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Machine Learning is the intersection of computer science and applied mathematics and statistics. Machine Learning can solve many real world problems and it is extensively used in medicine, finance and banking. Machine learning requires knowledge of statistics, programming and applied calculus.
Machine Learning is a forever evolving field and lot’s research are being performed every day here. Many students who are pursuing machine learning courses are searching for a skilled person who can solve their assignment problems and fetch good accuracy score. Machine learning is a vast area of science and sometimes it’s difficult for a single person to know everything in machine learning unless he/she has certain job experience in top tech companies.
Machine Learning Assignment Urgent Help
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In most of the courses python and R programming language is used. It is very important for the student to understand the basics of python. In some cases student seek for python assignment help at first then pursue machine learning assignment help. Opting python at first helps them to get the basics or python programming and then they can easily understand any coding related stuff.
Our experts are experienced in machine learning and they can easily help you with your assignment problems, they know where a particular algorithm overfits and where it’s underfits.
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What is Machine Learning?
Machine Learning is an important aspect of computer and applied mathematics and computation where new discoveries are happening on a weekly basis. Machine Learning provides machines the ability to learn and improve without being explicitly programmed to.
Instead of knowledge driven approach, machines follow data driven approach to solve problems.
Using techniques of machine learning, we can nurture machines to perform classification, predictions, recommendation etc.
For a programmer to build a career in artificial intelligence and machine learning, he/she should have a profound knowledge of mathematics, statistics, programming (especially python or R) and machine learning. The core of every machine learning algorithm is based upon statistics and solving optimization problems using calculus.
At first we perform some data processing and analysis on the raw training data and then fed it to the machine learning algorithm, the algorithm trains a model that learns from the given data and evolve over time when fed with new data. Once the model is trained we fed the model the test data and the model takes decision based on the patterns is has learned from the training data.
The way machine learning works is as follows:
- We find the appropriate data for a relevant task and then perform some data analysis and the clean and preprocess the data.
- We pick up the best machine learning algorithm based on our analysis of the data.
- We train the model on the training data and compute the model accuracy.
- Then we perform hyperparameter tuning on the model which assures more accuracy score.
- We productionize the model using any web framework.
Machine learning is having wide range of application across industries. Not only tech companies but machine learning is used extensively in finance, medicine and banking. The world needs more machine learning engineers and data scientists at this moment. So if you need help with a machine learning assignment to ensure you get excellent grades in your course and thus have a good career, then ask our programming experts. Most of our programming experts are machine learning engineers and data scientists from top companies, not only but they have also achived a good kaggle rank. They can easily solve your machine learning assignment problems and fetch good accuracy scores. This favour will be very helpful to you and you can easily fetch good grades by letting us solve your assignment problem. We also provide the best programming coursework help including assignments, homework and projects related to machine learning subjects.
Machine Learning Types
Machine learning is categorized into three different types. There include:
The majority of practical and real world machine learning uses supervised machine learning. In supervised learning we use both the input and the output variable of our data. Given the data, we can easily find out a function that knows the mapping between the input and output variable. Any supervised learning machine learning algorithm can discover the function, then we tune the hyperparameters to get the best possible results. This process is called supervised because all of the output variables of our training data supervise the learning process of the particular machine learning algorithm.
There are two types of problems that we can solve by using supervised learning technique.
- Classification problems.
- Regression problems.
In unsupervised learning we only get the input variable in our training data, we don’t get any output variable. The goal of unsupervised learning is to group together the same kind of points and here algorithms use various advance techniques to find the patterns between same kind of data points and group them together to form a cluster.
Unsupervised learning problems can be of two types:
Semi-Supervised Machine Learning
In Semi supervised machine learning we get training data where only some of the input labels have their corresponding output labels. These problems position themselves in between supervised learning and unsupervised learning. A large set of real world machine learning problems fall into this area where most of the output labels are missing. Here we use supervised learning techniques learn and discover patterns in the training data.
One idea is to use a supervised learning technique to classify the labels of the unlabeled output data for certain input data points.
Machine Learning Assignment Help
We have a team of statistics experts who can deliver superior quality assignments at affordable prices. Machine learning is a complicated subject which needs mastery on various topics including mathematics, statistics, programming, python etc. An expert should know how to gather, analyse, interpret and feed data to the machine. Designing a machine learning program is one of the toughest assignments for any programming student. Our statistics experts provide machine learning assignment help and homework help services to global students and ensure they get A+ grade.
