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Machine Learning Masterclass E-Learning Kurs

Machine Learning Masterclass E-Learning Kurs

€1.302,84 1.042,06 1.240,05 exkl. MwSt.

Preisgekröntes E-Learning der Meisterklasse für maschinelles Lernen mit Zugang zu einem Online-Mentor per Chat oder E-Mail, Abschlussprüfung und Practice Labs.

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Machine Learning Masterclass E-Learning Ausbildung

Architekten für maschinelles Lernen interpretieren Echtzeitanalysen von Daten, um die Effizienz in allen Geschäftsbereichen zu automatisieren und zu steigern. Dies ebnet den Weg für eine aussagekräftige KI, die von reaktiv zu prädiktiv wechselt. Diese Reise führt Sie durch Mechanismen wie die Computertheorie zum Übergang von einem ML-Programmierer zu einem ML / DL-Architekten.

Dit leertraject, met meer dan 100 uur online content, is onderverdeeld in de volgende vier tracks:

  • ML Track 1: Machine Learning Programmer
  • ML Track 2: Deep Learning Programmer
  • ML Track 3: Machine Learning Engineer
  • ML Track 4: Machine Learning Architect

Kursinhalt

Track 1: Machine Learning Programmer

In this track of the machine learning journey, the focus is linear regression, computational theory, and training sets.

E-Learning courses

NLP for ML with Python: NLP Using Python & NLTK
Course: 1 Hour, 3 Minutes

Course Overview
Uses and Challenges of NLP
Terminologies and Steps of NLP
Parsing Approach and Parser Types
Corpus and Corpus Linguistic
Regular Expressions in Python
NLP Libraries
NLTK Setup
Components of NLP
Tokenization
Tokenization with NLTK
Stop Words with NLTK
Exercise: NLP Terminologies and Stopworks

NLP for ML with Python: Advanced NLP Using spaCy & Scikit-learn
Course: 41 Minutes

Course Overview
Stemming and Lemmatization
Synonyms and Antonyms with NLTK
Topic Extraction with LDA
NER and Standard Libraries
POS Tagging and NLTK Implementations
spaCy Framework
Analyzing and Processing Texts
Text Classification Using scikit-learn
Sentiment Analysis
Exercise: Sentiment Analysis with scikit-learn

Linear Algebra and Probability: Fundamentals of Linear Algebra
Course: 1 Hour, 41 Minutes

Course Overview
Linear Algebra and Machine Learning
Class of Spaces
Types of Vector Space
Linear Product Vector and Theorems
Vector Arithmetic
Vector Scalar Multiplication
Vector Norms
Matrix Arithmetic
Working with Matrix
Matrix Operations
Matrix Decomposition
Exercise: Vector Norms and Matrix Arithmetic

Linear Algebra & Probability: Advanced Linear Algebra
Course: 1 Hour, 44 Minutes

Course Overview
Matrix and PCA
Sparse Matrix
Tensor Arithmetic
Hadamard Product and Tensors
Singular-Value Decomposition
Reconstruct Rectangular Matrix Using SVD
Probability
Probability Basics and Propositions
Random Variable
Central Limit Theorem
Parameter Estimation and Gaussian Distribution
Binomial Distribution
Exercise: Tensor Arithmetic and Hadamard Product

Linear Regression Models: Introduction to Linear Regression
Course: 1 Hour, 19 Minutes

Course Overview
Statistical Tools and Regression
Reasons to Use Regression
Regression Loss: Least Square Error
Capturing Variance in Regression
Prediction Using Regression
Introduction to Deep Learning
The Architecture of Neural Networks
Neurons: The Building Blocks of a Neural Network
Linear Regression Using a Single Neuron
Training a Neural Network
Gradient Descent Optimization
Exercise: Introduction to Linear Regression

Linear Regression Models: Building Simple Regression Models with Scikit Learn and Keras
Course: 42 Minutes

Course Overview
Statistical Tools and Regression
Reasons to Use Regression
Regression Loss: Least Square Error
Capturing Variance in Regression
Prediction Using Regression
Introduction to Deep Learning
The Architecture of Neural Networks
Neurons: The Building Blocks of a Neural Network
Linear Regression Using a Single Neuron
Training a Neural Network
Gradient Descent Optimization
Exercise: Introduction to Linear Regression

