Upcoming Machine learning training in Bangalore schedules for Jan & Feb 2020

Machine Learning is betted by organization across the globe as a big game changer which has potential to disrupt businesses. To meet the growing demand of ML professionals across the globe, DataMites provides ML Training and certification courses. DataMites provides online, classroom and LVC training program.

What is Machine Learning

DataMites™ Machine Learning training is conducted in Bangalore the upcoming training dates for weekends and weekdays are.

Schedules Weekends

Jan: 11th, 12th, 18th, 19th, 25th, 26th

Feb: 8th, 9th, 15th, 16th, 22nd, 23rd

Schedules Weekdays

Jan: 13th(Monday to Thursday)

Feb: 10th (Monday to Thursday)

Following are the Topics to be covered for Machine Learning Training

Python for Machine Learning,
Machine Learning Associate,
Machine Learning expert,
Time series foundation,
Model deployment (Flask-API),
Deep Learning -CNN Foundation,

For more information on Machine Learning Training in Bangalore Visit:
https://datamites.com/data-science-course-training-bangalore/

DataMites Training Reviews

Comments

What is the Purpose of a Bottleneck Layer in an Autoencoder?

Autoencoders are an essential part of modern machine learning, widely used in various applications such as data compression, denoising, and feature extraction. Among the components of an autoencoder, the bottleneck layer plays a crucial role in shaping how data is processed and encoded. In this blog post, we'll explore the purpose of the bottleneck layer in an autoencoder, its significance in machine learning, and how understanding it can enhance your machine learning knowledge. Whether you're considering enrolling in a Machine Learning course with live projects or seeking a Machine Learning certification, grasping the concept of the bottleneck layer can be highly beneficial. In the realm of machine learning, autoencoders are a type of neural network designed to learn efficient representations of data. The architecture of an autoencoder consists of two primary parts: the encoder and the decoder. Between these two components lies the bottleneck layer, which is pivotal in determi...

How Do You Apply the Concept of Bagging in Machine Learning?

Machine learning has transformed the way we approach data analysis, making it possible to derive insights and predictions from vast amounts of data. Among the various techniques in machine learning, bagging (Bootstrap Aggregating) stands out as a powerful method for enhancing model performance and stability. In this blog post, we will explore the concept of bagging, its applications, and how you can learn more about it through various educational resources. Understanding Bagging in Machine Learning Bagging is an ensemble learning technique designed to improve the accuracy and robustness of machine learning models. It works by generating multiple subsets of the training data through random sampling with replacement. Each subset is then used to train a separate model, and the final prediction is obtained by averaging the predictions from all models (for regression tasks) or by majority voting (for classification tasks). The primary goal of bagging is to reduce variance and minimize the ...

Fairness-Aware Machine Learning: Tackling Discrimination

Machine learning algorithms are increasingly embedded in critical decision-making processes across various sectors, from finance and healthcare to law enforcement and hiring practices. However, as these algorithms wield significant influence, concerns about fairness and discrimination have come to the forefront. Addressing these issues is crucial to ensure equitable outcomes for all individuals affected by algorithmic decisions. In this blog post, we delve into the concept of fairness-aware machine learning, exploring its importance, challenges, and solutions within the context of today's rapidly evolving technological landscape. Understanding Fairness in Machine Learning Fairness in machine learning refers to the ethical principle of ensuring that algorithms do not systematically disadvantage certain groups based on sensitive attributes such as race, gender, or socioeconomic status. Achieving fairness involves identifying and mitigating biases that may be present in the data used...

Machine Learning 101

Search This Blog