Everyone can contribute to the efforts of turning data into valuable information. Thus, even if your aspirations are not to be a data scientist, ... Understanding how we can derive valuable information from the data has become an everyday expectation. Previously, organizations looked up to data scientists. Everyone can contribute to the efforts of turning data into valuable information. Thus, even if your aspirations are not to be a data scientist, open yourself the door to these projects by gaining so-necessary intuitive understanding.

In this course I will cover, how to develop a Credit Card Fraud Detection model to categorize a transaction as Fraud or Legitimate with very high accuracy using different Machine Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model. This course will walk you through the initial data exploration and understanding, data analysis, data preparation, model building and evaluation. We will explore RepeatedKFold, StratifiedKFold, Random Oversampler, SMOTE, ADASYN concepts and then use multiple ML algorithms to create our model and finally focus into one which performs the best on the given dataset. I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.

Learn, build and implement the most powerful AI model at home. Two months ago we discovered that a very new kind of AI was invented. The kind of AI which is based on a genius idea and that you can build from scratch and without the need for any framework. We checked that out, we built it, and... the results are absolutely insane! This game-changing AI called Augmented Random Search, ARS for short.

Created by Coding Minutes, Prateek Narang, Apaar Kamal 55.5 hours on-demand video course Equip yourself with essential programming techniques required for ACM-ICPC, Google CodeJam, Kickstart, Facebook HackerCup & more. Welcome to Competitive Programming Essentials – the ultimate specialisation on Algorithms for Competitive Coders! The online Competitive Programming Essentials by Coding Minutes is a highly exhaustive & rigorous course on Competitive Programming. The 50 hours course covers the breadth & depth of algorithmic programming starting from a recap of common data structures, and diving deep into essential and advanced algorithms. The course structure is well-researched by instructors who not only Competitive Coders but have worked with companies like Google & Scaler.

Humans ability to transfer knowledge through teaching is one of the essential aspects for human intelligence. A human teacher can track the knowledge of students to customize the teaching on students needs. With the rise of online education platforms, there is a similar need for machines to track the knowledge of students and tailor their learning experience. This is known as the Knowledge Tracing (KT) problem in the literature. Effectively solving the KT problem would unlock the potential of computer-aided education applications such as intelligent tutoring systems, curriculum learning, and learning materials' recommendation. Moreover, from a more general viewpoint, a student may represent any kind of intelligent agents including both human and artificial agents. Thus, the potential of KT can be extended to any machine teaching application scenarios which seek for customizing the learning experience for a student agent (i.e., a machine learning model). In this paper, we provide a comprehensive and systematic review for the KT literature. We cover a broad range of methods starting from the early attempts to the recent state-of-the-art methods using deep learning, while highlighting the theoretical aspects of models and the characteristics of benchmark datasets. Besides these, we shed light on key modelling differences between closely related methods and summarize them in an easy-to-understand format. Finally, we discuss current research gaps in the KT literature and possible future research and application directions.

This tutorial is an extension of Method Of Lagrange Multipliers: The Theory Behind Support Vector Machines (Part 1: The Separable Case)) and explains the non-separable case. In real life problems positive and negative training examples may not be completely separable by a linear decision boundary. This tutorial explains how a soft margin can be built that tolerates a certain amount of errors. In this tutorial, we'll cover the basics of a linear SVM. We won't go into details of non-linear SVMs derived using the kernel trick.

Welcome to Time Series Analysis, Forecasting, and Machine Learning in Python. Time Series Analysis has become an especially important field in recent years. With inflation on the rise, many are turning to the stock market and cryptocurrencies in order to ensure their savings do not lose their value. COVID-19 has shown us how forecasting is an essential tool for driving public health decisions. Businesses are becoming increasingly efficient, forecasting inventory and operational needs ahead of time.

Covering all the fundamental concepts of using ML models inside React Native applications, this is the most comprehensive React Native ML course available online. The important thing is you don't need to know background working knowledge of Machine learning and computer vision to use ML models inside React Native and train them. Starting from a very simple example course will teach you to use advanced ML models in your React Native ( Android & IOS) Applications. So after completing this course you will be able to use both simple and advanced Tensorflow lite models in your React Native( Android & IOS) applications. We will use React Native CLI but course will also guide you if you just have the expo knowledge.

Learn the approaches and tools to tune hyperparameters and improve the performance of your machine learning models.

Video Transitions are a post-production technique used in film or video editing to connect one shot to another. In this course, you'll learn how to create Colorful Video Transitions in After Effects CC and then you will learn how to export and use them in your Video Editing programs such as Premiere Pro CC, Final Cut, … You don't need any previous knowledge of Adobe After Effects, You will learn all the essential techniques you need to create your first Colorful & Shape Transition. In this course, you will learn Motion Graphics in After Effects so you can create stunning and professional video transitions quickly. Why you should use Video Transitions? While working on putting together great video work, you will come across breaks in scenes where they need to come back together.