Linear Regression Models: Multiple and Parsimonious Linear Regression
Course: 1 Hour, 11 Minutes

Course Overview
Understanding Multiple Regression
Kitchen Sink Regression
Training and Evaluating the Model
Preparing Data for a Neural Network
Building a Neural Network
Evaluating the Neural Network
Finding Correlations in a Dataset
Introducing Parsimonious Regression
Applying Parsimonious Regression with Scikit Learn
Applying Parsimonious Regression with Keras
Exercise: Multiple Linear Regression

Linear Regression Models: An Introduction to Logistic Regression
Course: 58 Minutes

Course Overview
Introducing Logistic Regression
The Logistic Regression Curve
Logistic Regression and Classification
Logistic Regression vs. Linear Regression
Logistic Regression in Keras
Preparing Data for Logistic Regression
Classification using a Logistic Regression Model
Preparing Data for a Neural Network
Building and Evaluating the Keras Classifier
Exercise: An Introduction to Logistic Regression

Linear Regression Models: Simplifying Regression and Classification with Estimators
Course: 36 Minutes

Course Overview
Introducing Estimators
Preparing Data for a Linear Regressor Estimator
Training and Evaluating a Regressor Estimator
Preparing Data for a Linear Classifier Estimator
Training and Evaluating a Classifier Estimator
Exercise: Using TensorFlow Estimators

Computational Theory: Language Principle & Finite Automata Theory
Course: 45 Minutes

Course Overview
Theory of Computation
Computation Models
Automata Theory and Classes
Principles of Finite State Machine
Principles of Formal Languages and Automata Theory
Elements of Formal Language
Regular Expressions
Regular Grammar
Closure Properties of Regular Languages
Context-Free Grammar Features
Exercise: Computation Theory and Formal Language

Computational Theory: Using Turing, Transducers, & Complexity Classes
Course: 47 Minutes

Course Overview
Analytical Capabilities of Grammar
Normal Forms in Context-Free Grammar
Pushdown Automata
Turing Machines
Turing Machine Themes
Finite Transducers Types
Computation Limitations
Computational Complexity
P and NP Class
Recursively Enumerable Languages
Exercise: Turing Machines and Finite Transducers

Model Management: Building Machine Learning Models & Pipelines
Course: 32 Minutes

Course Overview
Machine Learning Algorithms and Models
Machine Learning Model Types
Machine Learning Model Development
Creating and Saving ML Models with scikit-learn
Models for Regression and Classification Management
Building Machine Learning Pipelines
Machine Learning Pipeline Tools
Machine Learning Pipeline Implementation
Iterative Machine Learning Model
Exercise: Build Machine Learning Models & Pipelines

Model Management: Building & Deploying Machine Learning Models in Production
Course: 56 Minutes

Course Overview
Hyperparameter Tuning
Hyperparameter Tuning with Grid Search
Reproducing Study
Machine Learning Metrics
Machine Learning Model Versioning
Machine Learning Model Versioning with Git and DVC
ModelDB Architecture
Model Management Framework
Studio.ml Setup
Machine Learning Model Creation
Machine Learning Model in Production
Deploying Machine Learning Model in Production
Exercise: Hyperparameter Tuning and Model Versioning

Bayesian Methods: Bayesian Concepts & Core Components
Course: 1 Hour, 1 Minute

Course Overview
Bayesian Probability and Statistical Inference
Bayes' Theorem in Machine Learning
Frequentist and Subjective Probability
Probability Distribution
Ingredients of Bayesian Statistics
Bayesian Methods
Bayesian Concepts in ML Modeling
Prior Knowledge Distribution
Bayesian Analysis Approach
Exercise: Bayesian Statistics and Analysis

Bayesian Methods: Implementing Bayesian Model and Computation with PyMC
Course: 48 Minutes

