
This video provides a brief introduction at a high level view of the entire course.
Explore the architecture of deploying a machine learning model in production, including data cleansing, model building, scheduler-driven predictions, and database workflows, with Tableau visualizations for customer insights.
Master the seven steps of the machine learning lifecycle, from data gathering and processing to feature engineering, algorithm selection, training, evaluation, deployment, and ongoing maintenance.
The code source present in kaggle.com is provided as resource link for you to copy/ fork, edit and run the code on kaggle cloud. Please register on kaggle with google account and use the cloud for academic purposes.
Attached Kaggle Code Link as resource.
Navigate to the link and copy/ edit the kaggle notebook and work on it.
Explore how the support vector classifier finds the best separating line between red and blue classes by maximizing the margin, using the nearest data points to determine distance.
Explore how support vector machine regression builds a linear model to predict sales from promotion data, using a decision boundary to find the best regression line.
Identify the best cluster formation using the elbow plot for k-means clustering. Plot cost against k to locate the elbow and determine the optimum number of clusters.
The code source present in kaggle.com is provided as resource link for you to copy/ fork, edit and run the code on kaggle cloud. Please register on kaggle with google account and use the cloud for academic purposes.
Explore how convolutional neural networks power image recognition in computer vision by transforming rgb image data through convolution, pooling, and ReLU activations to classify objects.
Build a multiclass Keras image recognition model for apple, banana, and dates detection using transfer learning with MobileNet, image augmentation, and a three-class output.
Explore how deep learning autoencoder models enable dimension reduction with exact reconstruction of data patterns, including applications to audio input.
The code files in kaggle can be accessed by resource link shared with this lecture
Explore the siamese network architecture with two identical subnetworks that compute a distance between inputs, and use distance-based loss to learn similarities and dissimilarities.
The code source present in kaggle.com is provided as resource link for you to copy/ fork, edit and run the code on kaggle cloud. Please register on kaggle with google account and use the cloud for academic purposes.
Explore back propagation and the chain rule to compute how small changes in weights affect the loss, derive derivatives through a neural network, and apply gradient descent to reduce error.
Explore Flask as a web framework that bridges client and server, enabling asynchronous Ajax requests to endpoints, JSON responses, and integration with databases, models, and file systems.
Build a Flask api that runs on localhost, handles get and post requests, defines endpoints, and returns an age calculation from submitted data.
The course ensures to test the true potential of the candidate on both conceptual and practical knowledge of Natural Language Processing.
A good attempt in the Test would ensure the candidate has the potential to understand the Machine Learning & Statistics concepts in depth which would be required for on job project execution and delivery.
To measure your efficiency correctly and boost confidence, ensure sincerity during the exam and do not resort to copy.
Explanations are provided in end of the exams for multi-choice. Assessment need to be submitted and score criteria need to be met to ensure successful execution . You can learn them and use internet to dive further in case required on the points.
Are you planning to get Interviewed for Data Science Role?
The exam or mock interview test can help determine your strengths and weakness before interview.
As per our Study, Successful Completion of this Exam would increase the Job Interview Success by 80% as majority of questions seems to be repeated by the candidates.
Practice on Real Interview Questionnaire summarized across 150+ Machine Learning Interviews.
The interviews were conducted for Multinational Firms and Research Centers across the Globe.
How to Prepare for a Data Science Interview:
Read the Job Description for the Particular Position You are Interviewing for.
Review your Resume before each Stage of the Interviewing Process.
Ask the Recruiter about the Structure of the Interview.
Do Mock Interviews.
To become a data scientist, you must have a strong understanding of mathematics, statistical reasoning, computer science and information science. You must understand statistical concepts, how to use key statistical formulas, and how to interpret and communicate statistical results.
This Data Science Test assesses a candidate's ability to analyze data, extract information, suggest conclusions, and support decision-making, as well as their ability to take advantage of Python and its data science libraries such as NumPy, Pandas, or SciPy. It's the ideal test for pre-employment screening.
Strength of Data Scientist:
A passion for solving problems. A data scientist needs to go beyond identifying and analyzing a problem – he or she needs to solve it.
Statistical thinking. Data scientists are professionals who turn data into information, so statistical know-how is at the forefront of our toolkit. Knowing your algorithms and how and when to apply them is arguably the central task to a data scientist's work.