Category Archives: Machine Learning

14 Python Automl Frameworks Data Scientists Can Use

Python automl frameworks

In this post, you will learn about Automated Machine Learning (AutoML) frameworks for Python that can use to train machine learning models. For data scientists, especially beginners, who are unfamiliar with Automl, it is a tool designed to make the process of generating machine learning models in an automated manner, user-friendly, and less time-consuming. The goal of Automl is not just about making it easier for machine learning (ML) developers but also democratizing access to model development. What is AutoML? AutoML refers to automating some or all steps of building machine learning models, including selection and configuration of training data, tuning the performance metric(s), selecting/constructing features, training multiple models, evaluating …

Continue reading

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

Data Analytics – Different Career Options / Opportunities

data analytics career options

Data analytics career paths span a wide range of career options, from data scientist to data engineer. Data scientists are often interested in what they can do with the data that is analyzed, while data engineers are more focused on the analysis itself. Whether you’re looking for a career as a data scientist, data analyst, ML engineer, or AI researcher, there’s something for everyone! In this blog post, we will different types of jobs and careers available to those interested in data analytics and data science. What are some of the career paths in data analytics? Here are different career paths for those interested in data analytics career: Data Scientists: …

Continue reading

Posted in AI, Career Planning, Data analytics, data engineering, Data Science, Machine Learning. Tagged with , , , .

Top 50 Interview Questions for Beginner Data Scientists

interview questions for machine learning

What interview questions should a beginner data scientist prepare for? This is an important question that many interviewees have. If you are going for a data scientist interview and don’t know what interview questions will you be asked, this blog post has some of the common interview questions that will help you excel in your interview. These interview questions are perfect for beginners because they cover basic topics about data science and machine learning and how it works. We hope this list helps! What is the difference between AI, machine learning, deep learning? Do you know how machine learning works? How is machine learning different from statistical modeling techniques like linear …

Continue reading

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

How to Create & Detect Deepfakes Using Deep Learning

create and detect deepfake using deep learning

Deepfake are becoming a more common occurrence in today’s world. What is deepfake and how can you create it using deep learning? This blog post will help data scientists learn techniques for creating and detecting deepfakes, so they can stay ahead of this technology. A deepfake is a video or audio that alters reality by changing the way something appears. For example, someone could place your face onto someone else’s body in a video to make it seem like you were there when you really weren’t. There are many ways that one can detect if a photo has been manipulated with software such as Photoshop or Gimp. What is deepfake? …

Continue reading

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

50+ Machine learning & Deep learning Youtube Courses

In this post, you get an access to curated list of 50+ Youtube courses on machine learning, deep learning, NLP, optimization, computer vision, statistical learning etc. You may want to bookmark this page for quick reference and access to these courses. This page will be updated from time-to-time. Enjoy learning! Course title Course type URL MIT 6.S192: Deep Learning for Art, Aesthetics, and Creativity Deep learning https://www.youtube.com/playlist?list=PLCpMvp7ftsnIbNwRnQJbDNRqO6qiN3EyH AutoML – Automated Machine Learning AutoML https://ki-campus.org/courses/automl-luh2021 Probabilistic Machine Learning Machine learning https://www.youtube.com/playlist?list=PL05umP7R6ij1tHaOFY96m5uX3J21a6yNd Geometric Deep Learning Geometric deep learning https://www.youtube.com/playlist?list=PLn2-dEmQeTfQ8YVuHBOvAhUlnIPYxkeu3 CS224W: Machine Learning with Graphs Machine learning  https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn MIT 6.S897 Machine Learning for Healthcare Machine learning https://www.youtube.com/playlist?list=PLUl4u3cNGP60B0PQXVQyGNdCyCTDU1Q5j Deep Learning and Combinatorial Optimization Deep …

Continue reading

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

MOSAIKS for creating Climate Change Models

MOSAIKS models comparison with Resnet and pre-trained CNN models

In this post, you will learn about the framework, MOSAIKS (Multi-Task Observation using Satellite Imagery & Kitchen Sinks) which can be used to create machine learning linear regression models for climate change. Here is the list of few prediction use cases which has already been tested with MOSAIKS and found to have high model performance: Forest cover Elevation Population density Nighttime lights Income Road length Housing price Crop yields Poverty mapping What is MOSAIKS? MOSAIKS provides a set of features created from Satellite imagery dataset. We are talking about 90TB of data gathered per day from 700+ satellites. These features can be combined with machine learning algorithms to address global …

Continue reading

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

Machine Learning for predicting Ice Shelves Vulnerability

ice shelves machine learning

In this post, you will learn about usage of machine learning for predicting ice shelves vulnerability. Before getting into the details, lets understand what is ice shelves vulnerability and how it is impacting global warming / climate change. What are ice shelves? Ice shelves are permanent floating sheets of ice that connect to a landmass. Most of the world’s ice shelves hug the coast of Antarctica. Ice from enormous ice sheets slowly oozes into the sea through glaciers and ice streams. If the ocean is cold enough, that newly arrived ice doesn’t melt right away. Instead it may float on the surface and grow larger as glacial ice behind it continues to flow into the …