Course Overview
Bayesian Learning
Bayesian Model Types
Probabilistic Programming
Modeling with PyMC
Bayesian Data Analysis Process
Bayesian Data Analysis with PyMC
Bayesian Computation Methods
Markov Chain Simulation
Implementing Markov Chain Simulation
Finding Posterior Modes
Exercise: Bayesian Modeling with PyMC

Bayesian Methods: Advanced Bayesian Computation Model
Course: 52 Minutes

Course Overview
Bayesian Model and Linear Regression
Hierarchical Linear Model
Probability Model
Building Probability Models
Non-Linear Model
Gaussian Process
Mixture Model
Dirichlet Process Model
Bayesian Modeling with PyMC
Exercise: Implement Bayesian models

Reinforcement Learning: Essentials
Course: 30 Minutes

Course Overview
Reinforcement Learning Basics
Reinforcement Learning and Machine Learning
Reinforcement Learning Flow
State Change and Transition Process
Rewards and Reinforcement Learning
Agents in Reinforcement Learning
Types of Reinforcement Learning Environment
OpenAI
Exercise: Reinforcement Learning Elements

Reinforcement Learning: Tools & Frameworks
Course: 35 Minutes

Course Overview
Reinforcement Learning Types
Reinforcement Learning with Keras and Python
Markov Decision Process
Q-Learning Concepts
TensorFlow Installation
Reinforcement Learning and TensorFlow
Q-learning and Python
Exercise: Reinforcement Learning with Python

Math for Data Science & Machine Learning
Course: 1 Hour, 2 Minutes

Course Overview
Work with Vectors
Basis and Projection of Vectors
Work with Matrices
Matrix Multiplication
Matrix Division
Linear Transformations
Gaussian Elimination
Determinants
Orthogonal Matrices
Eigenvalues
Eigenvectors
Pseudo Inverse
Exercise: Math for Data Science and Machine Learning

Building ML Training Sets: Introduction
Course: 1 Hour, 10 Minutes

Course Overview
Loading and Exploring a Dataset
The Binarizer
The MinMaxScaler
The StandardScaler
The Normalizer
The MaxAbsScaler
Label Encoding
One-Hot Encoding
Exercise: Building ML Training Sets

Building ML Training Sets: Preprocessing Datasets for Linear Regression
Course: 51 Minutes

Course Overview
Loading and Analyzing a Dataset
Building and Evaluating a Linear Regression Model
Scaling and Encoding the Data
Analyzing the Effects of Preprocessing
Standardizing Continuous Data
Exercise: Preprocessing Data for Regression

Building ML Training Sets: Preprocessing Datasets for Classification
Course: 44 Minutes

Course Overview
Loading and Scaling a Dataset
Spotting Correlations in a Dataset
Principal Component Analysis
Normalizing a Dataset
Exercise: Processing Data for Classification

Linear Models & Gradient Descent: Managing Linear Models
Course: 48 Minutes

Course Overview
Linear Model and its Classification
Linear Modeling Approach
Generalized Linear Model
ANOVA and ANCOVA
Linear Model Implementation
Bias, Variance and Regularization
Ensemble Techniques
Bagging Implementation
Implementing Boosting Algorithm
Exercise: Linear Models and Ensemble

Linear Models & Gradient Descent: Gradient Descent and Regularization
Course: 54 Minutes

Course Overview
Types of Linear Regression
Simple and Multiple Regression
Implementing Simple Regression
Implementing Multiple Regression
Gradient Descent and Types
Gradient Descent Optimization Algorithms
Implementing Gradient Descent
Implementing Mini Batch Gradient Descent
Regularization Types
Implementing L1 & L2 Regularization
Exercise: Regression and Gradient Descent

Online Mentor

• You can reach your Mentor by entering chats or submitting an email.

Final Exam assessment

• Estimated duration: 90 minutes

Practice Labs: Machine Learning Programming with Python (estimated duration: 8 hours)

Perform ML programming tasks with Python, such as splitting data and standardizing data, and classification using nearest neighbors and ridge regression. Then, test your skills by answering assessment questions after performing principal component analysis, visualizing correlations, training a naive Bayes model and a support vector machine model. This lab provides access to several tools commonly used in ML, including: Microsoft Excel 2016, Visual Studio Code, Anaconda, Jupyter Notebook + JupyterHub, Pandas, NumPy, SiPy, Seaborn Library, Spyder IDE.

