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Statistical Learning


Top Data Science Crash Courses to Shape Your Career in 2021

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As the demand for data science professionals grows rapidly, students are looking for data science crash courses to gain the necessary knowledge and high-end skills needed to tackle real-world challenges. Here are the top data science courses for data aspirants to pursue. The program features a five-course series formulated to boost the foundation of data scientists in the areas of machine learning, data science, and statistics. This course is best suited for students wanting to learn big data analysis. The course gives you a deep understanding of statistics, data analysis techniques, machine learning algorithms, and probability.


How to Perform K means clustering Python? - StatAnalytica

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The k means clustering Python is one of the unsurprised machine learning methods applied to identify data object clusters within a dataset. There are various kinds of clustering methods, but it has been seen that k means is the oldest and most preferred clustering method. Because of this, k-means clustering in Python is the straightforward method that various data scientists and programmers adopt. If you want to know how to implement k-means clustering Python, then keep scrolling the blog. In this blog, we have covered all the necessary details about the K-means clustering, and an example is also detailed to help you the clustering's functioning.


Top 5 Statistical Data Analysis Techniques a Data Scientist Should Know

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Statistical data analysis is a procedure of performing various statistical operations. It is a kind of quantitative research, which seeks to quantify the data, and typically, applies some form of statistical analysis. Quantitative data involves descriptive data, such as survey data and observational data. Statistical data analysis generally involves some form of statistical tools, which a layman cannot perform without having any statistical knowledge. Linear Regression, is the technique that is used to predict a target variable by providing the best linear relationship among the dependent and independent variables where best fit indicates the sum of all the distances amidst the shape and actual observations at each data point is as minimum as achievable.


Clustering City Nightlife using Machine Learning

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Everyone knows how Covid-19 pandemic devastated the nightlife industry with social distancing, lockdowns, mask-wearing and early curfews. These nightlife spaces were shuttered because they had been deemed non-essential services and places of easy transmission for the coronavirus. Now that central and state governments in India have eased the restrictions people can finally enjoy a breather, commemorating a special occasion or just spending time with friends over food and drinks. In a city like Pune, which boasts a happening nightlife scene, there's always a party happening somewhere or the other. Widely known as the "IT hub of India", "Automobile and Manufacturing hub of India" and "Oxford of the East", Pune is known for its lifestyle, pleasant weather and just… everything good.


Fake News Detection Using Machine Learning Ensemble Methods

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The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality. Automated classification of a text article as misinformation or disinformation is a challenging task. Even an expert in a particular domain has to explore multiple aspects before giving a verdict on the truthfulness of an article. In this work, we propose to use machine learning ensemble approach for automated classification of news articles. Our study explores different textual properties that can be used to distinguish fake contents from real. By using those properties, we train a combination of different machine learning algorithms using various ensemble methods and evaluate their performance on 4 real world datasets. Experimental evaluation confirms the superior performance of our proposed ensemble learner approach in comparison to individual learners. The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. Besides other use cases, news outlets benefitted from the widespread use of social media platforms by providing updated news in near real time to its subscribers. The news media evolved from newspapers, tabloids, and magazines to a digital form such as online news platforms, blogs, social media feeds, and other digital media formats [1]. It became easier for consumers to acquire the latest news at their fingertips.


Top 10 Machine Learning Algorithms for Beginners

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There's no denying that the area of machine learning or artificial intelligence has grown in prominence in recent years. Machine learning is very effective for making predictions or calculating suggestions based on vast quantities of data, which is the trendiest topic in the tech sector right now. In this article, we will discuss the top 10 ML algorithms for newbies. Any other algorithm in computer programming can be connected to a machine learning method. An ML algorithm is a data-driven process for developing a production-ready ML model.


Overview of Machine Learning

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Most people see Machine Learning as robots that will dominate the world, computers winning against people in board games, robot butlers. However, Machine Learning can be things simpler than that and also be used in thousands of different tasks. Personally, my first experience with Machine Learning was in 2019 during my internship at a startup, where I make a system that could automatically count insects using only an RGB image. I don't know when was the first time that you have heard about Machine Learning, but probably this can take less than a decade, however, Machine Learning is not a young approach. First of all, Machine Learning is not a magic trick, there is Math behind that, of course, our computer wasn't be able to learn if we didn't set up a well-defined mathematical model.


Data Profiling -- Having that First Date with your Data

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"Know thy data" is one of the fundamental principles of sound data science. Another name for this is data profiling. I simply refer to it as "having your first date with your data." We expand on data profiling here by elucidating the following four steps toward knowing your data: (1) data preview and selection; (2) data cleansing and preparation; (3) feature selection and engineering; and (4) data typing for normalization and transformation. Knowledge of your data begins with a thorough preview of the good, bad and ugly parts of your data collection, and it ultimately leads to a decision about which portions of the data set you will select for your data science analysis.


Top 10 Python Libraries To Set Your Idea of Machine Learning in 2021 - SoftwareFirms Blog

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Python is the most popular, full-fledged, and high-level programming language in a flock of programming languages. It has successfully made a quality space in many developers' hearts with immense features and object-oriented quality--the reason why Python libraries have the same space in the IT industry. As per the Builtwith, 45% of IT industrialists prefer Python above all other programming languages to implement AI and Machine Learning. This post compiled some must-have Python Libraries for developers looking to implement ML in their live application. Do you know the top 100 Python Development Companies in 2021?


07 -- Hands On ML -- Ensemble

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Ensemble Learning is taking the predictions of multiple models and assume the output to be having the most votes. When you train multiple Decision Trees each on some random sampling of the dataset and for predictions you take predictions of all the trees, the output class would be the class which gets the most votes. This approach is called Random Forest. Voting classifier is when you train the data on multiple classifier such as Logistic Regression, SVM, RF and other classifiers and the majority vote is the predicted output class ie hard classifier. Voting can also be taken as soft by taking argmax of the outputs.