The Data Science & Machine Learning Developer Certification program provides a comprehensive set of knowledge and skills in data science, machine learning, and deep learning. This immersive training curriculum covers all the key technologies, techniques, principles and practices you need to play a key role on your data science development team, and to distinguish yourself professionally.
Beginning with foundational principles and concepts used in data science and machine learning, this program moves progressively and rapidly to cover the foundational components at the core of machine learning. The program builds on the foundations and quickly moves into deep learning, along the way teaching you via lectures and interactive online labs.The training uses open-source tools — along with your developing judgment and intuition — to address actual business needs and real-world challenges.
This program also covers the significant development of deep learning methods that enable state-of-the-art performance for many tasks, including image classification, time series (such as audio) classification and natural language processing. In this program, delegates gain hands-on deep learning experience.
Delegates will learn by hands-on labs working tools including Python, Scikit-Learn, Keras, and Tensorflow.
This is module#2
- Module 1 : Introduction to Machine Learning
- Module 2 : Exploring Data Sets/ Machine Learning Algorithms
- Module 3 : Machine Learning with Scikit / Deep Learning with Keras and TensorFlow
- Module 4 : Deeper Understanding of Tensorflow / Building a Machine Learning Pipeline
- Develop solutions to real-world machine learning problems
- Explain and discuss the essential concepts of machine learning and in particular deep learning
- Implement supervised and unsupervised learning models for tasks such as forecasting, predicting and outlier detection
- Apply and use advanced machine learning applications, including recommendation systems and natural language processing
- Evaluate and apply deep learning concepts and software applications
- Identify, source and prepare raw data for analysis and modelling
- Work with open source tools such as Python, Scikit-learn, Keras and Tensorflow
- Exposure to coding (Python is helpful but not an absolute must)
- Exposure to math or, at the very least, no aversion (linear algebra helpful but not required)
Who Should Attend
- Developers aspiring to be a data scientist or machine learning engineer
- Developers seeking to understand machine and deep learning to be more valuable in their role interfacing with data scientists
- Analytics managers who are leading a team of analysts
- Business analysts who want to understand data science techniques
- Information architects who need expertise in machine learning algorithms
- Analytics professionals who work in machine learning or artificial intelligence
- Graduates looking to build a career in data science and machine learning