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Machine Learning Assignment Help
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Machine Learning Assignment Help Services
Machine Learning
Machine learning programming is quite complicated, and there is nothing wrong or unusual to look for assignment help to deal with it
Deep Learning
Our Deep Learning expert will provide help in any type of programming Help, tutoring, Deep Learning project development
Computer Vision
Our dedicated team of Computer vision assignment expert will help and will guide you throughout your learning Computer vision journey.
TensorFlow
Codersarts TensorFlow expert offer the best quality TensorFlow coding or programming experts.
Data Analysis
Interactive Data Visualizations with d3, Google Visualization API, Charts, Data Visualization In R.
Visualization
Hire Us for Build beautiful interactive maps, explore your data by over plots, and design, dynamic charts
Image Processing
Image processing is the process of partitioning a digital image into multiple segments
Object Detection
computer vision technique that allows us to identify and locate
objects in an image or video
What is Machine Learning?
Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. Machine learning uses a variety of algorithms that iteratively learn from data to improve, describe data, and predict outcomes.
Why Machine learning is important?
As a Student or developer, you can't ignore or skip the java programming language it's not just a programming language instead you can see Machine Learning a technology serving almost every area of technologies. One would say ML is complete package journey from student to the developer. It provides great flexibility while choosing a platform. The large volume of data is generated every day by billions of user using the internet. For a human being, it's not possible to process data and get insight from data. These data is very important and profitable for a business to guide customer behavior and activity. Machine learning is a collection of advanced algorithms which process data. If short, In machine learning.
In Machine Learning pipeline we perform following main tasks:

Data Collections

Data Preprocessing

Feature Extraction

Model Training

Model Evaluation

Make Prediction
The two main challenges of machine learning are data preparation and accurate data collection. Then divide data into labels/Fields or properties. The second task is to select the best model suited for data. To learn and understand machine learning need statistics skills to predict and evaluate data mining results and predictions.
Type Of Machine Learning Algorithms
Supervised Learning algorithm
In supervised learning, data is composed of examples where each example has an input element that will be provided to a model and an output or target element that the model is expected to predict. Classification is an example of a supervised learning problem where the target is a label, and regression is an example of a supervised learning problem where the target is a number.
Following are the supervised learning algorithms as given below:

Logistic regression Classifier

Decision Tree Classifier

Random Forest Classifier

K nearest neighbor Classifier

Support Vector Classifier (SVC)

Naive Bayes Classifier

AdaBoost Classifier

Gradient Boosting Classifier

XGB Classifier

Linear Regression algorithm
Unsupervised Learning algorithm
Unsupervised learning is a type of machine learning in which models are trained using an unlabeled dataset and are allowed to act on that data without any supervision. In unsupervised learning not use the target variable. Unsupervised learning cannot be directly applied to a regression or classification problem because unlike supervised learning, we have the input data but no corresponding output data. The goal of unsupervised learning is to find the underlying structure of the dataset, group that data according to similarities, and represent that dataset in a compressed format.
Types of Un Supervised learning

Clustering

Association
Clustering
It is a method of grouping the objects into clusters based on the object with most similarities that remains in a group and has less or no similarities with the object of the other group. clustering analysis finds the commonalities between the data objects and categorizes them as per the presence and absence of those commonalities.
Association
An association rule is an unsupervised learning method which is used for finding the relationships between variables in the large database. It determines the set of items that occur together in the dataset. Association rule makes marketing strategy more effective.

KMeans Clustering

Hierarchical Clustering

Principal component analysis

Singular value decomposition

Independent component analysis

Anomaly detection

Neural network

Apriori algorithm

Singular value decomposition (SVD)
Reinforcement Learning algorithm
Reinforcement Learning is a reward based Machine learning technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions. For each good action, the agent gets positive reward, and for each bad action, the agent gets negative reward or penalty. In Reinforcement Learning, the agent learns automatically using reward without any labeled data.
Reinforcement learning is a type of machine learning method where an intelligent agent (computer program) interacts with the environment and learns to act within that.
SemiSupervised Learning algorithm
This type of algorithm is neither fully supervised nor fully unsupervised. This type of algorithm uses a small supervised learning component i.e small amount of prelabeled annotated data and large unsupervised learning component i.e. lots of unlabeled data for training.
A SemiSupervised algorithm assumes the following about the data –

Continuity assumption : The algorithm assumes that the points which are closer to each other are more likely to have the same output label.

Cluster assumption : The data can be divided into discrete clusters and points in the same cluster are more likely to share an output label.

Manifold assumption : the data lie approximately on a manifold of much lower dimension than the input space. This assumption allows the use of distances and densities which are defined on manifolds.
Gradient Boosting algorithms
Gradient boosting algorithms are supervised machine learning techniques for classification and regression problems. It is one of the most powerful algorithms for building predictive analysis.
Gradient boosting involves three elements.

A loss function to be optimized.

A weak learner to make predictions.

