REDDY PRASAD E

Machine-Learning-Interview-Preparation

June 27, 2020 – August 13, 2020

Prepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, popula

Machine-Learning-Problems-DataSets

February 16, 2020 – February 12, 2021

We currently maintain 488 data sets as a service to the machine learning community. You may view all data sets through our searchable interface. For a general overview of the Repository, please visit our About page. For information about citing data sets in publications, please read our citation policy. If you wish to donate a data set, please consult our donation policy. For any other questions, feel free to contact the Repository librarians.

Deep-Learning

December 5, 2019 – January 27, 2021

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.

DataSet-for-ML-and-Data-Science

November 15, 2019 – June 13, 2022

Freely Available Data Sets For Real world Problems

Carbon-Dioxide-Emissions-Predication-of-FuelConsumption-Data-Sets

November 7, 2019 – February 27, 2020

which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada

Data-Science-With-Python

October 4, 2019 – May 14, 2021

The Data Science with Python course provides a complete overview of Data Science analytics techniques using Python. A data scientist is one of the hottest fields today and Python is a crucial skill for many Data Science roles. Expand your Data Science knowledge with this Python certification course.

Machine-Learning-with-Scikit-Learn-Python-3.x

June 29, 2019 – June 16, 2021

In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems fall into a few categories: supervised learning, in which the data comes with additional attributes that we want to predict (Click here to go to the scikit-learn supervised learning page).This problem can be either: classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data. An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories a

Zero-to-Hero-with-Python-2.x

April 19, 2019 – April 19, 2019

Learn Python For any one and any Where but i need you time to learn

Machine-Learning-With-R

March 3, 2019 – July 13, 2020

Machine Learning in R

Python-Basic-For-All-3.x

March 2, 2019 – January 12, 2024

We are going to Learn Python, it is a powerful multi-purpose programming language created by Guido van Rossum. It has simple easy-to-use syntax, making it the perfect language for someone trying to learn computer programming for the first time. This is a comprehensive guide on how to get started in Python, why you should learn it and how you can learn it. However, if you knowledge of other programming languages and want to quickly get started with Python.