Learn Python, NumPy, Pandas, scikit-learn, Regression, Clustering, Classification, SVM, Random Forests, Decision Trees, Dimensionality Reduction, TensorFlow, Convolutional & Recurrent Neural Networks, Autoencoders, Reinforcement Learning From Industry Experts
Machine learning is behind these innovations. In the recent times, it has been proven that machine learning and deep learning approach to solving a problem gives far better accuracy than other approaches. This has led to a Tsunami in the area of Machine Learning.
Most of the domains that were considered specializations are now being merged into Machine Learning. This has happened because of the following:
- Better research and algorithms
- Better computing resources
- Distributed computing infrastructures
- Availablity of Big Data
It consist of 3 Courses: 1. Python for Machine Learning
2. Machine Learning
3. Deep Learning
Apply the skills you learn on a distributed cluster to solve real-world problems.
Highlight your new skills on your resume or LinkedIn.
Work on 18+ projects to get hands-on experience.
Timely Doubt Resolution through the Discussion Forum with the help of international community of peers
Churn the mail activity from various individuals in an open source project development team.
2. Predict bikes rental demand
Build a model to predict the bikes demand given the past data.
3. Noise removal from the images
Build a model that takes a noisy image as an input and outputs the clean image.
4. Predict which passengers survived in the Titanic shipwreck
The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. In this project, you build a model to predict which passengers survived the tragedy.
5. Build a spam classifier
Build a model to classify email as spam or ham. First, download examples of spam and ham from Apache SpamAssassin’s public datasets and then train a model to classify email.
6. Build an Image Classifier in Fashion MNIST dataset
Classify images from the Fashion MNIST dataset using scikit-learn, and Python.
7. Deploy Machine Learning models to Production using Flask
Learn how to deploy a machine learning model as a web application using the Flask framework.
8. Build an Image Classifier in Fashion MNIST dataset
Classify images from the Fashion MNIST dataset using Tensorflow 2, Matplotlib, and Python.
9. Training from Scratch vs Transfer Learning
Learn how to train a neural network from scratch to classify data using TensorFlow 2, and how to use the weights of an already trained model to achieve classification to another set of data.
10. Working with Custom Loss Function
Create a custom loss function in Keras with TensorFlow 2 backend.
11. Image Classification with Pre-trained Keras models
Learn how to access the pre-trained models(here we get pre-trained ResNet model) from Keras of TensorFlow 2 to classify images.
12. Build cats classifier using transfer learning
In this project, you will build a basic neural network to classify if a given image is of cat or not using transfer learning technique with Python and Keras.
13. Mask R-CNN with OpenCV for Object Detection
Learn how to read a pre-trained TensorFlow model for object detection using OpenCV.
14. Art Generation Project
Use TensorFlow 2 to generate an image that is an artistic blend of a content image and style image using Neural Style Transfer.
15. NYSE Stock Closing Price Prediction using TensorFlow 2 & Keras
Predict stock market closing prices for a firm using GRU, a state-of-art deep learning algorithm for sequential data, with Keras and Python.
16. Sentiment Analysis using IMDB dataset
Create a sentiment analysis model with the IMDB dataset using TensorFlow 2.
17. Autoencoders for Fashion MNIST
Learn how to practically implement the autoencoder, stacking an encoder and decoder using TensorFlow 2, and depict reconstructed output images by the autoencoder model using the Fashion MNIST dataset.
18. Deploy Image Classification Pre-trained Keras model using Flask
Learn how to deploy a deep learning model as a web application using the Flask framework.
Earn your certificate
Our course is exhaustive and the certificate rewarded by them is proof that you have taken a big leap in Machine Learning and Deep Learning.