Tag Archives: Data Science

MIT Free Course on Machine Learning (New)

MIT Free Course on Machine Learning

In this post, the information regarding new free course on machine learning launched by MIT OpenCourseware. In case, you are a beginner data scientist or ML Engineer, you will find this course to be very useful.  Here is the URL to the free course on machine learning: https://bit.​ly/37iNNAA. This course, titled as Introduction to Machine Learning, introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning, with applications to images and to temporal sequences. Here are some of the key topics for which lectures can be found: …

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Posted in Career Planning, Data Science, Machine Learning, Tutorials. Tagged with , , .

Gradient Boosting Regression Python Examples

Gradient Boosting Regressor Feature Importances

In this post, you will learn about the concepts of Gradient Boosting Regression with the help of Python Sklearn code example. Gradient Boosting algorithm is one of the key boosting machine learning algorithms apart from AdaBoost and XGBoost.  What is Gradient Boosting Regression? Gradient Boosting algorithm is used to generate an ensemble model by combining the weak learners or weak predictive models. Gradient boosting algorithm can be used to train models for both regression and classification problem. Gradient Boosting Regression algorithm is used to fit the model which predicts the continuous value. Gradient boosting builds an additive mode by using multiple decision trees of fixed size as weak learners or …

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Posted in Data Science, Machine Learning, Python. Tagged with , , , .

Differences between Random Forest vs AdaBoost

decision trees in random forest

In this post, you will learn about the key differences between AdaBoost classifier and Random Forest algorithm. As data scientists, you must get a good understanding of the differences between Random Forest and AdaBoost machine learning algorithm. Both algorithms can be used for both regression and classification problems. Both Random Forest and AdaBoost algorithm is based on creation of Forest of trees. They are called as ensemble learning algorithms. Random forest is created using a bunch of decision trees which make use of different variables or features and makes use of bagging techniques for data sample. In AdaBoost, the forest is created using a bunch of what is called as decision …

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Posted in Data Science, Machine Learning. Tagged with , .

Data Quality Challenges for Analytics Projects

data quality challenges for analytics projects

In this post, you will learn about some of the key data quality challenges which you may need to tackle with, if you are working on data analytics projects or planning to get started on data analytics initiatives. If you represent key stakeholders in analytics team, you may find this post to be useful in understanding the data quality challenges.  Here are the key challenges in relation to data quality which when taken care would result in great outcomes from analytics projects related to descriptive, predictive and prescriptive analytics: Data accuracy / validation Data consistency Data availability Data discovery Data usability Data SLA Cos-effective data Data Accuracy One of the most important …

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Posted in Analytics, data engineering, Data Science. Tagged with , , .

Data Science vs Data Engineering Team – Have Both?

Data engineering vs Data Science

In this post, you will learn about different aspects of data science and data engineering team and also understand the key differences between them. As data science / engineering stakeholders, it is very important to understand whether we need to have one or both the teams to achieve high quality dataset & data pipelines as well as high-performant machine learning models. Background When an organization starts on the journey of building data analytics products, primarily based on predictive analytics, it goes on to set up a centralized (mostly) data science team consisting of data scientists. The data science team works with the product team or multiple product teams to gather the …

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Posted in data engineering, Data Science. Tagged with , .

500+ Machine Learning Interview Questions

machine learning interview questions

This post consists of all the posts on this website in relation to interview questions / quizzes related to data science / machine learning topics.  These questions can prove to be helpful for the following: Product managers Data scientists Product Managers Interview Questions Find the questions for product managers on this page – Machine learning interview questions for product managers Data Scientists Interview Questions Here are posts representing 500+ interview questions which will be helpful for data scientists / machine learning engineers. You will find it useful as practise questions and answers while preparing for machine learning interview. Decision tree questions Machine learning validation techniques questions Neural networks questions – …

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Posted in Data Science, Interview questions, Machine Learning. Tagged with , , .

Spacy Tokenization Python Example

Spacy Tokenizer Python Example

In this post, you will quickly learn about how to use Spacy for reading and tokenising a document read from text file or otherwise. As a data scientist starting on NLP, this is one of those first code which you will be writing to read the text using spaCy. First and foremost, make sure you have got set up with Spacy, and, loaded English tokenizer. The following commands help you set up in Jupyter notebook. Reading text using spaCy: Once you are set up with Spacy and loaded English tokenizer, the following code can be used to read the text from the text file and tokenize the text into words. Pay attention …

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Posted in Data Science, NLP. Tagged with , .

Different Success / Evaluation Metrics for AI / ML Products

Success metrics for AI and ML products

In this post, you will learn about some of the common success metrics which can be used for measuring the success of AI / ML (machine learning) / DS (data science) initiatives / products. If you are one of the AI / ML stakeholders, you would want to get hold of these metrics in order to apply right metrics in right business use cases. Business leaders do want to know and maximise the return on investments (ROI) from AI / ML investments.  Here is the list of success metrics for AI / DS / ML initiatives: Business value metrics / Key performance indicators (KPIs): Business value metrics such as operating …

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Posted in AI, Data Science, Machine Learning. Tagged with , , .

