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This bundle is considered revolutionary in eLearning ground
as it covers all the important elements of artificial intelligence which
empowers you with the immense expertise of the modern world. Also, it discusses
and explain the key topics like dependent and independent variables,
regression, estimate data, predict data and building active work structure etc.
The bundle consist of 8 effective courses and each one is well-designed by considering your learning capacity and adopting the best practices from industry experts. The aim of this bundle is to provide quicken way to enhance your proficiency within the short span of time. After completing the course, you will jump up to your next professional level. The key features of this bundle are following:
Most Important information:
Rating 4.5 (50 Reviews)
Learn Regression techniques, Learn Linear & Multi-linear Regression, python coding, variables, processes, models and much more.
In this course, you will learn the statistics through Linear
Regression which is a linear approach for modeling the relationship between a
scalar dependent variable Y and one or more explanatory variables (or
independent variables) signified X. Also, it will define the case of one
explanatory variable is called simple linear regression. If you are having more
than one explanatory variable, the process is called multiple linear
regression.
After enrolling in this course you will understand the Linear Regression and the relationships which are modeled using linear predictor functions. Moreover this course has divided into 2 section for your better understanding. The key topics of this course are mentioned below:
Most Important information:
Rating 4.5 (50 Reviews)
Learn Regression techniques, techniques for polynomial and logistic regression python coding, best practices, concepts, implementation and much more.
In this course your will understand the statistics, Logistic
Regression, or log it regression, important models of regression. Also you will
know the implementation categorically of dependent variable (DV) is. You will
explore the case of a binary dependent variable, where the output can take only
two values, "0" and "1", which represent result such as
pass/fail, win/lose, alive/dead or healthy/sick, and running/stop etc.
Moreover this course will provide the complete guideline of cases where the dependent variable has more than two outcome categories may be analyzed in multinomial logistic regression. After enrolling yourself in this course, you will be able to enhance your productivity by understanding the Polynomial and Logistic Regression. For your better understanding, course has divided into 2 section so that you can easily understand step by step. Following are the key point of the course.
Rating 4.5 (50 Reviews)
Learn KNN, classification methods, k-Nearest Neighbors & Bayes Classification &code in python programming, coding, concept and much more.
In this course you learn the power of k-Nearest Neighbors
& Naive Bayes Classification Methods. In pattern respect, the k-nearest neighborâ€™s
algorithm (k-NN) is a non-parametric method frequently used for classification
and regression.
K-NN is a type of instance-based learning, or lazy learning,
where the purpose is only come close to locally and all calculation is postponed
until classification. The k-NN algorithm is among the easy and simplest of all
machine learning algorithms. In the statistics and computer science works,
Naive Bayes models are well-known under a variety of names, including simple
Bayes and independence Bayes.
Moreover, In this course you will understand how to classify datasets by k-Nearest Neighbors Classification Method to find the correct class for data and reduce error. Enroll yourself into this course and be the best version of you. The key topics of this course are mentioned below:
Rating 4.5 (50 Reviews)
Learn classification methods, vector machine, Bayes classification, Python coding, procedures, processes, concept and much more
This course empowers you with complete understanding of Vector Machine & Logistic Classification
Methods are considered very important in artificial intelligence. This course
is also a perfect set of practical example of each marked as belonging
to one or the other of two categories, an SVM training algorithm builds a model that consigns new
examples to one category or the other, making it a non-probabilistic binary
linear classifier.
Furthermore, you will also learn about how to perform linear classification, SVMs that can professionally perform a non-linear classification using what is called the kernel trick, covertly mapping their inputs into high-dimensional feature spaces. Following are the key topics of this course.
Rating 4.5 (50 Reviews)
Learn how to make Multilayer Perceptron Neural Network by using Scikit and explore Keras Libraries and Python, ANNs, rules and much more.
In this course, you will learn about artificial neural
network (ANNs) and its computing system.
An ANN is based on a collection of linked units or nodes
called artificial
neurons which lightly model the neurons in a biological
brain. Each linking, like the synapses in a biological brain, can conduct a
signal from one artificial neuron to another. An artificial neuron that obtains
a signal can process it and then signal added artificial neurons related to it.
In this Course you will learn the multilayer perceptron (MLP) neural network by using Scikit learn & Keras libraries and Python. Furthermore, you will also be educated to classify datasets by MLP Classifier to find the correct classes for them. Next you go further. You will learn how to Predict time series model by using neural network in Keras environment. The key features of this course are mentioned below:
Rating 4.5 (50 Reviews)
Learn artificial intelligence with Recurrent Neural Network and LSTMs by using Keras Libraries and Python coding.
In this course you learn the complete knowledge about how to
build RNN and LSTM network in python and keras
environment. It starts with basic examples and move forward to more difficult
examples. Moreover, it will also defined procedures of Keras to Predict Google stock price, Keras to
predict NASDAQ Index precisely
and much more.
Rating 4.5 (50 Reviews)
Learn artificial intelligence, optimization problem using Genetic Algorithm Optimization Technique and much more
In this course, you will learn about the basic theory
behind Genetic Algorithm Optimization Technique.
While going into details, you will get the various ways to solve optimization
problems. In this course aims to provide the best learning methods and theory behind Genetic Algorithm Optimization
Method.
Then we go more you will learn how to use python and deap
library to solve optimization problem and find Min/Max points for your anticipated functions.Genetic algorithms are commonly used
to generate high-quality solutions to optimization and search problems by
relying on bio-inspired operators such as mutation, crossover and selection.
Rating 4.5 (50 Reviews)
Learn artificial intelligence, problem solving techniques for optimization using Particle Swarm Optimization and much more.
In this course, you will learn the Particle Swarm Optimization methodology with complete understanding.
As we know, Particle
Swarm Optimization (PSO)
is a computational method that optimizes a problem by iteratively trying to
improve a candidate solution with regard to a given measure of quality. It
solves a problem by having a population of candidate solutions, or particles, and moving
these particles around in the search-space according to simple mathematical
formula over the particle's position
and velocity.
Furthermore, you will learn how to use python and deap to
optimize simple
function precisely and deap to solve Rastrigin standard function accurately.
Rating 4.5 (50 Reviews)