**Natural Language Processing** (NLP)** **is a field at the intersection of computer science, artificial intelligence, and linguistics. The goal is for computers to process or “understand” natural language to perform tasks like Language Translation and Question Answering.

With the rise of voice interfaces and chatbots, NLP is one of the most important technologies of the information age a crucial part of artificial intelligence. Fully understanding and representing the meaning of language is an extremely difficult goal. Why? Because human language is quite special.

The field of artificial intelligence has always envisioned machines being able to mimic the functioning and abilities…

Understanding Encoders-Decoders, Sequence to Sequence Architecture in Deep Learning.

In Deep Learning, Many Complex problems can be solved by constructing better neural network architecture. The RNN(Recurrent Neural Network) and its variants are much useful in sequence to sequence learning. The RNN variant LSTM (Long Short-term Memory) is the most used cell in seq-seq learning tasks.

The encoder-decoder architecture for recurrent neural networks is the standard neural

machine translation methodthat rivals and in some cases outperforms classical statistical machine translation methods.

This architecture is very new, having only been pioneered in 2014, although, has been adopted as the core technology…

XGBoost stands for “Extreme Gradient Boosting”. XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable. It implements Machine Learning algorithms under the Gradient Boosting framework. It provides a parallel tree boosting to solve many data science problems in a fast and accurate way.

XGBoost is a software library that you can download and install on your machine, then access from a variety of interfaces. Specifically, XGBoost supports the following main interfaces:

- Command Line Interface (CLI).
- C++ (the language in which the library is written).
- Python interface as well as a model in scikit-learn.
- …

The algorithms are used to measure the distance between each text and to calculate the score.

Distance measures play an important role in machine learning

They provide the foundations for many popular and effective machine learning algorithms like KNN (K-Nearest Neighbours) for supervised learning and K-Means clustering for unsupervised learning.

Different distance measures must be chosen and used depending on the types of data, As such, it is important to know how to implement and calculate a range of different popular distance measures and the intuitions for the resulting scores.

In this blog, we’ll discover distance measures in machine learning.

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Supervised Learning Algorithm Summary

**Linear Regression**

- Linear regression: One of the most commonly used methods of regression.
- If X1, X2, X3…. Xn is the independent variable and Y is a target variable. We need to predict Y given X’s
- The method of linear regression is a statistical model that fits a linear relation of Y with X’s
- The relation is linear in the coefficient of X’s.
- In a 2-Dimension, the equation is a line.
- In 3- Dimension, the equation is a plane and in higher dimensions, it is called a hyperplane.

If One Predictor variable (x) → Simple Linear Regression

ARIMA stands for Auto Regression Integrated Moving Average.

ARIMA — Important Concepts

ACF-PACF and STATIONARITY

ACF: Correlation between the original data and the same data lagged by ‘h’ time period

The Correlation Coefficient is computed between two variables. Since we have only one variable here, we need to compute autocorrelation.

Let’s understand how the data points in this series are related to their immediately preceding data points.

A time series forecasting series.

Holt-Winters forecasting is a way to model and predict the behavior of a sequence of values over time — a time series. Holt-Winters is one of the most popular forecasting techniques for time series.

It’s decades old, but it’s still ubiquitous in many applications, including monitoring, where it’s used for purposes such as anomaly detection and capacity planning.

Holt-Winters is a model of time series behavior. Forecasting always requires a model, and Holt-Winters is a way to model three aspects of the time series: a typical value (average), a slope (trend) over time, and a…

A time series is a sequence of observations taken sequentially in time.

Time series forecasting uses information regarding historical values and associated patterns to predict future activity. Most often, this relates to trend analysis, cyclical fluctuation analysis, and issues of seasonality.

If the independent variables are

- Unknown
- Not available
- Might not fit the data
- Difficult to forecast

**Typical Time Series**

Essential Skills you Need to know to start Doing Data Science.

Data science is ever-evolving, so mastering its foundational technical and soft skills will help us be successful in a career as a Data Scientist, as well as pursue advanced concepts, such as deep learning and artificial intelligence.

Data Science is such a broad field that includes several subdivisions:

- Data Preparation and Exploration
- Data Representation and Transformation
- Data Visualization and Presentation
- Predictive Analytics
- Machine Learning….etc

Lie #01

From an early age, thinking about money was not encouraged!

It was the unsaid rule that money is important, but thinking about it is evil. After all, money was the reason behind the fights, the wars, the disagreements.

Now I know, it is not money that is the cause. It is the importance we attach to it, in our lives. Money is simply a medium of transaction. when it becomes an emotion, is when it consumes us. And that is true for all things in life!.

Lie #2.

It was always assumed that getting rich was possible only…