You are reading the article Top Machine Learning Jobs To Apply For In December 2023 updated in December 2023 on the website Daihoichemgio.com. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested January 2024 Top Machine Learning Jobs To Apply For In December 2023Analytics Insight announces the top machine learning jobs to apply for in December 2023
The emergence of major disruptive technologies like artificial intelligence, big data, computer vision, and many more have introduced some lucrative machine learning jobs for aspiring machine learning engineers, machine learning specialists, and more. Almost every industry today relies on technology to operate and thrive, which is why artificial intelligence (AI) and machine learning (ML) is becoming more integral to helping businesses make smarter and faster decisions and products. Many companies have started posting machine learning jobs in December to apply as soon as possible. There is tough competition in the recruitment process due to its high demand. Thus, let’s go through some of the top machine learning jobs to apply for in December 2023 with Analytics Insight.Machine Learning Specialist – Samsung Electronics
Location: Noida, Uttar Pradesh
Responsibilities:Machine Learning Lead – Skit
Location: Bangalore Urban, Karnataka
You will be working with Product Managers and ML Engineers to design and deliver ML models and capabilities for our voice-bot offering, VIVA. A regular roster for the role looks like the following:
Formulate, design, and oversee mMachine lLearning capabilities of our speech-tech stack.
Lead a team of ML Engineers in the process of development.
Organize regular research and architecture reviews and discussions.
Mentor other machine lLearning team members.Manager-Product Development (Machine Learning) – American Express
Location: Gurgaon, Haryana
The individual in this role will be responsible for overlaying sales and marketing strategy needs with analytics and machine learning workstreams and leveraging technology expertise to architect and develop enterprise big data platforms and applications. This individual will work closely with partners across teams like commercial sales and marketing, analytics and machine learning, American Express Technology, external vendors, and others. It is one of the best machine learning jobs to apply for in December 2023.Machine Learning Engineer – Wipro
Location: India (Remote)
Responsibilities:Machine Learning Engineer – Adobe
Location: Bengaluru, Karnataka
The position involves working closely with Product Management for the design, development, debugging, effort estimation, and maintenance of Statistical and Machine Learning models that power various features in AdCloud. Work closely with AdCloud engineering team in building cCloud nNative pipelines taking the sStatistical and ML models through the entire lifecycle improving on usability, explain-ability, and performance.Machine Learning Engineer II – Amazon
Location: Bengaluru, Karnataka
As a Machine Learning Engineer, you will help solve a variety of technical challenges and mentor other engineers. You will play an active role in translating business and functional requirements into concrete deliverables and build quick prototypes or proofs of concept in partnership with other technology leaders within the team. You will help invent new features, develop and deploy highly scalable and reliable distributed services. You will work with a variety of core languages and technologies including, Linux, and AWS technologies. It is one of the best machine learning jobs to apply for in December 2023.Machine Learning Engineer – Cleareye.ai
Location: India (Remote)
Work on end-to-end aspects of machine learning solutions for the financial domain: acquiring data, training and building models, deploying models, building API services for exposing these models, maintaining them in production.AI & ML Lead Software Engineer – Parallel Wireless
Location: Bengaluru, Karnataka
You're reading Top Machine Learning Jobs To Apply For In December 2023
Learn about the unsupervised Machine Learning models that are in the topmost position in 2023
Unsupervized Machine Learning models are not supervised using training datasets when using the machine learning technique this is called unsupervised learning. Instead, models themselves decipher the provided data to reveal hidden patterns and insights. It is comparable to the learning process that occurs in the human brain when learning something new.
It primarily deals with unlabelled data. It can be compared to learning, which happens when a learner resolves a problem without the guidance of a teacher. Unsupervised learning cannot be used to solve a regression or classification issue directly. We lack the input data with the corresponding output label, much like supervised machine learning. It aims to identify the underlying pattern of the dataset, group the data based on similarities, and express the dataset in a precise manner.
