Mobile AI Development Learning Path

This course covers emerging technologies in real estate, including AI and machine learning, and their applications in areas like market analysis, fraud detection, and improving customer experience.

This course explores the transformative power of Generative Artificial Intelligence (GenAI) in mobile app development. It is designed for both experienced mobile app developers and newcomers to the field. Participants will learn how GenAI can optimize efficiency in mobile app testing, user interface optimization, performance analysis, and automation.

A hands-on course on building an AI-enhanced recipe application using Flutter Flow. It covers user authentication, database management, and integrating OpenAI's API for smart recipe suggestions.

Multi-modal AI Applications

A step-by-step guide on how to integrate TensorFlow Lite with Flutter to build AI-powered mobile apps for both iOS and Android from a single codebase. It covers using pre-trained models and training custom models.

Build 2 complete projects start to finish -- with each step explained thoroughly by instructor Nimish Narang from Mammoth Interactive.Hands-On Neural Networks: Build Machine Learning Models was funded by a 1 project on Kickstarter Nimish is our cross-platform developer and has created over 20 other courses specializing in machine learning, Java, Android, Sprite Kit, iOS and Core Image for Mammoth Interactive. When he's not developing, Nimish likes to play guitar, go to the gym and laze around at the beach. Project 1 -- Learn to construct a model for credit card fraud detection. Our model will take in a list of transactions, some fraudulent and some legitimate. It will output the percentage at which it can calculate fraudulence and legitimacy, how accurate it is. We will also modify the model so that it output whether a specific transaction is fraudulent or legitimate if we pass them in one by one.We will explore a dataset so that you fully understand it, and we will work on it. It's actually pretty hard to find a dataset of fraudulent/legitimate credit card transactions, but we at Mammoth Interactive have found everything for you and curated a step by step curriculum so that you can build alongside us.We will manipulate the dataset so that it will be easy to feed into our model. We will build a computational graph with nodes and functions to run input through the mini neural network.Machine Learning Projects Using TensorFlow -- Mammoth Interactive Project 2 -- Learn to build a simple stock market prediction model that will predict whether the price stock will go up or down the next morning based on the amount of volume exchange for a given day Any kind of glo

If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass™ is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you’ll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We’re approaching a new era where only apps and games that are considered “smart” will survive. (Remember how Blockbuster went bankrupt when Netflix became a giant?) Jump the curve and adopt this innovative approach; the Complete iOS Machine Learning Masterclass™ will introduce Machine Learning in a way that’s both fun and engaging. In this course, you will: Master the 3 fundamental branches of applied Machine Learning: Image & Video Processing, Text Analysis, and Speech & Language Recognition Develop an intuitive sense for using Machine Learning in your iOS apps Create 7 projects from scratch in practical code-along tutorials Find pre-trained ML models and make them ready to use in your iOS apps Create your own custom models Add Image Recognition capability to your apps Integrate Live Video Camera Stream Object Recognition to your apps Add Siri Voice speaking feature to your apps Dive deep into key frameworks such as coreML, Vision, Core Graphics, and Game Play Kit. Use Python, Keras, Caffee, TensorFlow, sci-kit learn, libsvm, Anaconda, and Spyder–even if you have zero experience Get FREE unlimited hosting for one year And more! This course is also full of practical use cases

En este curso veremos cómo implementar nuestros modelos de inteligencia artificial en una aplicación Android utilizando TensorFlow Lite. El TensorFlow lite es un conjunto de herramientas que nos ayuda a ejecutar modelos de TensorFlow en dispositivos móviles, integrados y de IoT. Esta nos permitirá realizar la inferencia en un dispositivo móvil. Implementaremos desde cero un modelo de “Regresión Lineal” en Python y lo llevaremos a Android utilizando TensorFlow Lite. Implementaremos desde cero un modelo de “Regresión en Múltiple” con normalización de datos y lo llevaremos a Android utilizando TensorFlow Lite. Implementaremos desde cero una “Red Neuronal Convolucional” para clasificar imágenes y llevaremos el modelo a Android utilizando TensorFlow Lite. Implementaremos un ejemplo de detección de objetos basado en la “Red Neuronal Convolucional” Mobile Net. Implementaremos desde cero una “Red Neuronal Artificial” para clasificar dígitos utilizando el dataset MNIST y llevaremos el modelo a Android para reconocer dígitos del 0 al 9 utilizando TensorFlow Lite. Entrenamiento del algoritmo Yolo en Google Colab y despliegue en Aplicación Android. Veremos también como descargar cientos de imágenes para elaborar datasets de manera automática. Implementaremos la técnica de “Data Augmentation” para incrementar la precisión de nuestros modelos de clasificación de imágenes. Además implementaremos OpenCV para segmentar y reconocer digitos escritos a mano.Los invito cordialmente a tomar el curso en donde aprenderán a implementar sus modelos de inteligencia artificial en una aplicación Android.