Track 2: Deep Learning Programmer

In this track of the machine learning journey, the focus is neural networks, CNNs, RNNs, and ML algorithms.

E-Learning courses

Getting Started with Neural Networks: Biological & Artificial Neural Networks
Course: 59 Minutes

Course Overview
Neural Network Fundamentals
Biological Neural Network
Artificial Neural Network Structure
Neural Network Architecture
Computational Models in Neural Networks
Neurons Interconnection
Threshold Functions and Artificial Neural Networks
Implementing Neural Networks
Building Neural Network Models
Use Cases of Artificial Neural Network
Exercise: Implement Neural Networks

Getting Started with Neural Networks: Perceptrons & Neural Network Algorithms
Course: 45 Minutes

Course Overview
Perceptrons
Single Layer Perceptron Training Model
Multilayer Perceptrons
Linear and Non-Linear Functions
Implement Perceptrons with Python
Backpropagation
Activation Functions
Perceptron Classifier
Exercise: Implement Perceptrons

Building Neural Networks: Development Principles
Course: 1 Hour, 21 Minutes

Course Overview
Artificial Neural Network Processing Components
Learning and Training in Artificial Neural Network
Cluster Analysis in Artificial Neural Network
Neural Network Building Blocks
Perceptron to Deep Neural Network
Model and Hyperparameter
Classification with Neural Networks
Deep Learning Frameworks
Neural Network Categorization
Neural Network Computational Model
Exercise: ANN Training and Classification

Building Neural Networks: Artificial Neural Networks Using Frameworks
Course: 1 Hour, 55 Minutes

Course Overview
Neural Network Building Components8
Evolutionary Algorithms and Gradient Descent
Build Neural Networks
Building Neural Networks with PyTorch
Object Image Classification
Learning Rates and Deep Learning Optimization
Optimizing Speed
Dense Network Tuning Using Hyperas
Linear Model with Estimators
Neural Network for Predictions
Optimization Approach for Predictions
Exercise: Build Neural Networks

Training Neural Networks: Implementing the Learning Process
Course: 1 Hour, 40 Minutes

Course Overview
Perceptrons and Neural Networks
Perceptron Learning Algorithm
Learning Rules in Neural Networks
Supervised and Unsupervised Learning
Neural Network Algorithms
Data Preparation For Neural Networks
ANN Training Process in Python
Algorithms to Train Neural Networks
Backpropagation in Python
Classification Algorithm for Learning
Regularization in Multilayer Perceptrons
Exercise: Implement ANN Learning

Training Neural Networks: Advanced Learning Algorithms
Course: 1 Hour, 41 Minutes

Course Overview
Online and Offline Learning
Training Patterns and Teaching Input
Training Samples
Baseline Overfitting and Underfitting
L1 and L2 Regularization
Training Neural Networks
Pattern Association Training Algorithms
Learning Vector Quantization
Modified Hebbian Learning
Hebbian Learning Rule
Competitive Learning
Optimizing Neural Networks
Debugging Neural Networks
Exercise: Implement Advanced Algorithms

Improving Neural Networks: Neural Network Performance Management
Course: 1 Hour, 57 Minutes

Course Overview
Iterative Machine Learning Workflow
Hyperparameter Optimization
Performance Management of Neural Networks
Impact of Dataset Sizes on Neural Network Models
Overfitting Prevention and Management
Neural Network Problems and Solutions
Bias and Variance
Implementing Bias and Variance Trade Off
Improving Performance Using Data and Algorithm
Model Evaluation and Selection
Exercise: Testing Models with Scikit-learn
Privacy and Cookie PolicyTerms of Use

Improving Neural Networks: Loss Function & Optimization
Course: 1 Hour, 4 Minutes

Course Overview
Loss Function
Impact of Loss Function
Calculating Loss Function
Causes of Optimization Problems
Optimizer Algorithms
Comparing Optimizer Algorithms
Learning Rate Optimizations
Implement Learning Rate Optimizer
Exercise: Working with Loss Function