An additive model to add weak learners to minimize the loss function.
Many models are trained sequentially. Each new model gradually minimizes the loss function of the whole system using Gradient Descent method. The main idea behind this algorithm is to construct new base learners which can be optimally correlated with the negative gradient of the loss function, relevant to the whole ensemble.

Gradient Boosting Machine (GBM)

Extreme Gradient Boosting Machine (XGBM)

LightGBM

CatBoost
Dimensionality Reduction Algorithms.
Dimensionality reduction is an unsupervised learning technique. In machine learning classification problems, there are often too many factors on the basis of which the final classification is done. These factors are basically variables called features. The higher the number of features, the harder it gets to visualize the training set and then work on it. Sometimes, most of these features are correlated, and hence redundant. This is where dimensionality reduction algorithms come into play. Dimensionality reduction is the process of reducing the number of random variables under consideration, by obtaining a set of principal variables. It can be divided into feature selection and feature extraction.
Examples:

Principal Component Analysis

Singular Value Decomposition

Linear Discriminant Analysis

Isomap Embedding

Locally Linear Embedding

Modified Locally Linear Embedding
Pretrained model / Transfer Learning
A pretrained model is a model created by someone else to solve a similar problem. Instead of building a model from scratch to solve a similar problem, you use the model trained on other problems as a starting point. Using the pretrained model techniques is called transfer learning. It has a special feature train for one task and we can use the knowledge for another related task.
Some pretrained models' names.

VGG16

MobileNetV2

InceptionResNetV2

InceptionV3

ResNet50.
Linear Regression
It is a method of modeling the relationship between dependent variable y and independent variable X. Variable may be one or more. When there is only one independent/explanatory variable, it is called Simple Linear Regression and for more variables, it is called Multiple Linear Regression.
Logistic Regression
It is used to determine discrete values like binary value, yes/no or false/true, based on a set of independent variables.
Here some examples are:

Spam detection: predict if mail is spam or not

Credit card fraud: predict if credit card transaction is fraud or not

Marketing: if the user will buy a product or not
Decision Trees
It is very famous and widely used supervised learning algorithm.
Support Vector Machine(SVM)
It is used for the classification method. In this method, each plot is placed in ndimensional space. Here n represents the number of features.
Naive Bayes (NB)
It is also be used for the classification method. This classification method assumes that the features in this method are independent. This classifier assumes that the presence of any particular feature in this class is unrelated to presence of any other given feature.
KNN (kNearest Neighbors)
It supports both, classification and a regression method. But it large support for classification problems. It finds the distance from one given instance variable points. It works in the following way:
Loads the data, initializes the value of k. And follow given below procedure

Find the distance from instance data and rows in training data.

Sorts the calculated distance in ascending order.

Gets top k rows from the sorted array.

Gets the most frequent class of these rows.

Returns predicted class.
kmeans
It supports the unsupervised learning algorithm, which is used for unlabelled data. Kmeans is the simple and easy way to classify a given data set through a number of clusters where k is a number of assumed clusters. How kmeans works:

Find knumber of points from each cluster, here cluster is work as a centroid.

Each data point forms a cluster with the closest centroid.

After this find the centroid of each cluster based on members in that cluster. Repeats this step to find new centroids.

Finds the nearest distance for each data point from new centroids. Associates it with new kclusters.
And other Algorithms likes

Random Forest

Dimensionality Reduction
All these advantages make and remain one of the most popular AI technologies to handle statistical algorithms or powerful usable data predictions in industries. In the internet domain, java’s popularity has increased tremendously, especially on the serverside of the internet. Machine Learning landed top among the mostused in artificial intelligence.
Feature extraction Techniques in Machine Learning
Feature extraction involves reducing the number of resources required to describe a large set of data.
When the input data to an algorithm is too large to be processed and it is suspected to be redundant
then it can be transformed into a reduced set of features (also named a feature vector). Determining a subset of the initial features is called feature selection/extraction.The selected features are expected to contain the relevant information from the input data, so that the desired task can be performed by using this reduced representation instead of the complete initial data.

Bag of words

Autoencoders

Countvectorizer

TfIdf Vectorizer

Hashing Vectorizer

Kernel PCA

Partial least squares

Semidefinite embedding

Latent semantic analysis (LSA)

tdistributed Stochastic Neighbor Embedding (tSNE)

Multilinear subspace learning

Nonlinear dimensionality reduction

Multifactor Dimensionality reduction

Locally Linear Embedding (LLE)

Linear Discriminant Analysis (LDA)

Principal Component Analysis (PCA)

Multilinear Principal Component Analysis

Independent Component Analysis (ICA)
Most frequently used libraries in Machine Learning Assignment
Sklearn
Scikitlearn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forest, gradient boosting, kmeans and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
SciPy
SciPy is a free and opensource Python library used for scientific computing and technical computing. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
NumPy
NumPy is a library for the Python programming language, adding support for large, multidimensional arrays and matrices, along with a large collection of highlevel mathematical functions to operate on these arrays.