Tag Archives: machine learning
Great Mind Maps for Learning Machine Learning
In this post, you will get to look at some of the great mind-maps for learning different machine learning topics. I have gathered these mind maps from different web pages on the Internet. The idea is to reinforce our understanding of different machine learning topics using pictures. You may have heard the proverb – A picture is worth a thousand words. Keeping this in mind, I thought to pull some of the great mind maps posted on different web pages. I would be updating this blog post from time-to-time. If you are a beginner data scientist or an experienced one, you may want to bookmark this page for refreshing your …
Different Types of Distance Measures in Machine Learning
In this post, you will learn different types of distance measures used in different machine learning algorithms such as K-nearest neighbours, K-means etc. Distance measures are used to measure the similarity between two or more vectors in multi-dimensional space. The following represents different forms of distance metrics / measures: Geometric distances Computational distances Statistical distances Geometric Distance Measures Geometric distance metrics, primarily, tends to measure the similarity between two or more vectors solely based on the distance between two points in multi-dimensional space. The examples of such type of geometric distance measures are Minkowski distance, Euclidean distance and Manhattan distance. One other different form of geometric distance is cosine similarity which will discuss …
Machine Learning Terminologies for Beginners
When starting on the journey of learning machine learning and data science, we come across several different terminologies when going through different articles/posts, books & video lectures. Getting a good understanding of these terminologies and related concepts will help us understand these concepts in a nice manner. At a senior level, it gets tricky at times when the team of data scientists / ML engineers explain their projects and related outcomes. With this in context, this post lists down a set of commonly used machine learning terminologies that will help us get a good understanding of ML concepts and also engage with the DS / AI / ML team in …
Machine Learning Free Course at Univ Wisconsin Madison
In this post, you will learn about the free course on machine learning (STAT 451) recently taught at University of Wisconsin-Madison by Dr. Sebastian Raschka. Dr. Sebastian Raschka in currently working as an assistant Professor of Statistics at the University of Wisconsin-Madison while focusing on deep learning and machine learning research. The course is titled as “Introduction to Machine Learning”. The recording of the course lectures can be found on the page – Introduction to machine learning. The course covers some of the following topics: What is machine learning? Nearest neighbour methods Computational foundation Python Programming (concepts) Machine learning in Scikit-learn Tree-based methods Decision trees Ensemble methods Model evaluation techniques Concepts of …
MIT Free Course on Machine Learning (New)
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: …
Gradient Boosting Regression Python Examples
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 …
500+ 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 – …
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 …
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 …
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 …
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 …
Python Keras – Learning Curve for Classification 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: …
Free MIT Course on Machine Learning for Healthcare
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 – …
Keras Multi-class Classification using IRIS Dataset
In this post, you will learn about how to train a neural network for multi-class classification using Python Keras libraries and Sklearn IRIS dataset. As a deep learning enthusiasts, it will be good to learn about how to use Keras for training a multi-class classification neural network. The following topics are covered in this post: Keras neural network concepts for training multi-class classification model Python Keras code for fitting neural network using IRIS dataset Keras Neural Network Concepts for training Multi-class Classification Model Training a neural network for multi-class classification using Keras will require the following seven steps to be taken: Loading Sklearn IRIS dataset Prepare the dataset for training and testing …
How to Setup / Install MLFlow & Get Started
In this post, you will learn about how to setup / install MLFlow right from your Jupyter Notebook and get started tracking your machine learning projects. This would prove to be very helpful if you are running an enterprise-wide AI practice where you have a bunch of data scientists working on different ML projects. Mlflow will help you track the score of different experiments related to different ML projects. Install MLFlow using Jupyter Notebook In order to install / set up MLFlow and do a quick POC, you could get started right from within your Jupyter notebook. Here are the commands to get set up. Mlflow could be installed with …
Top Tutorials – Neural Network Back Propagation Algorithm
Here are the top web pages /videos for learning back propagation algorithm used to compute the gradients in neural network. I will update this page with more tutorials as I do further deep dive on back propagation algorithm. For beginners or expert level data scientists / machine learning enthusiasts, these tutorials will prove to be very helpful. Before going ahead and understanding back propagation algorithm from different pages, lets quickly understand the key components of neural network algorithm: Feed forward algorithm: Feed forward algorithm represents the aspect of how input signals travel through different neurons present in different layers in form of weighted sums and activations, and, result in output / …
I found it very helpful. However the differences are not too understandable for me