Continue reading

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

Free Online Books – Machine Learning with Python

Python data science

This post lists down free online books for machine learning with Python. These books covers topiccs related to machine learning, deep learning, and NLP. This post will be updated from time to time as I discover more books.  Here are the titles of these books: Python data science handbook Building machine learning systems with Python Deep learning with Python Natural language processing with Python Think Bayes Scikit-learn tutorial – statistical learning for scientific data processing Python Data Science Handbook Covers topics such as some of the following: Introduction to Numpy Data manipulation with Pandas Visualization with Matplotlib Machine learning topics (Linear regression, SVM, random forest, principal component analysis, K-means clustering, Gaussian …

Continue reading

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

Different types of Machine Learning Problems

types of learning problems

This post describes the most popular types of machine learning problems using multiple different images/pictures. The following represent various different types of machine learning problems: Supervised learning Unsupervised learning Reinforcement learning Transfer learning Imitation learning Meta-learning In this post, the image shows supervised, unsupervised, and reinforcement learning. You may want to check the explanation on this Youtube lecture video. Unsupervised Learning Problems In unsupervised learning problems, the learning algorithm learns about the structure of data from the given data set and generates fakes or insights. In the above diagram, you may see that what is given is the unlabeled dataset X. The unsupervised learning algorithm learns the structure of data …

Continue reading

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

Top 10+ Youtube AI / Machine Learning Courses

Online Courses Reskilling

In this post, you get access to top Youtube free AI/machine learning courses. The courses are suitable for data scientists at all levels and cover the following areas of machine learning: Machine learning Deep learning Natural language processing (NLP) Reinforcement learning Here are the details of the free machine learning / deep learning Youtube courses.  S.No Title Description Type 1 CS229: Machine Learning (Stanford) Machine learning lectures by Andrew NG; In case you are a beginner, these lectures are highly recommended Machine learning 2 Applied machine learning (Cornell Tech CS 5787) Covers all of the most important ML algorithms and how to apply them in practice. Includes 3 full lectures …

Continue reading

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

Scikit-learn vs Tensorflow – When to use What?

scikit learn vs tensorflow

In this post, you will learn about when to use Scikit-learn vs Tensorflow. For data scientists/machine learning enthusiasts, it is very important to understand the difference such that they could use these libraries appropriately while working on different business use cases.  When to use Scikit-learn? Scikit-learn is a great entry point for beginners data scientists. It provides an efficient implementation of many machine learning algorithms. In addition, it is very simple and easy to use. You can get started with Scikit-learn in a very easy manner by using Jupyter notebook. Scikit-learn can be used to solve different kinds of machine learning problems including some of the following: Classification (SVM, nearest neighbors, random …

Continue reading

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

Machine Learning – Training, Validation & Test Data Set

Training, validation and test data set

In this post, you will learn about the concepts of training, validation, and test data sets used for training machine learning models. The post is most suitable for data science beginners or those who would like to get clarity and a good understanding of training, validation, and test data sets concepts. The following topics will be covered: Data split – training, validation, and test data set  Different model performance based on different data splits Data Splits – Training, Validation & Test Data Sets You can split data into the following different sets and each data split configuration will have machine learning models having different performance: Training data set: When you …

Continue reading

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

Why use Random Seed in Machine Learning?

random seed value generator

In this post, you will learn about why and when do we use random seed values while training machine learning models. This is a question most likely asked by beginners data scientist/machine learning enthusiasts.  We use random seed value while creating training and test data set. The goal is to make sure we get the same training and validation data set while we use different hyperparameters or machine learning algorithms in order to assess the performance of different models. This is where the random seed value comes into the picture. Different Python libraries such as scikit-learn etc have different ways of assigning random seeds.  While training machine learning models using Scikit-learn, …

Continue reading

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

Precision & Recall Explained using Covid-19 Example

Model precision recall accuracy as function of Covid19

In this post, you will learn about the concepts of precision, recall, and accuracy when dealing with the machine learning classification model. Given that this is Covid-19 age, the idea is to explain these concepts in terms of a machine learning classification model predicting whether the patient is Corona positive or not based on the symptoms and other details. The following model performance concepts will be described with the help of examples.  What is the model precision? What is the model recall? What is the model accuracy? What is the model confusion matrix? Which metrics to use – Precision or Recall? Before getting into learning the concepts, let’s look at the data (hypothetical) derived out …

Continue reading

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

Image Classification & Machine learning

Convolution operation of image and kernel function

In this post, you will learn about how could image classification problems be solved using machine learning techniques. The following are some of the topics which will be covered: How does the computer learn about an image? How could machine learning be used to classify the images? How does the computer learn about an image? Unlike the human beings, the image has to be converted into numbers for computer to learn about the image. So, the question is how can an image be converted into numbers? The most fundamental element or the smallest building block of an image is a pixel. An image can be represented as a set of …

Continue reading

Posted in Machine Learning. Tagged with .

When to use Deep Learning vs Machine Learning Models?

In this post, you will learn about when to go for training deep learning models from the perspective of model performance and volume of data. As a machine learning engineer or data scientist, it always bothers as to can we use deep learning models in place of traditional machine learning models trained using algorithms such as logistic regression, SVM, tree-based algorithms, etc. The objective of this post is to provide you with perspectives on when to go for traditional machine learning models vs deep learning models.  The two key criteria based on which one can decide whether to go for deep learning vs traditional machine learning models are the following: …

Continue reading

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