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