Improving Neural Networks: Data Scaling & Regularization
Course: 1 Hour, 38 Minutes

Course Overview
Optimizing Networks
Rate Adaption Schedule Implementation with Keras
Scaling and Scaling Methods
Batch Normalization and Internal Covariate Shift
Implementing Batch Normalization
Implementing L1 Regularization
Implementing L2 Regularization
Implementing Gradient Descent
Exercise: L1 Regularization and Gradient Descent

ConvNets: Introduction to Convolutional Neural Networks
Course: 1 Hour, 1 Minute

Course Overview
Convolutional Neural Network Use Cases
How Convolutional Neural Network Works
Types of Convolutional Neural Network
Computer Vision Problems and Techniques
Image Recognition and Classification
Layers and Parameters of ConvNets
Maths for Convolutional Neural Network
Building CNN Image Classification Model
Exercise: Working with Convolutional Neural Networks

ConvNets: Working with Convolutional Neural Networks
Course: 43 Minutes

Course Overview
NN Architecture and Softmax Classifier
Working with ConvoNetJS
Edge Detection
Operations on Convolutions and Pooling
Maths and Rules for Filter and Channel Detection
Principles of Convolutional Layers
Activation Layer and Comparing Activation Functions
Improving Convolutional Neural Network Model
Exercise: Edge Detection and CNN Improvement

Convolutional Neural Networks: Fundamentals
Course: 46 Minutes

Course Overview
Visual Signal Perception
CNN Architecture
Principles of CNN
Sparse Interaction
Shared Parameters and Spatial Extents
Convolutional Padding and Strides
Pooling Layers
CNN and ReLU
Semantic Segmentation
Gradient Descent and its Variants
Exercise: CNN Architecture and Principles

Convolutional Neural Networks: Implementing & Training
Course: 31 Minutes

Course Overview
Image Recognition
ResNet Layers
PyTorch Ecosystem
Install and Configure PyTorch
CNN Using PyTorch
Training CNN
Exercise: Implementing CNNs with PyTorch

Convo Nets for Visual Recognition: Filters & Feature Mapping in CNN
Course: 1 Hour, 7 Minutes

Course Overview
Convolutional Networks
Convo Nets Architecture and Layers
Filters and Their Usage
Filters with Keras
Feature Map
Plotting Feature Map with Python
Optimization Parameters
Hyperparameters Tuning
Tuning Hyperparameters with TensorFlow and Keras
Pooling Layer
Implementing Pooling Layer
Exercise: Plotting Feature Map

Convo Nets for Visual Recognition: Computer Vision & CNN Architectures
Course: 49 Minutes

Course Overview
Activation Functions and Types1
Why ReLU in Convolutional Neural Networks
Implementing ReLU
Computer Vision Tasks
Developing Object Photo Classification Model
Fully-connected Layer
Convolutional Neural Network Training Process
Convolutional Neural Network Architectures
Exercise: Applying ReLU in CNN

Fundamentals of Sequence Model: Artificial Neural Network & Sequence Modeling
Course: 36 Minutes

Course Overview
Artificial Neural Network (ANN)
Components of ANN
Modeling Tools and Frameworks
Sequence Modeling
Recurrent Neural Network (RNN)
Types of RNN
Build a RNN with PyTorch and Google Colab
Exercise: ANN and Sequence Modeling

Fundamentals of Sequence Model: Language Model & Modeling Algorithms
Course: 19 Minutes

Course Overview
Language Model and NLP
Sequence Generation for NLP
Vanishing Gradient Problem
Gated Recurrent Unit (GRU)
Long Short-Term Memory (LSTM) Network
Exercise: Language Modeling

Build & Train RNNs: Neural Network Components
Course: 37 Minutes

Course Overview
Artificial Neural Network
Network Topologies
Neuron Activation Mechanism
Learning Samples
Supervised, Unsupervised, and Reinforcement
Training Samples
Training Set and Pattern Recognition
Gradient Optimization Procedure
Exercise: Learning and Training Samples

