Data Science & Machine Learning - Developer Certification
The Data Science & Machine Learning Developer Certification program provides a comprehensive set of knowledge and skills in data science, machine learning, and deep learning.
Data Science | advanced | 3 hours | Published: Nov 2019
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Overview
2.3KSTUDENTS*
96.3%RECOMMEND*
This course includes:
3 Hours Expert-led Instruction
Extensive Use-Case Examples
24/7 Instructor Forums
Comprehensive Final Exam
Course Completion Certificate
Online Alumni Community
Online Chat Support / Mentoring
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.
Skills You Will Gain
Datascience
Keras
Python
Scikit-Learn
Tensorflow
Learning Outcomes (At The End Of This Program, You Will Be Able To...)
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
Prerequisites
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
Curriculum
Instructors
*Where courses have been offered multiple times, the “# Students” includes all students who have enrolled. The “%Recommended” shown is also based on this data.
1Welcome!
2Module 0: Statistics and Mathematics for Data Science
3Module 1: Introduction to Machine Learning
4Module 2:Exploring and Using Data Sets
5Module 3: Review of Machine Learning Algorithms
6Module 4: Machine Learning with Scikit
7Module 5: Deep Learning with Keras and TensorFlow
8Module 6: Deeper Understanding of Tensorflow
9Module 7: Building a Machine Learning Pipeline
10Quizzes
11Labs
Mark Kerzner
Mark Kerzner is a hands-on software architect, writer, consultant His expertise include Spark, Hadoop, MapReduce, MapR, Hive, Pig, Sqoop, Flume, HBase, Cassandra, high-performance multi-threaded applications, data mining, text analytics, mathematical optimization, linear and dynamic programming, and visualization (Tableau), among other things.A data scientist and an instructor who has delivered courses throughout the globe in data science.