Tag Archives: machine learning

Reinforcement Learning Real-world examples

Reinforcement-learning-real-world-example

In this post, you will learn about some real-world / real-life examples of Reinforcement learning, one of the different approaches to machine learning where other approaches are supervised and unsupervised learning. Before looking into the real-world examples of Reinforcement learning, let’s quickly understand what is reinforcement learning. Introduction to Reinforcement Learning (RL) Reinforcement learning is an approach to machine learning in which the agents are trained to make a sequence of decisions. The agent, also called as an AI agent gets trained in the following manner: The agent interacts with the environment and make decisions or choices. For training purpose, the agent is provided with the contextual information about the environment and …

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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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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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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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Classification Problems Real-life Examples

classification problems real life examples

In this post, you will learn about some popular and most common real-life examples of machine learning classification problems. For beginner data scientists, these examples will prove to be helpful to gain perspectives on real-world problems which can be termed as machine learning classification problems. This post will be updated from time-to-time to include interesting real-life examples which can be solved by training machine learning classification models. Before going ahead and looking into examples, let’s understand a little about what is machine learning (ML) classification problem. You may as well skip this section if you are familiar with the definition of machine learning classification problems & solutions.  What are ML …

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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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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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Predictive vs Prescriptive Analytics Difference

In this post, you will quickly learn about the difference  between  predictive analytics and prescriptive analytics. As data analytics stakeholders, one must get a good understanding of these concepts in order to decide when to apply predictive and when to make use of prescriptive analytics in analytics solutions / applications. Without further ado, let’s get straight to the diagram.  In the above diagram, you could observe / learn the following: Predictive analytics: In predictive analytics, the model is trained using historical / past data based on supervised, unsupervised, reinforcement learning algorithms. Once trained, the new data / observation is input to the trained model. The output of the model is prediction in form …

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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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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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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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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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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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Python Keras – Learning Curve for Classification Model

Training & Validation Accuracy & Loss of Keras Neural Network Model

In this post, you will learn about how to train an optimal neural network using Learning Curves and Python Keras. As a data scientist, it is good to understand the concepts of learning curve vis-a-vis neural network classification model to select the most optimal configuration of neural network for training high-performance neural network. In this post, the following topics have been covered: Concepts related to training a classification model using a neural network Python Keras code for creating the most optimal neural network using a learning curve  Training a Classification Neural Network Model using Keras Here are some of the key aspects of training a neural network classification model using Keras: …

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10 Key Challenges for AI / ML Projects Implementation

Challenges related to Machine Learning Projects Implementations

In this post, you will learn about some of the key challenges in relation to achieving successful AI / ML projects implementation in a consistent and sustained manner. As AI / ML project stakeholders including senior management stakeholders, data science architects, product managers etc, you must get a good understanding of what would it take to successfully execute AI / ML projects and create value for the customers and the business.  Either you are building AI / ML products or enabling unique models for your clients in SaaS setup, you will come across most of these challenges.  Here are some of the key challenges: Whether a machine learning solution is …

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Free MIT Course on Machine Learning for Healthcare

machine learning and healthcare MIT free course

In this post, you will get a quick overview on free MIT course on machine learning for healthcare. This is going to be really helpful for machine learning / data science enthusiasts as building machine learning solutions to serve healthcare requirements comes with its own set of risks. It will be good to learn about different machine learning techniques, applications related disease progression modeling, cardiac imaging, pathology etc, risks and risk mitigation techniques. Here is the link to the course – Machine Learning for Healthcare Here are the links to some of the important course content: Video lectures Lecture notes (PDF) The entire course material can be downloaded from this page – …

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