CloudyML Job Hunting Course

Level Up Your Job Cracking probabilities With The Best Job Hunting Course..

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05th Feb 2021

10.30 A.M

Akash Raj

CloudyML Job Hunting Course

Level Up Your Job Cracking probabilities With The Best Job Hunting Course..

CloudyML Job Hunting Course

Get experts’ tips, tricks and techniques on how to success in career.

Discover the Secret to Landing Your Dream Job in

Data Science

Become Job-Ready Data Scientist from Scratch in just 120 hours.

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Get Real World Industrial Experience with Complete hands-on practical Learning.

🌟 More than 100+ Learners have Already enrolled in this course just in last 3 days.

Course Curriculum

Neural Network Module

In this assignment, we have covered, use cases of tensorflow in real world, tensors definition, tensor types, tensor formats, tensorflow methods, mathematical operations in tensorflow, numpy compatibility, gradient and finally a small end to end linear classifier using the concepts of tensorflow we learnt from the beginning.

In this this assignments we have mentioned every fundamentals of Pytorch which one should know while working on this library.

In this assignment we have mentioned all the concepts that one should know before getting into neural networks. Each and every concept that is required for working on neural nets has been mentioned in this assignment.

In this assignment, we have added more examples related to neural networks.

This assignment will teach you to code neural network completely from scratch. After this assignment one will be very much comfortable on creating any kind of neural nets from scratch and that's what the main agenda of this course.

Computer Vision Module

The main purpose of this assignment is to get a complete idea of Alexnet and code it completely from Scratch.

The main purpose of this assignment is to get a complete idea of VGG-16 and code it completely from Scratch.

An image classification project where image augmentation and pretrained net is used.

An intermediate project to detect facial emotion using openCV.

NLP MOdule

In this assignment, you will learn about NLP, top 8 use cases in industry, concepts of tokenization, punkt, lemmatization, stemming , stemming vs lemmatization and stop words removal. All these concepts will help you in dealing with sentences and paragraphs later.

This assignment covers concepts of  countvectorizer, bag of words, TD-IDF, N-Grams, POS tagging which will teach you how you can convert words into vectors. It will help you dealing with text data. You will find all the necessary resources to do the assignment inside the file.

We’ll build a Text Classification model using a dataset of News headlines,
where the headlines have been categorized as sarcastic or not. We’ll train a classifier on this
and it can then tell us that if a new piece of text looks like it might be sarcastic or not.

Named-Entity recognition (NER) is a process to extract information from an Unstructured Text.
Its also known as Entity Extraction. The project uses techniques in Machine Learning and Natural Language Processing to automate the process.
One of the techniques used for document analysis is called natural language processing.
Thus, we'll be using one of the advanced NLP technique known as Entity Extraction/Named Entity Recognition to train and build model which
extracts useful information quickly without manually going through it.

An LSTM Network has its origin in a RNN. But it can solve the memory loss by changing the neuron architecture. In this
assignment you are going to work on air index quality dataset and will perform LSTM on the same.

Time Series Module

In this assignment you are going to work on stock prices dataset. The topics covered in this assignment are sequences and LSTM which have been explained very nicely.

In this project the main task is to predict the values of power
consumption and here we have used time series of data given for 2 million minutes of a Household.
As part of this case study, your main goal will be to predict the Global active Power

Challenging Projects to Enrich Your Resume

In this project, the task is to read the number on the Credit Card using Computer Vision. OCR basically stands for Optical Character Recognition ,it is used in industries such as Communication, Banking, Insurance, Legal, Healthcare, Tourism, Retail. The OCR technology can digitize data from reports containing X-rays, patient’s history, treatments or diagnostics, tests, and overall hospital records.

In this project, the task is to create an Automated Attendance system using computer vision, where the attendance will be automatically marked based on detection of the faces.
Attendance system using face recognition is a procedure of recognizing students by using face biostatistics based on the high definition monitoring and other computer technologies to reduce human effort and human error as well.

in this project we are going to do semantic segmentation using CamVid dataset, CamVid basically stands for Cambridge-driving Labeled Video database which is the first collection of videos with object class semantic labels, complete with metadata.
Semantic segmentation network classifies every pixel in an image, resulting in an image that is segmented by class. Applications for semantic segmentation include road segmentation for autonomous driving and cancer cell segmentation for medical diagnosis

In this project, the task is to detect fracture in knee, based on MRI images.  Mostly MRI images are of different modalities from which mostly we use single modality at a time, so in this project we will combine all the modalities of the MRI image and train different models for different modalities and combine the predictions from them to get better analysis of the data.

In this project the task is to detect landmarks. It can be used to recognize well known landmarks from the images posted on social media. This project uses the second version of the Google Landmarks dataset (GLDv2), which contains approximately 5 million images annotated with labels representing human-made and natural landmarks. The dataset can also be used for retrieval experiments.

After centuries of intense whaling, recovering whale populations still have a hard time adapting to warming oceans and struggle to compete everyday with the the industrial fishing industry for food.
So in order to aid whale conservation efforts, scientists use photo surveillance systems to monitor ocean activity. They basically use the shape of whale’s tails and unique marking found in the footage to basically identify what species of whale they are analyzing and minutely log whale pod dynamics and movements.
For the past 40 years, most of this work has been done manually by individual scientist, leaving a huge heap of data untapped and underutilized. So in this project the main task is to basically build an algorithm to identify individual whales in the images. We will be basically analyzing HappyWhale’database of around 25k images.