To understand more about it. Let us know the top 10 Unsupervized Machine Learning Models/algorithms,
Gaussian mixture models – It is a probabilistic model that assumes that all of the data points were produced by combining a limited number of Gaussian distributions with unknowable parameters.
Frequent pattern growth – Models use algorithms that allow the detection of recurring patterns without candidate production. Instead of employing Apriori’s generate and test strategy, it constructs an FP Tree.
K-means Clustering – This Unsupervised learning is used in the K-Means Clustering technique. It clusters the unlabelled dataset into several groups. The program repeatedly divides the unlabelled dataset into K clusters. Each dataset only belongs to one group that shares common characteristics. It enables us to group the data into different categories. It is a useful technique for finding the groups’ categories in the provided dataset without training.
Hierarchical Clustering – Hierarchical cluster analysis is another name for hierarchical clustering. It is an algorithm for unsupervised clustering. It entails creating clusters that are arranged initially from top to bottom.
Anomaly Detection – Anomaly detection is most helpful in training scenarios where we have a variety of regular data instances. By allowing the machine to get close to the underlying population, a clear model of normality is produced.
Principal Component Analysis – By utilizing orthogonal transformation, a statistical method converts the observations of correlated characteristics into a group of linearly uncorrelated components. The Principal Components are these newly altered features that make it one of the most widely used machine learning algorithms.
Apriori Algorithm – It utilizes databases that store transactional data. The association rule establishes the strength of the relationship between two objects. The associations for the itemset are chosen using a breadth-first search in this approach. It assists in identifying common item sets in a huge dataset.
KNN (k-nearest neighbors) – A new data point is classified using the K-NN algorithm based on similarity after all the existing data has been stored. This indicates that new data can be easily viewed when it appears.
Neural Networks – Since a neural network approximates any function, it is theoretically conceivable to use one to learn any function.
Independent Component Analysis – This technique works by assuming non-Gaussian signal distribution and enables the separation of a mixture of signals into their various sources.
Conclusion –The biggest drawback of unsupervised learning is that you cannot get precise information regarding data sorting. However, this learning helps you find all kinds of unknown patterns in data. Algorithms used models are important to learn as they are unsupervised and needed to be understood
With source program in Python, check these top 10 Machine Learning with Python projects for freshers
Machine learning is same as how it sounds. It is the idea that multiple types of technology, such as computers and tablets, can learn something from programming and other data. It appears to be an abstract idea. However, this type of technology is used by several people each day. Speech identification is a good example of this. Virtual assistants including Siri and Alexa use technology to present messages, answer questions and respond to instructions.
In this tutorial, you will find top 10 machine learning project ideas for freshers, intermediates, and professionals to gain real-world experience of this developing technology in 2023. These machine learning project ideas will assist you in learning all the practicalities that you want to with prevailing in your profession and to make you employable in the business.1.Movie Recommendations from Movielens Dataset
Many individuals currently use technology to stream TV and film shows. Although choosing the next stream to watch can be complex and time-consuming, recommendations are generally built based on customer habits and history. This is accomplished by machine learning and is a great and simple task for beginners to tackle. Starting developers can learn by writing program utilizing one of the two languages, Python and R, and using data from Movielens Dataset. Movielens has over 6000 people make it currently involves more than 1 million film valuations of 3900 movies.2.Music Recommendation System ML Project
This is one of the most popular machine learning projects and can be used across multiple domains. You should be very familiar with a recommendation system if you have utilized any E-commerce site or Movie/Music website. In some E-commerce sites such as Amazon, at the time of checkout, the system will recommend elements that can be added to the cart.3.BigMart Sales Prediction ML Project
As a fresher, you should work on multiple machine learning projects ideas to expand your skillset. Therefore, we have added a project that will learn unsupervised machine learning algorithms to us by utilizing the business dataset of a grocery supermarket store.4.TensorFlow
