Learn the powerful tools used in data science and machine learning from a top instructor

What you will learn

Develop 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

Description

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.

English

Language

Content

Module 1: Introduction to Machine Learning

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)

Module 2: Exploring and Using Data Sets

Lesson 1a: Classification (Support Vector Machines)

Lesson 1b: Classification (Naive Bayes)

Lesson 2-1: Lab1a and 1b

Lesson 1a: Classification (Support Vector Machines)

dsml-seg-20-Decision_Trees

dsml-seg-21-RandomForests

dsml-seg-22-Lab2a_2b

dsml-seg-23-Lab2c

dsml-seg-24-clustering


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dsml-seg-25-pca

dsml-seg-26-lab_3a_3b

dsml-seg-27-lab_3c

Module 3: Review of Machine Learning Algorithms

dsml-seg-28-deep-learning-introduction

dsml-seg-29-Lab_1a

dsml-seg-30-tensorflow-introduction

dsml-seg-31-lab_1b

dsml-seg-32-tensorflow-Low-Level

dsml-seg-33-tensorfiow-Linear-Models

dsml-seg-33-tensorfiow-Linear-Models

dsml-seg-34-lab_2a_2b

dsml-seg-35-tensorflow-High-Level-API

dsml-seg-36-Lab_2c_2d

dsml-seg-37-lab_3a

dsml-seg-38-lab_3b_3c

dsml-seg-39-lab_3d_3e

dsml-seg-40-multilayer-perceptron-mlp

Module 4: Machine Learning with Scikit

dsml-seg-41-convolutional-neural-network

dsml-seg-42-convolutional-neural-network-extended

dsml-seg-43-tensorboard

Module 5: Deep Learning with Keras and TensorFlow

dsml-seg-44-transfer-learning

dsml-seg-45-recurrent-neural-network

dsml-seg-46-long-short-term-memory

Module 7: Building a Machine Learning Pipeline

dsml-seg-47-scaling-machine-learning-distributed-tensorflow

dsml-seg-48-feature-engineering

dsml-seg-49-pipeline-examples

Quiz

Quiz 1

Quiz 2

Quiz 3

Quiz 4

Quiz 5