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As more and more new, quality materials are published for better understanding of big data and machine learning concepts, finding such latest materials is becoming increasingly challenging.

Fortunately, that really isn’t necessary if you have an indexed, properly curated and constantly updated source that gets constant feedback from its readers.

In this regularly updated post, I’ll give you resources you can use to learn about Big Data and machine learning and stay on top of the job market, including some free tools that’ll be useful for getting the job done.

weekly updates about popular articles on these topics by email Doing Data Science at Twitter It talks about how machine learning has played an increasingly prominent role across many core Twitter products that were previously not ML driven and how the data science landscape in Twitter has changed in the recent past Data Science Salary Survey 2015 the 2015 version of the Data Science Salary Survey explores patterns in tools, tasks, and compensation through the lens of clustering and linear models.

The research is based on data collected through an online 32-question survey, including demographic information Some Real World Machine Learning Examples The post talks about what are some real-world examples of applications of machine learning in the field- ranging from Computational Biology &

two-hour introduction to data analysis in R If you’re looking for a non-diamonds or non-nycflights13 introduction to R / ggplot2 / dplyr feel free to use materials from this workshop.

In this Intro to Python class, you will learn about powerful ways to store and manipulate data as well as cool data science tools to start your own analyses.

You can find many additional references here (Python, Excel, Spark, R, Deep Learning, AI, SQL, NoSQL, Graph Databses, Visualization, etc.) Top 10 R Packages to be a Kaggle Champion Across all major surveys, R has clearly dominated as one of the top programming choices for data scientists.

Here’s a list of 10 R packages that played a key role in getting a top 10 ranking in more than 15 Kaggle competitions Integrating Python and R into a Data Analysis Pipeline The first in a series of blog posts that: outline the basic strategy for integrating Python and R, run through the different steps involved in this process;

The first is a grouping of algorithms by the learning style.The second is a grouping of algorithms by similarity in form or function (like grouping similar animals together). Arriving

Essentials of Machine Learning Algorithms (with Python and R Codes)

Note: This article was originally published on Aug 10, 2015 and updated on Sept 9th, 2017

Google’s self-driving cars and robots get a lot of press, but the company’s real future is in machine learning, the technology that enables computers to get smarter and more personal.

The idea behind creating this guide is to simplify the journey of aspiring data scientists and machine learning enthusiasts across the world.

How it works: This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables).

Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data.

This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions.

These algorithms can be applied to almost any data problem: It is used to estimate real values (cost of houses, number of calls, total sales etc.) based on continuous variable(s).

In this equation: These coefficients a and b are derived based on minimizing the sum of squared difference of distance between data points and regression line.

And, Multiple Linear Regression(as the name suggests) is characterized by multiple (more than 1) independent variables. While finding best fit line, you can fit a polynomial or curvilinear regression.

It is a classification not a regression algorithm. It is used to estimate discrete values ( Binary values like 0/1, yes/no, true/false ) based on given set of independent variable(s).

It chooses parameters that maximize the likelihood of observing the sample values rather than that minimize the sum of squared errors (like in ordinary regression).

source: statsexchange In the image above, you can see that population is classified into four different groups based on multiple attributes to identify ‘if they will play or not’.

In this algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate.

For example, if we only had two features like Height and Hair length of an individual, we’d first plot these two variables in two dimensional space where each point has two co-ordinates (these co-ordinates are known as Support Vectors)

In the example shown above, the line which splits the data into two differently classified groups is the black line, since the two closest points are the farthest apart from the line.

It is a classification technique based on Bayes’ theorem with an assumption of independence between predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

Step 1: Convert the data set to frequency table Step 2: Create Likelihood table by finding the probabilities like Overcast probability = 0.29 and probability of playing is 0.64.

Yes) * P(Yes) / P (Sunny) Here we have P (Sunny |Yes) = 3/9 = 0.33, P(Sunny) = 5/14 = 0.36, P( Yes)= 9/14 = 0.64 Now, P (Yes |

However, it is more widely used in classification problems in the industry. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases by a majority vote of its k neighbors.

Its procedure follows a simple and easy  way to classify a given data set through a certain number of  clusters (assume k clusters).

We know that as the number of cluster increases, this value keeps on decreasing but if you plot the result you may see that the sum of squared distance decreases sharply up to some value of k, and then much more slowly after that.

grown as follows: For more details on this algorithm, comparing with decision tree and tuning model parameters, I would suggest you to read these articles: Python R Code

For example: E-commerce companies are capturing more details about customer like their demographics, web crawling history, what they like or dislike, purchase history, feedback and many others to give them personalized attention more than your nearest grocery shopkeeper.

How’d you identify highly significant variable(s) out 1000 or 2000? In such cases, dimensionality reduction algorithm helps us along with various other algorithms like Decision Tree, Random Forest, PCA, Factor Analysis, Identify based on correlation matrix, missing value ratio and others.

GBM is a boosting algorithm used when we deal with plenty of data to make a prediction with high prediction power. Boosting is actually an ensemble of learning algorithms which combines the prediction of several base estimators in order to improve robustness over a single estimator.

The XGBoost has an immensely high predictive power which makes it the best choice for accuracy in events as it possesses both linear model and the tree learning algorithm, making the algorithm almost 10x faster than existing gradient booster techniques.

It is designed to be distributed and efficient with the following advantages: The framework is a fast and high-performance gradient boosting one based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Since the LightGBM is based on decision tree algorithms, it splits the tree leaf wise with the best fit whereas other boosting algorithms split the tree depth wise or level wise rather than leaf-wise.

So when growing on the same leaf in Light GBM, the leaf-wise algorithm can reduce more loss than the level-wise algorithm and hence results in much better accuracy which can rarely be achieved by any of the existing boosting algorithms.

Catboost can automatically deal with categorical variables without showing the type conversion error, which helps you to focus on tuning your model better rather than sorting out trivial errors.

My sole intention behind writing this article and providing the codes in R and Python is to get you started right away. If you are keen to master machine learning, start right away.

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