This open-source artificial intelligence library is a best place for fresher to enhance their machine learning skills. With TensorFlow, they can use the library to make data flow graphs, projects utilizing Java, and an array of applications. It also involves APIs for Java.5.Iris Classification
This is one of the simplest machine learning projects with Iris Flowers being the elementary machine learning datasets in classification writing. This machine learning problem is defined as the “Hello World” of machine learning. The dataset has numeric characteristics and ML freshers need to figure out how to load and handle information. The iris dataset is small which simply fits into the memory and does not need any specific transformations or scaling, to start with.6.Sales Forecasting with Walmart
While predicting future sales efficiently may not be applicable, businesses can come near to machine learning. For example, Walmart supports datasets for 98 products across 45 outlets so programmer can access data on weekly sales by locations and branch. The main objective of this project is to create better data-driven decisions in channel optimization and stock planning.7.Stock Price Predictions
It is same as sales forecasting, forecasts of prices for stocks can be changed from the data of previous prices, indexes of volatility, and different fundamental indicators. For freshers, it is possible to start with a concept like this and create use of stock industry data to create predictions over the recent months. It is a best way to get familiar with making predictions utilizing huge data sets.8.Breast Cancer Prediction
This project uses machine learning to make data that helps decide whether the tumour in the breast is mild or deadly. There are multiple factors considered, including the thickness of the lump, the number of bare nuclei, and mitosis. It is also a best method for a new expert in machine learning to get familiar with using R.9.Sorting of Specific Tweets on Twitter
In an optimal world, quickly filtering tweets with definite words and elements would be best. There’s a huge fresher-level machine-learning project which enables programmers to develop an algorithm that takes scraped tweets processed by an artificial language processor to recognize which tweets are more likely to be associated to specific topics or talk about specific individuals, etc.10.Making Handwritten Documents Digital Versions
Continuing our theme of collecting and sharing the top machine learning GitHub repositories every month, the February edition is fresh off the shelves ready for you!
GitHub repositories are one of the easiest and best things for all the people working in data science to keep ourselves updated with the latest developments and projects. It’s also an awesome collaboration tool where we can connect with other like minded data scientists on various projects.
Without any further ado, let’s dive into this month’s list.
This is part of a series from Analytics Vidhya that will run every month. You can check out the top 5 repositories that we picked out in January here.
FastPhotoStyle is a python library developed by NVIDIA. The model takes a content photo and a style photo as inputs. It then transfers the style of the style photo to the content photo.
The developers have cited two examples to show how the algorithm works. The first is a very simple iteration – you download a content and a style image, re-size them, and then simply run the photorealistic image stylization code. In the second example, semantic label maps are used to create the stylized image.
You can read more about this library on Analytics Vidhya’s blog here.
If you’ve ever scraped tweets from Twitter, you have experience working with it’s API. It has it’s limitations and is not easy to work with. This python library was created with that in mind – it has no API rate limits (does not require authentication), no limitations, and is ultra quick. You can use this library to scrape the tweets of any user trivially
The developer has mentioned that it can be used for making Markov Chains. Do note that it works only with python version 3.6+.
This is an implementation of the handwriting synthesis experiments presented in the ‘Generating Sequences with Recurrent Neural Networks’ paper by Alex Graves. As the name of the repository suggests, you can generate different styles of handwriting. The model is based on priming and biasing. Priming controls the style of the samples and biasing controls the neatness of the samples.
The samples presented by the author on the GitHub page are truly fascinating in their diversity. He is looking for contributors to enhance the repository so if you’re interested, get in touch with him!
This is a PyTorch implementation of “Efficient Neural Architecture Search (ENAS) via Parameters Sharing”. What do ENAS do? They reduce the computational requirement, that is, the GPU Hours of the Neural Architecture Search by an incredible 1000 times. They do this via parameter sharing between models that are subgraphs within a large computational graph.