Just a few years ago, the idea to build an object detection model to detect amenities in a digital picture might sound prohibitively difficult and intimidating. Nowadays, a great number of decent solutions have already emerged.
So in this project our aim is to detect amenities in the household using Airbnb dataset which is also a real life example of amenity detection is Airbnb (billion dollar hospitality venture). The goal of this is to understand whether the detected amenities provide convenience for guests and can assist the customer in decision making. Because a family trip might require a room with spacious kitchen when compared to a bachelor’s trip.

Sound Classification is one of the most widely used applications in Audio Deep Learning. It involves learning to classify sounds and to predict the category of that sound. This type of problem can be applied to many practical scenarios e.g. classifying music clips to identify the genre of the music, or classifying short utterances by a set of speakers to identify the speaker based on the voice.
So in this project we will use the audio dataset and perform some transformation which will then, suit the computer vision applications. This project basically is to notify that CNN are not just for images application.

HAR using smartphone sensors like accelerometer is one of the hectic topics of research. HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. In the same sequence, we can use LSTM (long short term memory) model of the Recurrent Neural Network (RNN) to recognize various activities of humans like standing, climbing upstairs and downstairs etc.

This project aims to develop a basic text summarizer that summarizes text based on frequency of words in the sentences. It utilizes techniques like tokenization and stopwords removal to get the word frequencies.

This project aims to train a model using transformer architecture which can translate Portuguese sentences into English. The Portuguese-English translation dataset will be used.

This project aims to perform sentiment analysis on a dataset of plain-text IMDB movie reviews. In addition to training a model, you will learn how to pre-process text into an appropriate format.

This project aims to build and train a bidirectional LSTM neural network model to recognize named entities in text data. Named entity recognition models can be used to identify mentions of people, locations, organizations, etc.

Challenging Capstone Projects
That Will Enrich Your Resume

1

Employee Salary Prediction

This is the first capstone project in which we are going to implement our salary prediction using the machine learning algorithm.
This model predicts the salary of the employee based on the year of experience of employee.

2

Seoul bike trip prediction

This is capstone project in which we need to predict the trip duration from all the other data from dataset like distance, location and weather information. Trip duration is the most fundamental measure in all modes of transportation. Hence, it is crucial to predict the trip-time precisely for the advancement of Intelligent Transport Systems (ITS) and traveler information systems.

3

Shipping cost prediction

This is the capstone project in which the task is to predict the cost of shipping paintings, antiques, sculptures, and other
collectibles to costumers based on the information provided in the dataset. The dataset consists of parameters such as the
artist’s name and reputation, dimensions, material, and price of the collectible, shipping details such as the customer information,
scheduled dispatch, delivery dates, and so on.

4

Merchandise Popularity prediction

Big Brands spend a significant amount on popularizing a product. Nevertheless, their efforts go in vain while establishing the merchandise in the hyperlocal market. Based on different geographical conditions same attributes can communicate a piece of much different information about the customer. Hence, insights this is a must for any brand owner.
So here in this project we are going to predict the popularity from all the other data from dataset like Store_Ratio, Basket Ratio, Store Score.

5

ODI Match winner Prediction

Cricket is one of the most popular sports in world, especially in India. The game is highly uncertain.
It is the sport which generate high revenue, so what if the winner team of the match can be predicted before the match, even have begin? In this assignment we are going to predict the future ODI cricket match winner based on previous year’s match result,

6

Video Games Recommender System

In this capstone project we are going to recommend the similar games to the user based on their behavior. This dataset is a list of user behaviors, with columns: user-id, game title, behavior name, value.

7

Sentiment Analysis on Financial news

The sentiment of financial news articles reflects and directs the performance of the U.S. stock market. By performing sentiment analysis on the news headlines, we get the label of positive or negative with their confidence scores. By using this output we can correlate to the stock market’s gains/losses on that particular day.

8

Web Scraping of Covid 19India website

In this capstone project we are going to scrape data from this covid 19 India website and using the data to develop our own dashboard which will update automatically by continuously fetching data after some fixed interval.
9

Ethereum Fraud Detection

In this capstone project the task is to predict whether the transaction is fraudulent or non – fraudulent using the transaction data. This dataset contains rows of known fraud and valid transactions made over Ethereum, a type of cryptocurrency. It is an imbalanced dataset project.

**We Provide Job Referrals

After Course Completion Based On Your Skills , we will refer you to companies like:

  • Fractal Analytics,
  • Tiger Analytics,
  • Tredence,
  • Analytics  etc.

Take a glance at what our course is delivered

After enrolling you would get access to our learning portal.
There we have uploaded videos, guided assignments and capstone projects. You will find the Skype link in the portal itself. You can ask your queries via live chat from 3pm to 12 midnight everyday.

★★ We also have given Topic wise interview QnA and Sample resumes.

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Bonuses You will get ONLY IF you Enroll before 6th Jan

Take a glance at what our course delivered

After enrolling you would get access to our learning portal. There we have uploaded videos, guided assignments and capstone projects. You will find the Skype link in the portal itself. You can ask your queries via live chat from 3pm to 12 midnight everyday.

★★ We also have given Topic wise interview QnA and Sample resumes.

Document

Take a glance at what our course is delivered

After enrolling you would get access to our learning portal.
There we have uploaded videos, guided assignments and capstone projects. You will find the Skype link in the portal itself. You can ask your queries via live chat from 3pm to 12 midnight everyday.

★★ We also have given Topic wise interview QnA and Sample resumes.

 

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