What you will learn from this course
- Machine learning, deep learning, neural networks, and ML algorithms like classification, regression, clustering, and dimensional reduction
- Implement supervised and unsupervised machine learning models using SciPy and ScikitLearn
- Deploy machine learning algorithms and pipelines on Apache Spark
- Build deep learning models and neural networks using Keras, PyTorch, and TensorFlow
- Learn about applications of Machine Learning in different fields such as health care, banking, telecommunication, etc..
- Learn about Linear, Non-linear, Simple and Multiple regression, and their applications
- Learn how to evaluate your regression model, and calculate its accuracy
- Learn about different clustering approaches
- Learn how to use clustering for customer segmentation, grouping same vehicles, and also clustering of weather stations
- Learn how Apache Spark internally works and how to use it for data processing
- Learn about exciting applications of deep learning
- Learn about the gradient descent algorithm and how variables are optimized with respect to a defined function
- Learn about the different deep learning libraries namely, Keras, PyTorch, and TensorFlow
- Learn about the difference between the shallow and deep neural networks
- Learn about convolutional networks and how to build them using the Keras library
- Learn about recurrent neural networks and autoencoders
- Learn about TensorFlow, and use it to create Linear and Logistic Regression models
- Learn about the fundamentals of Deep Learning
- Learn about Convolutional Neural Networks, and the building blocks of a convolutional neural network
- Learn how to build Multi-layer perceptron and convolutional neural networks in Python and using TensorFlow
- Learn about the recurrent neural network model, and special type of a recurrent neural network
- Learn about the applications of unsupervised learning
- Learn about Restricted Boltzmann Machines (RBMs), and how to train an RBM
- Learn how to load the image dataset, manipulate images, and visualize them
- Learn how to process image data and prepare it to build a classifier using pre-trained models
- Learn how to build a linear classifier
- Learn how to build an image classifier using the ResNet50 pre-trained model
How to Enroll IBM AI Engineering Professional Certificate course?
How many members can access this course with a coupon?
IBM AI Engineering Professional Certificate Course coupon is limited to the first 1,000 enrollments. Click 'Enroll Now' to secure your spot and dive into this course on Udemy before it reaches its enrollment limits!