The solutions offered by our Machine Learning project Help experts will be on step by step manner. If you are stressed and cannot take the pressure of researching, analyzing, and completing the assignment, you can take our experts’ help.
Machine Learning Assignment Help Topics
|Supervised learning||Linear models for regression and classification|
|Overfitting and regularization||Naïve bayes and logistic regression|
|Vector machines||Unsupervised learning|
|Expectation-maximization||Mixture of Gaussians|
|Deep learning||Neural networks for regression and classification|
|Instance-based learning||Decision tree induction|
|Maximum likelihood||Probabilistic inference|
|Artificial neural networks||Perceptron, back propagation|
|Clustering algorithms||Dimensionality reduction techniques|
|Kernel methods||Kernel PCA|
|Kernel Ridge Regression||Integral Probability Metrics|
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Essential Tools for Machine Learning
Anaconda is free and open source distribution of of the python programming language for data science and machine learning applications. Anaconda comes with all of the modern machine learning tools. It has the set of standard python libraries and bunch of other third party libraries like sklearn, SciPy, NumPy etc.
Our experts use anaconda tools extensively and we also advice our students to use it for most data science related work. Jupyter notebook is the most used IDE used in data science which comes pre-installed with Anaconda.
Essential Libraries for Machine Learning
NumPy is an open source numerical python library. With the help of NumPy we can easily various advanced mathematical operation on arrays. NumPy is written in C and it’s very fast when it comes to perform large computation. NumPy provides the best implementation of mathematical operation with very low time complexity.
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 most popular library that is used extensively in data visualization.
Matplotlib is the 2D plotting library in Python. To generate plots, histograms, bar charts and scatterplots, Matplotlib is used extensively . Not only we can plot various plots in matplotlib but we can also customize it according to our needs. We can easily plot various probabilistic plots like PDF, CDF using this library and make sense out of it.
Our experts use seaborn as well to visualize the data as seaborn is sometimes better.
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. Pandas is extensively in data science.
Scikit-learn is an open source project. It contains a number of machine learning algorithms. It is the most popular tool and prominent Python library for machine learning.
Except the above, we use NLTK for natural language processing, gensim for performing word2vec, seaborn for data visualization, pickle for saving a model etc. It’s kinda hard for someone to have knowledge of that many libraries and that comes with experience. Fortunately, most of our programming experts are working as a data scientists and they possess all the knowledge of these libraries. They can easily implement any complex task as they know what library is capable of doing what. Many students ask for machine learning assignment help which requires use of different libraries, our programming experts can easily help them out.
Machine Learning Algorithms
linear Regression is actually a regression technique. Given a dataset we predict the class labels in a classification problem while in regression we predict a real value as we don’t have class labels in a regression problem. Linear regression algorithm tries to find a line(in 2D)/ plane (in 3d) / hyperplane ( when the dimension is more than 3) that best fits the datapoints.
Assume, our training data is “D” consisting of “n” points and our dataset has “d” features. “D” consists of our X’s and Y’s and we have to predict Y where Y is a real number.
Suppose, we were given a dataset of height and weight where given a person’s weight we have to predict his/her height.
Here, our X is weight and Y is height. After we have our dataset, we can easily plot all of our datapoints in a 2d plane where x-axis represents the weight of the person and the y-axis represents the height of the person. The algorithm discovers a line that best fits all our datapoints and minimizes the squared loss. Where the squared loss is the square of the difference between actual Y and the predicted Y over all the datapoints, to be more precise we can easily minimize by GD or SGD algorithm.
Logistic regression is one of the most widely used supervised learning algorithm. Even though logistic regression has “regression” in it’s name but it’s actually a classification technique. Logistic regression can only solve two class classification problem, we can also use logistic regression in multiclass classification problem in “One vs rest” setting. Logistic regression assumes that the data is linearly separable and we can separate the datapoints by discovering a line(in 2D), plane (in 3D) and hyperplane (in more than 3 dimensional space). It is worth mentioning that the dataset must get column standarized before modelling.
Assume, our dataset is “D” consisting of “n” points and our dataset has “d” features. “D” consists of our X’s and Y’s and we have to predict Y where Y can only belong to a certain class.
