Bienvenidos
IPN-Dharma IA Lab
Es una iniciativa de Laboratorio de Inteligencia Artificial del CIC del IPN con la colaboración de DHARMA para motivar a investigadores, profesores y estudiantes a aprovechar los cursos, recursos y herramientas de las principales plataformas tecnológicas de la industria en las áreas de Aprendizaje Automático, Ciencia de Datos, Computación en la Nube, Inteligencia Artificial e Internet de las Cosas con el propósito de generar una experiencia práctica a través de un modelo de aprendizaje entre pares y por objetivos.
Nivel 2: Conocimiento Contextual
AI Engineering Professional
To stay competitive, organizations use machine learning algorithms and deep learning neural networks to provide data driven actionable intelligence for their businesses.
In this program, you will master fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python. You will apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers.
In this program, you will master fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python. You will apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers.
Cursos en este programa
1) Machine Learning with Python
This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You will learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.
Explore many algorithms and models:
Explore many algorithms and models:
- Algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
- Models: Train/Test Split, Root Mean Squared Error, and Random Forests.
Esfuerzo estimado 12 horas
Idioma español e inglés
2) Scalable Machine Learning on Big Data using Apache Spark
This course will empower you with the skills to scale data science and machine learning tasks on Big Data sets using Apache Spark. Most real world machine learning work involves very large data sets that go beyond the CPU, memory and storage limitations of a single computer.
Apache Spark is an open source framework that leverages cluster computing and distributed storage to process extremely large data sets in an efficient and cost effective manner. Therefore an applied knowledge of working with Apache Spark is a great asset and potential differentiator for a Machine Learning engineer.
Apache Spark is an open source framework that leverages cluster computing and distributed storage to process extremely large data sets in an efficient and cost effective manner. Therefore an applied knowledge of working with Apache Spark is a great asset and potential differentiator for a Machine Learning engineer.
3) Deep Learning & Neural Networks with Keras
This course will introduce you to the field of deep learning and teach you the fundamentals. You will learn about some of the exciting applications of deep learning, the basics for neural networks, different deep learning models, and how to build your first deep learning model using the easy yet powerful library Keras.
After completing this course, learners will be able to describe what a neural network is, what a deep learning model is, and the difference between them. Demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines. Demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks. Build deep learning models and networks using the Keras library.
After completing this course, learners will be able to describe what a neural network is, what a deep learning model is, and the difference between them. Demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines. Demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks. Build deep learning models and networks using the Keras library.
4) Deep Neural Networks with PyTorch
The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch's tensors and automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression; followed by Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered.
5) Deep Learning Models with TensorFlow
The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this course you will use TensorFlow library to apply deep learning to different data types in order to solve real world problems.
After completing this course, learners will be able to explain foundational TensorFlow concepts such as the main functions, operations and the execution pipelines. Describe how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. Understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.
After completing this course, learners will be able to explain foundational TensorFlow concepts such as the main functions, operations and the execution pipelines. Describe how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. Understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.
6) AI Capstone Project with Deep Learning
In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model and validate it. Learners will then present a project report to demonstrate the validity of their model and their proficiency in the field of Deep Learning.