Courses and Training

• Describe 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 

• explain foundational TensorFlow concepts such as the main functions, operations and the execution pipelines.

• describe how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. 

• understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

• apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.

Contents : 

• Describe what a neural network is, what a deep learning model is, and the difference between them.

• Demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines.

• Demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks.

• Build deep learning models and networks using the Keras library.

Contents : 

• Regression, classification, clustering, sci-kit learn and SciPy 

• Projects including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more.

• gain a practical understanding of Apache Spark, and apply it to solve machine learning problems involving both small and big data.

•  Understand how parallel code is written, capable of running on thousands of CPUs. 

• make use of large scale compute clusters to apply machine learning algorithms on Petabytes of data using Apache SparkML Pipelines. 

• eliminate out-of-memory errors generated by traditional machine learning frameworks when data doesn’t fit in a computer's main memory

• test thousands of different ML models in parallel to find the best performing one – a technique used by many successful Kagglers

• Run SQL statements on very large data sets using Apache SparkSQL and the Apache Spark DataFrame API.

Contents: 

Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).

Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).

Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).