The way logistic regression works is as follows:
1. At first it discovers the line (in 2D) The best plane maximizes the sum of signed distances such that one side of the line contains data corresponding to one particular class and on the other side of the line we have datapoints corresponding to another class.
2. But maximizing the sum of signed distances is not outlier prone, to avoid this problem we use the sigmoid function. There are many other function which we can choose but specifically we choose sigmoid function because it gives a helpful probabilistic interpretation.
3. Then we multiply the function by a “-” and take the log of it to turn it into a monotonic function. Earlier we had to maximize the the optimization problem but we we have to minimize the optimization problem as we have multiplied it with a “-“.
4. Then we just solve the optimization problem and the solution gives us the the ubit vector which is normal to the plane, once we get the unit vector we can easily find the equation of the line/hyperplane.
Logistic regression is useful when the dimension is very high and it’s not just a black-box model but it’s also interpretable.
Decision tree is a very useful supervised learning technique where we build a tree based model from the given Data. Decision tree works best when the dimensionality of the data is small. In most of the cases decision trees are used in classification problem, but there’s also a decision tree regressor available.
We can think of decision tree as a simple “if-else” statement model and the decision surface of decision tree is axis parallel lines/planes or hyperplanes.
Starting from the root of the tree, the data is split on the feature that gives the highest Information gain. We repeat the process and stop when we get a leaf node. As the depth if the tree increases, the model overfits. Decision tree models are interpretable when the dimensionality of the dataset and the depth of the tree is low.
Support Vector Machines or SVM:
Support vector machines is a very powerful supervised learning technique.Linear SVM assumes our datapoints is linearly separable and it tries to discover a line or hyperplane that separates our datapoints as widely as possible. In SVM we minimize the Hinge loss and SVM works very well even on those datasets where logistic regression fails as it uses the kernal function in the dual form formulation of SVM. In high dimension space SVM transforms the features using the Radical Basis Function or in short RBF kernal. If some certain datapoints is not separable in low dimension space SVM uses the kernal trick to make it separable in high dimension space.
In the softmax formulation of SVM in we just tries to maximize the distance between the line / hyperplane and the datapoints. But when we use kernal SVM we must have to choose the right kernal. And how we choose it depends upon the machine learning problem we are solving. Basically for most of the problem RBF kernal works like magic but there are some edge cases where it does not work perfectly, when the situation occures we use domain knowledge to design the right kernal.
In Linear SVM our decision surface is a line or hyperplane but in kernal SVM it is non-linear surfaces.
Naive Bayes (NB):
Naive Bayes is a classification algorithm based upon the fundamental of probability but specifically Naive Bayes uses “Bayes theorem” extensively. Naive bayes can easily solve multiclass classification problems and it assumes that every feature in our datapoints is independent and mutually exclusive. Naive bayes can not solve regression problems.
Assume, our dataset is “D” consisting of “n” points and our dataset has “d” features. “D” consists of our X’s and Y’s and we have to predict Y where Y can belong to multiple classes.
The way naive bayes works is as follows.
Given a datapoint, we want to find out the probabilities of the datapoint belonging to every possible class. Then we take the largest probability of the point belonging to a particular class. While computing the probability using bayes theorem we ignore the evidence and only care about the likelihood and prior of the bayes theorem. We ignore the evidence because the value of the evidence remains constant. Then the algorithm solves the numerator of the bayes theorem and by using the MAP rule or Maximum the posterior rule picks up the class that has the highest probability.
Naive Bayes uses Laplace smoothing to handle some edge cases.In real world, Naive bayes works very good on textdata. In spam classification naive Bayes can give nearly 98% accuracy.
KNN (k-Nearest Neighbors):
K-nearest neighbors is one of the most popular supervised learning algorithm. KNN can solve both classification and regression problem and it’s extremly powerful when we have very few features in our dataset.
Assume, our dataset is “D” consisting of “n” points and our dataset has “d” features. “D” consists of our X’s and Y’s and we have to predict Y where Y can only belong to a certain class or it can be a real value. For simplicity suppose we have only 2 features in our dataset.
The way KNN works is as follows.
Step 1: If we have 2 features, we will plot the points in a 2 dimensional space.
Step 2: Then given a query point, we basically look at the k’th nearest points and pick up the class label by performing majority vote in a classification setting. For regression we take all Y’s of K points and pick up the median or mean value of it.