The process of how to use it have been neatly explained on the GitHub page. The prerequisites for implementing this library are:
tqdm, imageio, graphviz, tqdm, tensorboardX
This is a relatively straightforward, yet utterly fascinating, use of machine learning. Using a convolutional neural network in python, the developer has built a model that can recognize the hand gestures and convert it into text on the machine.
The author of this repository built the CNN model using both TensorFlow and Keras. He has specified, in detail, how he went about creating this project and each step he followed. It’s definitely worth checking out and trying once on your own machine.
Fraud Detection with Machine Learning is possible because of the ability of the models to learn from past fraud data to recognize patterns and predict the legitimacy of future transactions. In most cases, it’s more effective than humans due to the speed and efficiency of information processing. Some types of internet frauds are: 1. ID forgery. Nowadays IDs are fabricated so well that it’s almost impossible for humans to verify their legitimacy and prevent any identity fraud. Through the use of AI, various features of the ID card appearance can be analysed to give a result on the authenticity of the document. This allows companies to establish their own criteria for security when requests are made which require certain ID documents. 2. Bank loan scams. These may happen if a person contacts you and offers a loan scheme with suspiciously favourable conditions. Here the person contacting you will ask for your bank details or for payment upfront, without having any proper company information or even using an international contact number. Such frauds can easily be handled by AI using previous loan application records to filter out loan defaulters. 4. Credit card frauds. This is the most common type of payment fraud. This is because all details are stored online which makes it easier for criminals and hackers to access. Cards sent through mail can also be easily intercepted. One way to filter such fraud transactions using machine learning is discussed below. 5. Identity theft. Machine Learning for detecting identity theft helps checking valuable identity documents such as passports, PAN cards, or driver’s licenses in real-time. Moreover, biometric information can be sometimes required to improve security even more. These security methods need in-person authentication which decreases the chance of frauds to a great extent.Model to predict fraud using credit card data:
Here a very famous Kaggle dataset is used to demonstrate how fraud detection works using a simple neural network model. Imports:
import pandas as pd import numpy as np import tensorflow as tf import keras from sklearn.preprocessing import StandardScaler from keras.models import Sequential from keras.layers import Dense from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report
Have a look at the dataset
data= pd.read_csv(‘creditcard.csv’) data[‘Amount_norm’] = StandardScaler().fit_transform(data[‘Amount’].values.reshape(-1,1)) data= data.drop([‘Amount’],axis=1) data= data.drop([‘Time’],axis=1) data= data[:-1] data.info()
Now after some data cleaning, our dataset contains a total of 28 features and one target, all having float values which are not empty. Our target is the Class column which determines whether the particular credit card transaction is fraud or not. So the dataset is divided accordingly into train and test, keeping the usual 80:20 split ratio. (random_state is fixed to help you reproduce your split data)
X = data.iloc[:, data.columns != ‘Class’] y = data.iloc[:, data.columns == ‘Class’]
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.2, random_state=0)
We use the sequential model from keras library to build a neural network with 3 dense layers. The output layer contains only a single neuron which will use the sigmoid function to result in either a positive class or a negative class. The model is then compiled with adam optimizer, though it is highly suggested that you try out different values of hyper parameters by yourself, such as the number of units in each layer, activation, optimizer, etc. to see what works best for a given dataset.
model= Sequential() model.add(Dense(units= 16 , activation = ‘relu’, input_dim = 29)) model.add(Dense(units= 16, activation = ‘relu’)) model.add(Dense(units= 1, activation = ‘sigmoid’))
model.fit(X_train, y_train, batch_size = 32, epochs = 15)
This is the result after running the model for a few epochs. We see that the model gives 99.97% accuracy very fast. Below, y_pred contains the predictions made by our model on the test data, and a neat summary of its performance is shown.
y_pred = model.predict(X_test) print(classification_report(y_test, y_pred))Conclusion
So this way we were successfully able to build a highly accurate model to determine fraudulent transactions. These come in very handy for risk management purposes.Author Bio:
In this article, we will discuss the likelihood function, the core idea behind that, and how it works with code examples. This will help one to understand the concept better and apply the same when needed.
