In the past few years, the number of people entering the field of data science has increased drastically because of higher salaries, an increasing job market, and more demand. Undoubtedly, there are unlimited programs to learn data science, several companies offering in-depth Data Science Bootcamp, and a ton of channels on YouTube that are covering data science content. The abundance of data science content can easily confuse one with where to begin or how to start their data science career. To ease this data science journey for beginners, intermediate, or starters, we are going to list a couple of data science tutorials, crash courses, webinars, and videos. The aim of this blog is to help beginners navigate their data science path, and also help them to determine if data science is the most perfect career choice for them or not.

The late Gabriel Opara was an African poet who wrote a lot of wonderful poems, including the popular "Piano and Drums," which is my favorite. I fell in love with the poem in my high school days when I did literature in English. Having completed the Deep Learning specialization and Tensorflow developer certification courses on Coursera, I decided to put my knowledge of Natural Language Processing into practice by applying AI to the things that interest me, of which African poems are one. Data collection is usually among the early stages of any machine learning project. I got all the data needed for this project from the public web. The data set consists of 14 poems by Gabriel in about 408 lines; the first 10 lines are the titles of the poems.

Neural compression offers a domain-agnostic approach to creating codecs for lossy or lossless compression via deep generative models. For sequence compression, however, most deep sequence models have costs that scale with the sequence length rather than the sequence complexity. In this work, we instead treat data sequences as observations from an underlying continuous-time process and learn how to efficiently discretize while retaining information about the full sequence. As a consequence of decoupling sequential information from its temporal discretization, our approach allows for greater compression rates and smaller computational complexity. Moreover, the continuous-time approach naturally allows us to decode at different time intervals. We empirically verify our approach on multiple domains involving compression of video and motion capture sequences, showing that our approaches can automatically achieve reductions in bit rates by learning how to discretize.

Search Algorithms and Optimization techniques are the engines of most Artificial Intelligence techniques and Data Science. There is no doubt that the Grey Wolf Optimizer is one of the most recent, well-regarded and widely-used AI search techniques. A lot of scientists and practitioners use search and optimization algorithms without understanding their internal structure. However, understanding the internal structure and mechanism of such AI problem-solving techniques will allow them to solve problems more efficiently. This also allows them to tune, tweak, and even design new algorithms for different projects.

Welcome to The Complete 2021 Android Machine Learning Course. In this course, you will learn the use of Machine learning in Android along with training your own image recognition models for Android applications without knowing any background knowledge of machine learning. The course is designed in such a manner that you don't need any prior knowledge of machine learning to it. In modern world app development, the use of ML in mobile app development is compulsory. We hardly see an application in which ML is not being used.

Python is one of the most widely used programming languages in the Machine Learning field. Python has many packages and libraries that are specifically tailored for certain functions, including pandas, NumPy, scikit-learn, Matplotlib, and SciPy. So if you want to learn Machine Learning with Python, this article is for you. In this article, you will find the 12 Best Online Courses for Machine Learning with Python. Now, without wasting your time, let's start finding the Best Online Courses for Machine Learning with Python.

This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will introduce you to problem definition and data preparation in a machine learning project. By the end of the course, you will be able to clearly define a machine learning problem using two approaches. You will learn to survey available data resources and identify potential ML applications. You will learn to take a business need and turn it into a machine learning application.

Quantum machine learning is a rapidly growing field that combines the power of quantum computing with the insights of machine learning. In this advanced guide, we will walk through the steps of building and training a quantum model for machine learning on an advanced dataset. We will use the latest techniques and tools, and we will assume that you have a strong background in both quantum computing and machine learning. The first step in any machine learning project is to load and preprocess the data. For this advanced guide, we will use the MNIST dataset, which consists of images of handwritten digits and their corresponding labels.

Accurately predicting their future performance can ensure students successful graduation, and help them save both time and money. However, achieving such predictions faces two challenges, mainly due to the diversity of students' background and the necessity of continuously tracking their evolving progress. The goal of this work is to create a system able to automatically detect students in difficulty, for instance predicting if they are likely to fail a course. We compare a naive approach widely used in the literature, which uses attributes available in the data set (like the grades), with a personalized approach we called Personalized Student Attribute Inference (PSAI). With our model, we create personalized attributes to capture the specific background of each student. Both approaches are compared using machine learning algorithms like decision trees, support vector machine or neural networks.

This report summarizes the work carried out by the authors during the Twelfth Montreal Industrial Problem Solving Workshop, held at Université de Montréal in August 2022. The team tackled a problem submitted by CBC/Radio-Canada on the theme of Automatic Text Simplification (ATS). In order to make its written content more widely accessible, and to support its second-language teaching activities, CBC/RC has recently been exploring the potential of automatic methods to simplify texts. They have developed a modular lexical simplification system (LSS), which identifies complex words in French and English texts, and replaces them with simpler, more common equivalents. Recently however, the ATS research community has proposed a number of approaches that rely on deep learning methods to perform more elaborate transformations, not limited to just lexical substitutions, but covering syntactic restructuring and conceptual simplifications as well.