You’ll master fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python. You’ll apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers.
Through hands-on projects, you’ll gain essential data science skills scaling machine learning algorithms on big data using Apache Spark. You’ll build, train, and deploy different types of deep architectures, including convolutional neural networks, recurrent networks, and autoencoders.
In addition to earning a Professional Certificate from Coursera, you will also receive a digital badge from IBM recognizing your proficiency in AI engineering.
- Машинное обучение с использованием Python.
- Introduction to Deep Learning & Neural Networks with Keras.
- Introduction to Computer Vision and Image Processing.
- Deep Neural Networks with PyTorch.
- Building Deep Learning Models with TensorFlow.
- AI Capstone Project with Deep Learning.
What will you learn
- Describe machine learning, deep learning, neural networks, and ML algorithms like classification, regression, clustering, and dimensional reduction.
- Deploy machine learning algorithms and pipelines on Apache Spark.
- Implement supervised and unsupervised machine learning models using SciPy and ScikitLearn.
- Build deep learning models and neural networks using Keras, PyTorch, and TensorFlow.