Let us dive into the likelihood first to understand the maximum likelihood estimation.What is the Likelihood?
In machine learning, the likelihood is a measure of the data observations up to which it can tell us the results or the target variables value for particular data points. In simple words, as the name suggests, the likelihood is a function that tells us how likely the specific data point suits the existing data distribution.
For example. Suppose there are two data points in the dataset. The likelihood of the first data point is greater than the second. In that case, it is assumed that the first data point provides accurate information to the final model, hence being likable for the model being informative and precise.
After this discussion, a gentle question may appear in your mind, If the working of the likelihood function is the same as the probability function, then what is the difference?Difference Between Probability and Likelihood
Although the working and intuition of both probability and likelihood appear to be the same, there is a slight difference, here the possibility is a function that defines or tells us how accurate the particular data point is valuable and contributes to the final algorithm in data distribution and how likely is to the machine learning algorithm.
Whereas probability, in simple words is a term that describes the chance of some event or thing happening concerning other circumstances or conditions, mostly known as conditional probability.
Also, the sum of all the probabilities associated with a particular problem is one and can not exceed it, whereas the likelihood can be greater than one.What is Maximum Likelihood Estimation?
After discussing the intuition of the likelihood function, it is clear to us that a higher likelihood is desired for every model to get an accurate model and has accurate results. So here, the term maximum likelihood represents that we are maximizing the likelihood function, called the Maximization of the Likelihood Function.
Let us try to understand the same with an example.
Let us suppose that we have a classification dataset in which the independent column is the marks of the students that they achieved in the particular exam, and the target or dependent column is categorical, which has yes and No attributes representing if students are placed on the campus placements or not.
Noe here, if we try to solve the same problem with the help of maximum likelihood estimation, the function will first calculate the probability of every data point according to every suitable condition for the target variable. In the next step, the function will plot all the data points in the two-dimensional plots and try to find the line that best fits the dataset to divide it into two parts. Here the best-fit line will be achieved after some epochs, and once achieved, the line is used to classify the data point by simply plotting it to the graph.Maximum Likelihood: The Base
The maximum likelihood estimation is a base of some machine learning and deep learning approaches used for classification problems. One example is logistic regression, where the algorithm is used to classify the data point using the best-fit line on the graph. The same approach is known as the perceptron trick regarding deep learning algorithms.
As shown in the above image, all the data observations are plotted in a two-dimensional diagram where the X-axis represents the independent column or the training data, and the y-axis represents the target variable. The line is drawn to separate both data observations, positives and negatives. According to the algorithm, the observations that fall above the line are considered positive, and data points below the line are regarded as negative data points.Maximum Likelihood Estimation: Code Example
We can quickly implement the maximum likelihood estimation technique using logistic regression on any classification dataset. Let us try to implement the same.
The above code will fit the logistic regression for the given dataset and generate the line plot for the data representing the distribution of the data and the best fit according to the algorithm.Key Takeaways
Maximum Likelihood is a function that describes the data points and their likeliness to the model for best fitting.
Maximum likelihood is different from the probabilistic methods, where probabilistic methods work on the principle of calculation probabilities. In contrast, the likelihood method tries o maximize the likelihood of data observations according to the data distribution.
Maximum likelihood is an approach used for solving the problems like density distribution and is a base for some algorithms like logistic regression.
The approach is very similar and is predominantly known as the perceptron trick in terms of deep learning methods.Conclusion
In this article, we discussed the likelihood function, maximum likelihood estimation, its core intuition, and working mechanism with practical examples associated with some key takeaways. This will help one understand the maximum likelihood better and more deeply and help answer interview questions related to the same very efficiently.
Update the detailed information about Top Machine Learning Jobs To Apply For In December 2023 on the Daihoichemgio.com website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!