the OpenCV library is working on NumPy arrays that can be work with a different library.

The process to count the objects in an image is done with the below process:

Step1…

Amit Chauhan

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