Solved end-to-end Data Science Projects in Entertainment & Media

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Data Science Projects in Entertainment & Media

Learn to Build a Polynomial Regression Model from Scratch

In this Machine Learning Regression project, you will learn to build a polynomial regression model to predict points scored by the sports team.

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Build Regression (Linear,Ridge,Lasso) Models in NumPy Python

In this machine learning regression project, you will learn to build NumPy Regression Models (Linear Regression, Ridge Regression, Lasso Regression) from Scratch.

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Linear Regression Model Project in Python for Beginners Part 1

Machine Learning Linear Regression Project in Python to build a simple linear regression model and master the fundamentals of regression for beginners.

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Linear Regression Model Project in Python for Beginners Part 2

Machine Learning Linear Regression Project for Beginners in Python to Build a Multiple Linear Regression Model on Soccer Player Dataset.

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Learn Hyperparameter Tuning for Neural Networks with PyTorch

In this Deep Learning Project, you will learn how to optimally tune the hyperparameters (learning rate, epochs, dropout, early stopping) of a neural network model in PyTorch to improve model performance.

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Learn How to Build PyTorch Neural Networks from Scratch

In this deep learning project, you will learn how to build PyTorch neural networks from scratch.

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Build a Customer Churn Prediction Model using Decision Trees

Develop a customer churn prediction model using decision tree machine learning algorithms and data science on streaming service data.

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Build a Logistic Regression Model in Python from Scratch

Regression project to implement logistic regression in python from scratch on streaming app data.

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Topic modelling using Kmeans clustering to group customer reviews

In this Kmeans clustering machine learning project, you will perform topic modelling in order to group customer reviews based on recurring patterns.

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Build a Music Recommendation Algorithm using KKBox's Dataset

Music Recommendation Project using Machine Learning - Use the KKBox dataset to predict the chances of a user listening to a song again after their very first noticeable listening event.

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NLP and Deep Learning For Fake News Classification in Python

In this project you will use Python to implement various machine learning methods( RNN, LSTM, GRU) for fake news classification.

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Locality Sensitive Hashing Python Code for Look-Alike Modelling

In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.

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Forecasting Business KPI's with Tensorflow and Python

In this machine learning project, you will use the video clip of an IPL match played between CSK and RCB to forecast key performance indicators like the number of appearances of a brand logo, the frames, and the shortest and longest area percentage in the video.

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Build a Churn Prediction Model using Ensemble Learning

Learn how to build ensemble machine learning models like Random Forest, Adaboost, and Gradient Boosting for Customer Churn Prediction using Python

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MLOps AWS Project on Topic Modeling using Gunicorn Flask

In this project we will see the end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable machine learning models by using AWS

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End-to-End Speech Emotion Recognition Project using ANN

Speech Emotion Recognition using RAVDESS Audio Dataset - Build an Artificial Neural Network Model to Classify Audio Data into various Emotions like Sad, Happy, Angry, and Neutral

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Learn How to Build a Linear Regression Model in PyTorch

In this Machine Learning Project, you will learn how to build a simple linear regression model in PyTorch to predict the number of days subscribed.

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Learn How to Build a Logistic Regression Model in PyTorch

In this Machine Learning Project, you will learn how to build a simple logistic regression model in PyTorch for customer churn prediction.

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Build a CNN Model with PyTorch for Image Classification

In this deep learning project, you will learn how to build an Image Classification Model using PyTorch CNN

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MLOps Project to Build Search Relevancy Algorithm with SBERT

In this MLOps SBERT project you will learn to build and deploy an accurate and scalable search algorithm on AWS using SBERT and ANNOY to enhance search relevancy in news articles.

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Data Science Project-TalkingData AdTracking Fraud Detection

Machine Learning Project in R-Detect fraudulent click traffic for mobile app ads using R data science programming language.

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Jingwei Li

Graduate Research assistance at Stony Brook University

ProjectPro is a unique platform and helps many people in the industry to solve real-life problems with a step-by-step walkthrough of projects. A platform with some fantastic resources to gain hands-on experience and prepare for job interviews. I would highly recommend this platform to anyone looking to upskill and stay updated with the latest projects and solutions. Overall this platform is awesome and worth the money spent as we get a lot of value out of it and helps soar our career to greater heights.

Anand Kumpatla

Sr Data Scientist @ Doubleslash Software Solutions Pvt Ltd

I come from a background in Marketing and Analytics and when I developed an interest in Machine Learning algorithms, I did multiple in-class courses from reputed institutions though I got good theoretical knowledge, the practical approach, real word application, and deployment knowledge were missing. ProjectPro helped me bridge that gap. ProjectPro has real-time projects that helped me improve my skills. What I liked most is that I get exposure to so many projects, given the work nature I wouldn't have gotten exposure to such a variety of projects and their approaches. It is helping me apply knowledge to other projects too. I highly recommend ProjectPro to everyone who wants to excel in their DataScience career.

Ameeruddin Mohammed

ETL (Abintio) developer at IBM

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Graduate Student at Northwestern University

I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop Admin, Hadoop projects. I have been happy with every project. They have really brought me into the forefront of Data Science and Big data. I would recommend this to everyone. It is more than worth the price. After working with them I feel so much more employable for current projects.

Ray han

Tech Leader | Stanford / Yale University

I am the Director of Data Analytics with over 10+ years of IT experience. I have a background in SQL, Python, and Big Data working with Accenture, IBM, and Infosys. I am looking to enhance my skills in Data Engineering/Science and hoping to find real-world projects fortunately, I came across Project Pro. Project Pro helped me by providing an in-depth explanation of the end-to-end real-world data engineering projects. From data extraction, transformation, and storage up to data visualization. I learned more about Kafka, AWS, NI-FI, and Spark. Thru the help of the knowledge I gained from Project Pro, I was able to do well in the coding exams, interview and helped me land a job at EY. I will recommend every aspiring data professional as well as existing data science/engineer expert to try Project Pro to enhance their knowledge.

Ed Godalle

Director Data Analytics at EY / EY Tech

Having worked in the field of Data Science, I wanted to explore how I can implement projects in other domains, So I thought of connecting with ProjectPro. A project that helped me absorb this topic was "Credit Risk Modelling". To understand other domains, it is important to wear a thinking cap and that's where ProjectPro helped me. I also got a chance to talk to experts who have worked on these domains - they helped me by walking through the project. Kudos to the ProjectPro team!

Gautam Vermani

Data Consultant at Confidential

As a student looking to break into the field of data engineering and data science, one can get really confused as to which path to take. Very few ways to do it are Google, YouTube, etc. I was one of them too, and that's when I came across ProjectPro while watching one of the SQL videos on the E-Learning Bridge YouTube channel. One of the standout features was that it featured real projects on topics I just read about, across different job descriptions at the time. The main issue was the right path to guide us in using these tools and adding to the resume, and that's exactly what ProjectPro got me through. The fact that I can have a reliable route and videos explaining each tool in detail really motivated me to continue with the platform. Another thing we all struggle with is how to really connect with someone if we're stuck somewhere because there are so many solutions. But this has also been solved by experts we can chat with and believe me when I say this they will do whatever it takes to solve your problem even if it takes longer than expected. In my sophomore year of college and getting hands-on exposure to technologies like PySpark, NLP, Kafka, etc, and being able to really apply the theory and work on a project from start to finish really boosted my confidence in general!

Savvy Sahai

Data Science Intern, Capgemini

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