Build & Train RNNs: Implementing Recurrent Neural Networks
Course: 49 Minutes

Course Overview
Perception and Backpropagation
Single and Multilayer Perception
Building Recurrent Neural Network Models
RNN with Python and TensorFlow
LSTM with TensorFlow
Caffe2 and Neural Network
Implement RNN with Caffe
Deep Learning Language Model with Keras
Exercise: Implement RNN Using TensorFlow and Caffe

ML Algorithms: Multivariate Calculation & Algorithms
Course: 39 Minutes

Course Overview
Multivariate Calculus
Function Representation
Gradient and Derivative
Product and Chain Rule
Partial Differentiation
Linear Algebra
Gradient and Jacobian Matrix
Taylor's Theorem and Local Minima
Exercise: Multivariate Operations for Calculus

ML Algorithms: Machine Learning Implementation Using Calculus & Probability
Course: 31 Minutes

Course Overview
Probability and Machine Learning
Chain and Bayes Rules
Variance and Random Vectors
Estimation Parameters
Deep Learning and Calculus
R and Calculus
Calculus in Python
Series Expansion in Python
Exercise: Derivatives and Integrals with SymPy

Predictive Modeling: Predictive Analytics & Exploratory Data Analysis
Course: 41 Minutes

Course Overview
Predictive Analytics
Analytical Base Table
Business Problems and Predictive Modeling
Predictive Modeling with Python
Exploratory Data Analysis
Dataset and Variables Types
Missing Values and Outlier Management
Exercise: Predictive Modeling with Python

Predictive Modeling: Implementing Predictive Models Using Visualizations
Course: 42 Minutes

Course Overview
Feature Selection Algorithm
Predictive Models
Scatter Plots
Pearson's Correlation
Boxplot
Boxplot Using Python
Crosstab Using Python
Statistical Concepts for Predictive Models
Tree-Based Method
Best Practices for Predictive Modeling
Exercise: Implement Boxplots and Scatter Plots

Online Mentor

You can reach your Mentor by entering chats or submitting an email.

Final Exam assessment

Estimated duration: 90 minutes

Practice Labs: Deep Learning Programming with Python (estimated duration: 8 hours)

Perform DL programming tasks with Python, such as performing series expansion and calculus, and work
with TensorFlow and scikit-image. Then, test your skills by answering assessment questions after loading a
data set for hierarchical clustering and k-means clustering, and train a model using random forests and
gradient boosting.

Track 3: Machine Learning Engineer

In this track of the machine learning journey, the focus is predictive modeling and analytics, ml modeling, and ml architecting.

E-Learning collections

Predictive Modelling Best Practices: Applying Predictive Analytics
Course: 1 Hour, 27 Minutes

Course Overview
Overview of Predictive Analytics
The Predictive Modeling Process
Statistical Concepts for Predictive Modeling
Regression Techniques for Predictive Analytics
Commonly Used Models for Predictive Analytics
Survival Analysis for Customer Churn
Market Basket Analysis
Data Clustering Models
Random Forests
Probabilistic Graphical Models
Classification Models
Best Practices for Predictive Modeling
Exercise: Applying Predictive Analytics Models

Planning AI Implementation
Course: 45 Minutes

Course Overview
Setting Expectations
Challenges of AI
The Importance of Training
The Need for Data and Algorithms
Understanding the Human Problem
Developing Organizational Capability
Management Challenges
Avoiding AI Pitfalls
Developing a Strategy
Data Quality
AI Needs and Tools
Exercise: Describe AI Planning Considerations

Automation Design & Robotics
Course: 36 Minutes

Course Overview
Automation Overview
Automation Targets
Display Status
Human-Computer Collaboration
Human Intervention
Software Testing Automation
Task Runners in Software Design and Development
DevOps and Automated Deployment
Software Design Patterns for Robotics
Process Automation Using Robotics
Modern Robotics and AI Designs
Exercise: Applying Automation and Robotics Design

ML/DL in the Enterprise: Machine Learning Modeling, Development, & Deployment
Course: 1 Hour, 5 Minutes

