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Machine Learning Assignment Help
What is Machine Learning?
Machine learning is a part of Artificial Intelligence in computer science that uses statistical techniques to learn from the given set of data and based on those results, it predicts output for some other data. There are many areas where machine learning is being used widely. Healthcare, fraud detection, personalised recommendation, finance etc.
The process of machine learning can be explained in following steps:
- Identifies relevant data set and prepares them for analysis
- Chooses the type of machine learning algorithm to use
- Builds an analytical model based on chosen algorithm
- Trains the model on the test data sets
- Runs the model to generate the scores and other findings
Machine learning algorithms falls into three categories:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
In Supervised Learning, dependent variable (target) is to be predicted based on the give set of independent variables (predictors). We create a model using these variables. We keep training our model until desired accuracy is not achieved.
In supervised learning, there are mainly two types of algorithms: classification and regression. Classification is divided into binary classification and multi-class classification. Binary classification distinguishes exactly two classes. For example, classify the email that is spam or not spam. The answer falls in either yes or no. In Multi-class classification, it distinguishes more than two classes. For example, identify the fruits based on its features such as shape, colour and radius. Apple, kivy, mango can be different labels to be predicted. In Regression task, the goal is to predict the continuous number. Example of regression is: prediction of person%s annual income based on his gender, age, education, city they live in. Another example is to predict how much it will rain this year based on results of last 10 years.
Some of the popular supervised learning Algorithms are k-Nearest Neighbor (KNN), Linear regression, Naive Bayes Classifiers (NB), Decision trees, Random forest, Support Vector Machine (SVM) and Neural Network.
In Unsupervised Learning there is no need for dependent variable. This method can be used for complex processing tasks such as face recognition, image recognition, natural language processing and speech to text etc. K-means clustering, hierarchical clustering are popular unsupervised learning algorithms.
At Assignment Solution Guru, we provide solutions to both types of machine learning assignments using Python and using R. As per the requirements, we develop the solution with classification, regression, clustering, etc.
IDE (Editors) we use for Machine Learning programming
- Jupyter/IPython Notebook
For Python machine learning assignment solutions, we mostly make use of Jupyter notebook and PyCharm. They are the best IDE for developing solutions with Python.
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Essential Tools for Machine Learning
To install the packages related to data science and machine learning, Anaconda tool is very useful. Anaconda is a free and open source distribution for Python. It comes with installation packages and installation tools. It has set of standard Python libraries as well as numerous third party libraries like numpy, scipy, matplolib, IPython, pandas, scikit-learn, etc.
At Assignment Solution Guru, we use Anaconda to build machine learning assignment solution and we also ask the students to use it as it solves all the installation (building/compiling) issues which you face with numpy, scipy and other libraries.
Essential Libraries for Machine Learning
NumPy is the most fundamental package for science and statistical computing in Python. It is fundamental data structure that works for multidimensional array, linear algebra functions, random generators etc.
SciPy is free and open source library for Python which is used for scientific computing and technical computing. It can be used for linear algebra, integration, interpolation, signal and image processing.
Matplotlib is the 2D plotting library in Python. To generate plots, histograms, bar charts and scatterplots, Matplotlib is used.
Pandas is the Python library for data analysis. It is the tool for reading and writing data. It has fast and efficient object design called DataFrame, high performance on merging and joining the data sets and time series functionality.
Scikit-learn is an open source project. It contains number of machine learning algorithm. It is the most popular tool and prominent Python library for machine learning.
Many students ask us for machine learning assignment help which requires use of different libraries, like seaboarn, nltk, etc. We happily use those libraries as per their need and provide the best machine learning assignment solution.
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Machine Learning Algorithms
Linear regression is a method of modeling relationship between dependent variable
y and independent variable
X. There can be one or more independent variables. When there is only one independent/explanatory variable, it is called Simple Linear Regression and for more variables, it is called Multiple Linear Regression.
Example of linear regression is: as the age of a kid increases, his height increases.
Visit Introduction to Linear Regression for more details.
Logistic regression is used to estimate discrete values (binary values with yes/no or true/false stats) based on a set of independent variables.
Some of the binary classification 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 user will buy a product or not
Visit Introduction to Logistic Regression for more details.
This is a very famous and widely used supervised learning algorithm which is used in classification problems. It works for both categorical and continuous variables.
SVM (Support Vector Machine)
This is a classification method. In this method, each plot is placed in
n-dimensional space. Here
n represents the number of features. For example, if we have two features, we plot two variables in two dimensional space where each point will have two co-ordinates. These coordinates are known as Support Vector. After plotting the points, we can find one or more lines that splits the data in some groups. The line is called Classifier. According to where the data lies we can classify the data. We can say that it does the separation of the data into the classes.
Naive Bayes (NB)
This is a classification method. Naïve Bayes works on Bayes% theorem. 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. All features contribute independently to the probability of that class. Naive Bayes classifier calculates the probabilities for every factor. From that, it selects outcome with highest probability.
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KNN (k-Nearest Neighbors)
It is both: a classification and a regression method. However it is widely used for classification problems. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure. It works in following way:
Loads the data, initialises the value of
k. Then it repeats the steps to get the predicted class as given below.
- Calculates the distance from test data and rows in training data.
- Sorts the calculated distance in ascending order.
- Gets top
krows from the sorted array.
- Gets the most frequent class of these rows.
- Returns predicted class.
k-means is a type of unsupervised learning algorithm, which is used for unlabelled data. K-means is the simple and easy way to classify a given data set through a number of clusters where
k is number of assumed clusters. In k-means we have clusters and each cluster has its own centroid. Here is the way how k-means works:
- k-means picks
knumber of points for each cluster known as centroid.
- Each data point forms a cluster with closest centroid. Finds the centroid of each cluster based on members in that cluster. Repeats this step to find new centroids.
- Finds closest distance for each data point from new centroids. Associates it with new k-clusters.
Checkout the definitions of some of the machine learning assignments we have solved.
Random Forest or Random Decision Forest is supervised classification algorithm. It can also be used for the regression. By its name random forest is a collection of decision trees. This collection of decision trees is known as forest. It creates the forest in some random way.
In random forest, we can resolve the problem of over fitting if there are enough trees in the forest. This algorithm can handle missing values and also works for categorical values.
This algorithm has two steps. One is to create the random forest and other is to make the predictions. Random forest is the best algorithm for feature engineering. It can be used to identify most important features.
Dimensionality reduction or dimension reduction is the process of reducing number of random variables under consideration by obtaining a set of principal variables. There are two components for dimension reduction.
We try to find the subset of original set of variables, to get the smaller subset.
This reduces data in high dimensional space to lower dimension space.
Method of Dimensionality Reduction
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Generalized Discriminant Analysis (GDA)
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Most of the students ask us to develop the Python machine learning assignment solution using Jupyter notebook as IDE and using numpy, pandas, sklearn and matplotlib libraries. We provide fully commented solution with graphs which help understand the code, the data and the process of finding the solution in machine learning.
We have developed solution for lots of machine learning assignments and homeworks in Python. Please visit our page Python Assignment Help for more details about different types of Python assignments.