But how do we find the right “K”? At first we set k = 1 and compute the accuracy score, then we keep on increasing the value of K. After a certain value of K, the accuracy score will not increase but decrease. Then we pick the K value where the accuracy is high. We always choose an odd value for “K” as it may happen that we have equal number of class labels if “K” is even. When “K” is equal to “n” then the model underfits and when “k” is 1 then ithe model overfits.
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 k number 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 is one of the most popular supervised learning algorithm and it uses decision trees as it’s base model. We can also extend the random forest algorithm and the extended version is called Random Forest Regressor which solves regression problems.
Assume that, our dataset is “D” consisting of “n” points and our dataset has “d” features. “D” consists of our X’s and Y’s and we have to predict Y where Y can belong to multiple classes or it can be a real value.
The way random forest works is as follows.
1. In Random Forest we perform sampling. From the dataset “D” we randomly sample rows and columns and build decision trees on top of it. We break up our dataset into small datasets of “m” columns and “c” columns. Where n>m and d>c.
- Then we build decision trees on top of each dataset, here we don’t want our decision trees to have a lot of levels instead we want them to be shallow.
3. Random forest uses aggregation and in classification setting we typically take the majority vote to predict the classlabel. For regression task we can use mean, median to determine the right output Y.
Random forest is the most popular bagging algorithm and it’s works really good on real world dataset. It does not work good when the dimension of our dataset is high. When the dimension is high we typically choose Logistic regression or SVM.
In Exploratory data analysis we try to find out the relationship between the features of the data and we do it by plotting the data in 2D, 3D and by doing pair plots or even looking at the distribuition of the features of the dataset. But, What if our dataset has 784 dimensions? How do we visualize in that case? Dimensionality reduction helps us extensively in that case and there are two major dimensionality reduction technique,
1. PCA or Principle Component Analysis
2. T-SNE or T distributed stochastic neighbourhood embedding.
In PCA we at first perform column standardization and then we try to discover the best unit vector that minimizes the squared of the distance of all the points from that unit vector. The direction of the unit vector is the direction where the maximum variance is available and the unit vector is actually the eigen vector that corrsponds to the maximum eigen value. We use two eigen value and their corresponding eigen vectors when we want to transform data from higher dimension to 2D space. That is basically how PCA helps us to visualize high dimensional data and typically PCA works good but not best!
T-SNE stands for T-distributed Stochastic Neighbor Embedding. The neighborhood of a point is basically those points who are close to that particular point and embedding means we take points from high dimensional space and place them in low dimensional space while preserving the neighborhoods. T-SNE is an iteratrive algorithm and and it gives us two hyperparameters that we can tune, they are-
The preferred perplexity value is 5-50 and the step is basically the number of iteration, as the step increases the clusters go away from each other and after a certain number of iterations it does not move much.
The method of selecting the most important features for a classification or regression task is called Feature Selection. As the dimensionality increases the performance of our model decreases due to the “curse of dimensionality”. So, it will be very useful if somehow we can reduce the dimensionality and use the most important features only. But, How do we select the most important features?
We do it using a idea called Forward Feature Selection.
Step 1: We train a model for every single feature and test it’s accuracy on test data. We pick up the feature(suppose f1) which gives us the best accuracy score.
Step 2: We again train bunch of models by using f1 and every single feature except f1. Thenwe compute it’s accuracy on test data and pick up the best two features which gives best accuracy.
Step 3: We keep repeating the process and at some certain point we will have the number of
important features we want.
Feature Selection is independent of what model we choose to solve our problem. But one disadvantage of Feature selection is that the time complexity is pretty high.
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)
Most of the students order us to develop the python machine learning assignment solution using Jupyter Notebook as IDE and as per their needs we choose the best tools that fit the certain problem. Typically for data analysis and pre-processing we pick up NLTK, Sk-learn, matplotlib, re and Seaborn.
We provide fully commented solutions with visualization and the code we write is very readable. Completing your assignment is not our main purpose, we also make sure that you understand the solution. Our experts use advanced libraries to solve problems where the code has very low time complexity.
When it comes to solving assignment problems, our experts make sure that the model get the best accuracy, recall and precision scores. The more the score, the better the model. We care about each and every thing that can be helpful to you.
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