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  3. Data Science & Machine Learning - Developer Certification

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.

Mark Kerzner
Mark Kerzner
Data Science | advanced | 3 hours |   Published: Nov 2019
In partnership with:  Coursera

    Discussions

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

Frequently Asked Questions

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Yes, we do offer a certification upon completion of our course to showcase your newly acquired skills and expertise.

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Yes, we do offer a 100% refund guarantee for our courses within a specified time frame. If you are not satisfied with the course, contact our customer support team to request a refund with your order details. Some restrictions may apply.

*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

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.
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About this course: Data Science & Machine Learning - Developer Certification

Instructor Bio: Mark Kerzner

Statistics Module Slides - presentation

Focus and Objectives

READING: ISLR (Read Chapter 8 - Trees)

READING: ISLR (Read Chapter 9 - Support Vector Machine)

READING: ISLR (Read Chapter 10 - Unsupervised)

SLIDES - In pdf format (ALL SLIDES AVAILABLE HERE)

Module 3 - SLIDES - Part 1

Module 3 - SLIDES - Part 2

Module 3 - SLIDES - Part 3

Module 3 - SLIDES - Part 4

Module 3 - SLIDES - Part 5

Module 3 - SLIDES - Part 6

Lesson 1a: Classification (Support Vector Machines)

Lesson 1b: Classification (Naive Bayes)

Lesson 2-1: Lab1a and 1b

Lesson 2a: Decision Trees

Lesson 2b: Random Forests

Lesson 2-1: Lab-2a and 2b

Lesson 2-1: Lab-2c

Lesson 3a: Clustering

Lesson 3b: Principal Component Analysis

Lesson 2-1: Lab-3a and 3b

Lesson 3-1: Lab-3c (Principal Component Analysis)

Module 2 - Focus and Objectives

READING: ISLR (Read Chapter 8 - Trees)

READING: ISLR (Read Chapter 9 - Support Vector Machine)

Lesson 1a: Classification (Support Vector Machines)

Lesson 1b: Classification (Naive Bayes)

Lesson 2-1: Lab1a and 1b

Focus and Objectives

READING: Place of Convolutional Neural Networks (CNN) and Deep Learning

READING: Parameter Sharing and CNN

READING: Understanding CNN

READING: A Brief History of CNNs in Image Segmentation

READING: CNN Architectures

SLIDES - In pdf format (ALL SLIDES AVAILABLE HERE)

Module 5 - SLIDES - Part 1

Lesson 1 - Convolutional Neural Networks

Lesson 2 - Convolutional Neural Networks, Extended

Lesson 3 - TensorBoard: Visualizing Learning

Overview

Quiz 1

Quiz 2

Quiz 3

Quiz 4

Quiz 5

Quiz 6

Focus and Objectives

READING: Intro to Machine Learning for Managers (Read Pages 1-12)

READING: Jeff Dean Rice Talk - State of Artificial Intelligence (Read entire document) (Dated but useful)

Module 1 - SLIDES - Part 1

Module 1 - SLIDES - Part 2

Module 1 - SLIDES - Part 3

Module 1 - SLIDES - Part 4

Module 1 - SLIDES - Part 5

Module 1 - SLIDES - Part 6

Module 1 - SLIDES - Part 7

Lesson 1: Introduction to Machine Learning

Lesson 1: Lab 1

Lesson 2-1: Lab-2a

Lesson 2-1: Pandas

Lesson 2-1: Exploring Pandas

Lesson 2-2: Lab-2b

Lesson 2-2: Lab 2c

Lesson 2-3: Visualization

Lesson 2-4: Lab-2d

Lesson 2-4: Visualization-Stats

Lesson 2-4: Lab 3a

Lesson 3-1: Sklearn

Lesson 3-2: Lab-3b

Lesson 3-2: Linear Regression

Lesson 3-3: Multivariate Linear Regression

Lesson 3-4: Logistic Regression (updated audio)

Focus and Objectives

READING: Introduction to Deep Learning

READING: Introduction to Linear Algebra

READING: Introduction to Statistics

SLIDES - In pdf format (ALL SLIDES AVAILABLE HERE)

Module 4 - SLIDES - Part 1

Module 4 - SLIDES - Part 2

Module 4 - SLIDES - Part 3

Module 4 - SLIDES - Part 4

Module 4 - SLIDES - Part 5

Module 4 - SLIDES - Part 6

Module 4 - SLIDES - Part 7

Module 4 - SLIDES - Part 8

Module 4 - SLIDES - Part 9

Module 4 - SLIDES - Part 10

Lesson 1a: Deep Learning - Intro

Lesson 1a: Lab 1a - Tensorflow Playground

Lesson 1b: TensorFlow - Intro

Lesson 1b: Lab 1b - Tensorflow Sessions

Lesson 1c: TensorFlow- Low Level API

Lesson 2a: TensorFlow - Linear Models

Lesson 2a: Lab 2a and 2b

Lesson 2b: TensorFlow - High-Level API

Lesson 2b: Lab 2c and 2d

Lesson 3a: Lab 3a

Lesson 3a: Lab 3b and 3c

Lesson 3b: Lab 3d and 3e

Lesson 4: Multilayer Perceptron (MLP)

Overview

INSTRUCTIONS: Virtual labs (For Colaboratory)

INSTRUCTIONS: USING JUPYTER NOTEBOOKS

Module 1: Labs

Module 2: Labs

Module 3: Labs

Module 4: Labs

Module 5: Labs

Module 6: Labs

Focus and Objectives

READING: The State of Machine Learning Adoption in the Enterprise

READING: AI Transformation Playbook

READING: Machine Learning Yearning - Andrew Ng (Read all)

READING: Efficient Estimation o fWord Representations in Vector Space

READING: Distributed Representations of Sentences and Documents

READING: Linguistic Regularities in Continuous Space Word Representations

READING: Distribited Repesentation of Words and Phrases

READING: Text Understanding from Scratch

READING: Machine Learning: At a Glance

SLIDES - In pdf format (ALL SLIDES AVAILABLE HERE)

Module 6 - SLIDES - Part 1

Module 6 - SLIDES - Part 2

Module 6 - SLIDES - Part 3

Module 6 - SLIDES - Part 4

Lesson 1: Scaling Machine Learning - Distributed TensorFlow

Lesson 2: Feature Engineering

Lesson 3: Pipeline Examples

Focus and Objectives

READING: An Introduction to Recurrent Neural Networks

READING: Sequence Modeling: Recurrent and Recursive Nets

READING: Convolutional Neural Networks for Text

SLIDES - In pdf format (ALL SLIDES AVAILABLE HERE)

Module 5 - SLIDES - Part 1

Module 5 - SLIDES - Part 2

Module 5 - SLIDES - Part 3

Lesson 1: Transfer Learning

Lesson 2: Recurrent Neural Networks

Lesson 3: Long Short-Term Memory (LSTM)