Course Overview
Challenges of Machine Learning
Machine Learning Process Stages
Machine Learning Development Lifecycle
Machine Learning Workflow
Machine Learning Training Process
Machine Learning Platforms
Machine Learning Data Modelling and Processing
H2O Machine Learning Environment
Data Source Management
Machine Learning Pipeline
Git Code Movement
Exercise: Machine Learning Training Processes

ML/DL in the Enterprise: Machine Learning Infrastructure & Metamodel
Course: 54 Minutes

Course Overview
Infrastructure for Data and Process
Machine Learning and Data Pipeline
Machine Learning Models
Machine Learning Visualization
Machine Learning Frameworks and Tools
Working with H
Model Metadata and Governance
Risk Mitigation
Exercise: Build Data Pipelines and Visualization

Enterprise Services: Enterprise Machine Learning with AWS
Course: 1 Hour, 14 Minutes

Course Overview
Cloud and Machine Learning
Machine Learning Workflow Comparison
AWS Machine Learning Tools and Capabilities
Cloud Machine Learning Implementation Comparison
Generating Machine Learning Objects and Prediction
Amazon Machine Learning Console
Amazon SageMaker Architecture
Using Amazon SageMaker
Lex, Polly, and Transcribe
Amazon SageMaker Neo
Augmented Manifest in Amazon SageMaker
Amazon SageMaker Model Tuning
Amazon SageMaker Automatic Tuning
Course Summary

Enterprise Services: Machine Learning Implementation on Microsoft Azure
Course: 1 Hour, 13 Minutes

Course Overview
Azure Machine Learning Tools and Capabilities
Comparing Azure ML Studio and Azure ML Service
Creating & Configuring Azure ML Service Workspace
Building ML Pipelines with Azure ML Service
Working with Azure ML Studio
Using Azure ML Service Visual Interface
Working with Azure Open Datasets
Azure MLOps
Azure ML R Notebooks
Pipelines with Azure Data Lake and Azure ML
CI/CD for Machine Learning with Azure Pipeline
Using Microsoft DevLabs Extension
Course Summary

Enterprise Services: Machine Learning Implementation on Google Cloud Platform
Course: 1 Hour, 2 Minutes

Course Overview
GCP Machine Learning Tools and Capabilities
Google Cloud Platform ML Capabilities
Training and Job Execution with GCloud and Console
BigQuery and BigQuery ML Features
Implementing Models with BigQuery ML
ML Workflow Challenges and Serverless Approach
ML Implementation with Cloud Datalab
Google AI Platform Features and Components
Google Cloud AutoML Features
Managing Dataset Using AutoML Tables
Training Models and Predicting with AutoML Tables
Google Cloud AutoML Natural Language
Course Summary

Architecting Balance: Designing Hybrid Cloud Solutions
Course: 57 Minutes

Course Overview
Cloud Features and Deployment Models
Comparative Analysis of On-prem and Cloud Models
Factors Influencing On-premise & Cloud Architecture
Hybrid vs. Private vs. Public Cloud
Hybrid Cloud Need Assessment
Hybrid Cloud Strategy and Architecture
Hybrid Cloud Benefits
Challenges of Implementing Hybrid Cloud
Application Deployment Strategy
Setting up Hybrid Cloud Architecture
Exercise: Benefits of Hybrid Cloud

Enterprise Architecture: Architectural Principles & Patterns
Course: 1 Hour, 35 Minutes

Course Overview
Software Architecture Concepts
Software Architecture Principles
Architectural Models and Views
Software Architecture Styles
Principles of Developing Enterprise Architecture
Architectural Principles for Data and Technology
SOA Principles and the Maturity Model
Serverless Architecture
Backend-as-a-Service
Evolutionary Architecture
Documenting Architecture
Project Team and Collaboration
Consumer-Driven Contracts
Dimensions of Architecture to Maximize Benefit
Software Architecture Actions
Architectural Patterns and Styles
Course Summary

Enterprise Architecture: Design Architecture for Machine Learning Applications
Course: 1 Hour

Course Overview
Architecture for ML in Enterprises
Software Architecture to Model ML Apps in Production
Model Machine Learning Apps
ML Reference Architecture and Building Blocks
Evolvable Architectures and Migration
Pitfalls of Evolutionary Architecture and Antipatterns
Setting Up ML Solutions
Fitness Function and Categories
Architecture for Refinement and Production Readiness
Course Summary