Hierarchical Clustering Explained with Python Example

In this post, you will learn about the concepts of Hierarchical clustering with the help of Python code example. As data scientist / machine learning enthusiasts, you would want to learn the concepts of hierarchical clustering in a great manner. The following topics will be covered in this post: What is hierarchical clustering? Hierarchical clustering Python example What is Hierarchical Clustering? Hierarchical clustering is an unsupervised learning algorithm which is based on clustering data based on hierarchical ordering. Recall that clustering is an algorithm which groups data points within multiple clusters such that data within each cluster are similar to each other while clusters are different each other. The hierarchical clustering can be classified …

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Posted in Data Science, Machine Learning, Python. Tagged with , , , .

Generalized Linear Models Explained with Examples

In this post, you will learn about the concepts of generalized linear models (GLM) with the help of Python examples.  It is very important for data scientists to understand the concepts of generalized linear models and how are they different from general linear models such as regression or ANOVA models.  Some of the following topics have been covered in this post: What are generalized linear models (GLM)? Generalized linear models real-world examples When to use generalized linear models? What are Generalized Linear Models? Generalized linear models represent the class of regression models which models the response variable, Y, and the random error term () based on exponential family of distributions such as normal, Poisson, …

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Posted in Data Science, Machine Learning. Tagged with , .

Geometric Distribution Explained with Python Examples

In this post, you will learn about the concepts of Geometric probability distribution with the help of real-world examples and Python code examples. It is of utmost importance for data scientists to understand and get an intuition of different kinds of probability distribution including geometric distribution. You may want to check out some of my following posts on other probability distribution. Normal distribution explained with Python examples Binomial distribution explained with 10+ examples Hypergeometric distribution explained with 10+ examples In this post, the following topics have been covered: Geometric probability distribution concepts Geometric distribution python examples Geometric distribution real-world examples Geometric Probability Distribution Concepts Geometric probability distribution is a discrete …

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Posted in Data Science, statistics. Tagged with , .

Top 10 Analytics Strategies for Great Data Products

In this post, you will learn about the top 10 data analytics strategies which will help you create successful data products. These strategies will be helpful in case you are setting up a data analytics practice or center of excellence (COE). As an AI / Machine Learning / Data Science stakeholders, it will be important to understand these strategies in order to deliver analytics solution which creates business value having positive business impact.  Here are the top 10 data analytics strategies: Identify top 2-3 business problems Identify related business / engineering organizations Create measurement plan by identifying right KPIs Identify analytics deliverables such as analytics reports, predictions etc Gather data …

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Posted in Analytics, Data Science, Machine Learning. Tagged with , , .

Keras CNN Image Classification Example

In this post, you will learn about how to train a Keras Convolution Neural Network (CNN) for image classification. Before going ahead and looking at the Python / Keras code examples and related concepts, you may want to check my post on Convolution Neural Network – Simply Explained in order to get a good understanding of CNN concepts. Keras CNN Image Classification Code Example First and foremost, we will need to get the image data for training the model. In this post, Keras CNN used for image classification uses the Kaggle Fashion MNIST dataset. Fashion-MNIST is a dataset of Zalando’s article images—consisting of a training set of 60,000 examples and a …

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Posted in Data Science, Deep Learning, Machine Learning. Tagged with , , , , .

Data Quality Challenges for Machine Learning Models

In this post, you will learn about some of the key data quality challenges which need to be dealt with in a consistent and sustained manner to ensure high quality machine learning models. Note that high quality models can be termed as models which generalizes better (lower true error with predictions) with unseen data or data derived from larger population. As a data science architect or quality assurance (QA) professional dealing with quality of machine learning models, you must learn some of these challenges and plan appropriate development processes to deal with these challenges.   Here are some of the key data quality challenges which need to be tackled appropriately in …

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Posted in Data Science, Machine Learning, QA. Tagged with , , .

Neural Network Back-Propagation Python Examples

In this post, you will learn about the concepts of neural network back propagation algorithm along with Python examples. As a data scientist, it is very important to learn the concepts of back propagation algorithm if you want to get good at deep learning models. This is because back propagation algorithm is key to learning weights at different layers in the deep neural network. What’s Back Propagation Algorithm? The backpropagation algorithm represents the propagation of the gradients of outputs from each node (in each layer) on the final output, in the backward direction right up to the input layer nodes. All that is achieved using the backpropagation algorithm is to …

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Posted in Data Science, Deep Learning. Tagged with , , .

Z-Score Explained with Ronaldo / Robert Example

In Champion’s league 2019-2020, here is the data related to their performance (ESPN.in). Player No. of Matches Played No. of Goals Scored Avg Goals / Matches Christiano Ronaldo 8 4 0.5 Robert Lewandowski 10 15 1.5 Table 1. Ronaldo / Robert performance in 2019-2020 Champion’s League . Well, the average goals / match indicates that Robert Lewandowski played much better than Christiano Ronaldo. However, can we conclude the same using statistical measures? How could we find out if they performed better than their own performance over last 7-8 years? This is where Z-Score comes into picture. In above evaluation, what is used to compare the performance is average goals / …

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