What are some good machine learning projects?

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What are some good machine learning projects?

Top 10 Machine Learning Project Ideas That You Can Implement

  • Titanic Survival Project.
  • Personality Prediction Project.
  • Loan Prediction Project.
  • Stock Price Prediction Project.
  • Xbox Game Prediction Project.
  • Housing Prices Prediction Project.
  • Sales Prediction Project.
  • Digit Recognizer Project.

Is keras good for machine learning?

Keras is the high-level API of TensorFlow 2: an approachable, highly-productive interface for solving machine learning problems, with a focus on modern deep learning. It provides essential abstractions and building blocks for developing and shipping machine learning solutions with high iteration velocity.

What are some deep learning projects?

Deep Learning Project Ideas: Intermediate Level

  • Digit Recognition System.
  • Chatbot.
  • Music genre classification system.
  • Drowsiness detection system.
  • Image caption generator.
  • Colouring old B&W photos.

What is CNN in machine learning?

In deep learning, a convolutional neural network (CNN/ConvNet) is a class of deep neural networks, most commonly applied to analyze visual imagery. Now in mathematics convolution is a mathematical operation on two functions that produces a third function that expresses how the shape of one is modified by the other.

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What is an ML project?

Loan Prediction using Machine Learning Project idea – The idea behind this ML project is to build a model that will classify how much loan the user can take. It is based on the user’s marital status, education, number of dependents, and employments.

Should I learn keras or TensorFlow?

TensorFlow provides both high-level and low-level APIs while Keras provides only high-level APIs. Both frameworks thus provide high-level APIs for building and training models with ease. Keras is built in Python which makes it way more user-friendly than TensorFlow.

Can keras run without TensorFlow?

However, one size does not fit all when it comes to Machine Learning applications – the proper difference between Keras and TensorFlow is that Keras won’t work if you need to make low-level changes to your model. For that, you need TensorFlow.

How do I use CNN in Python?

We have 4 steps for convolution:

  1. Line up the feature and the image.
  2. Multiply each image pixel by corresponding feature pixel.
  3. Add the values and find the sum.
  4. Divide the sum by the total number of pixels in the feature.
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What is CNN deep learning?

A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other.

Is CNN better than RNN?

RNN is suitable for temporal data, also called sequential data. CNN is considered to be more powerful than RNN. RNN includes less feature compatibility when compared to CNN. This network takes fixed size inputs and generates fixed size outputs.

How can I be a good machine learning project?

A machine learning project may not be linear, but it has a number of well known steps:

  1. Define Problem.
  2. Prepare Data.
  3. Evaluate Algorithms.
  4. Improve Results.
  5. Present Results.

What is Keras in machine learning?

Keras is an incredibly powerful but simple to use API built on top of TensorFlow. Because of its ease of use, Keras is often used for rapid prototyping — imagine being able to train and test a model with just a few lines of code!

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What is the difference between Keras and TensorFlow?

TensorFlow is the one of most popular machine learning frameworks, and Keras is a high level API for deep learning which can be used with TensorFlow framework as its backend.

What are some good machine learning projects for beginners?

21 Machine Learning Project Ideas Ripe For The Taking 1. Cats and Dogs Image Classification using CNN. This is an amazing project. You can create an image classifier to… 2. Credit Card Fraud Detection. This is one of the most popular machine learning projects and there are plenty of… 3. Spam

How to become a successful machine learning developer?

When it comes to careers in software development, it is a must for aspiring developers to work on their own projects. Developing real-world projects is the best way to hone your skills and materialize your theoretical knowledge into practical experience. The more you experiment with different Machine Learning projects, the more knowledge you gain.