Architecting Balance: Hybrid Cloud Implementation with AWS & Azure
Course: 1 Hour, 8 Minutes

Course Overview
Use Cases of AWS Hybrid Cloud
AWS Services for Hybrid Cloud Implementation
Cloudbursting Application Hosting Model
AWS Services for Resource and Deployment Management
Hybrid Data Lake Implementation
Principles and Best Practices of AWS Hybrid
Azure Components for Hybrid Solutions
Azure Hybrid Services
Azure Stack
Azure Tooling and DevOps for Hybrid Cloud
Azure Stack Implementation
Exercise: Implement Hybrid Cloud with Azure

Refactoring ML/DL Algorithms: Techniques & Principles
Course: 1 Hour, 6 Minutes

Course Overview
Role of Refactoring
Technical Debts
Refactoring Techniques
PyCharm for Refactoring
Code Analysis and Refactoring
Design Principles
Refactoring Principles and Challenges
Principles of Good Code
Refactoring Python Code
Code Optimization
Using Rope to Refactor
Anti-patterns in Code
Exercise: Refactoring Techniques

Refactoring ML/DL Algorithms: Refactor Machine Learning Algorithms
Course: 59 Minutes

Course Overview
Machine Learning Types
Machine Learning Algorithm Design
Impact of Refactoring on Machine Learning
Algorithm Design
Machine Learning Algorithm Comparison
Refactor Machine Learning Code
Managing Technical Debt in Machine Learning
SonarQube and Code Coverage
Automatic Clone Refactoring
Exercise: Refactoring Machine Learning Code

Online Mentor

You can reach your Mentor by entering chats or submitting an email.

Final Exam assessment

Estimated duration: 90 minutes

Practice Labs: Architecting ML/DL Apps with Python (estimated duration: 8 hours)

Perform architecting tasks such as binning data, imputing values, performing cross validation, and evaluating
a classification model. Then, test your skills by answering assessment questions after validating a model,
tuning parameters, refactoring a machine learning model, and saving and loading models using Python.

Track 4: Machine Learning Architect

In this track of the machine learning journey, the focus is applied predictive modeling, CNNs and RNNs, and ML algorithms.

E-Learning collections

Applied Predictive Modeling
Course: 1 Hour, 8 Minutes

Course Overview
Overview of Predictive Modeling
Exploratory Data Analysis
Overview of Regression Methods
Linear Regression in Python
Logistic Regression in Python
Overview of Clustering Methods
Hierarchical Clustering in Python
K-Means Clustering in Python
Overview of Decision Trees and Random Forests
Decision Trees in Python
Random Forests in Python
Exercise: Apply Predictive Models

Implementing Deep Learning: Practical Deep Learning Using Frameworks & Tools
Course: 1 Hour

Course Overview
Comparing DL and ML
ML/DL Workflow
Deep Learning Network Components
DL/ML Frameworks
Recurrent CNN with Caffe
Autoencoders and PyTorch
Deep Neural Network Implementation
Platform and Framework Comparison
Selecting the Right ML/DL Frameworks
Challenges of Debugging Deep Learning Networks
Exercise: Using DL Frameworks and Tools

Implementing Deep Learning: Optimized Deep Learning Applications
Course: 43 Minutes

Course Overview
Computational Graph and Deep Learning
Accelerating Architectures
GPU Interfaces
TFX and Pipeline Components for ML Pipelines
Setting up TFX
Build TFX Pipeline
Using TFMA
Practical Consideration for DL Build and Train
Deep Learning Parameters
Exercise: Optimizing Deep Learning Applications

Applied Deep Learning: Unsupervised Data
Course: 1 Hour, 28 Minutes

Course Overview
Deep Learning to Model NLP and Audio Analysis
Recurrent Neural Network Architectures
Unsupervised Learning Challenges in Deep Learning
Generative and Discriminative Classifiers
Types of Generative Models
PixelCNN Setup
Differences between MLP, CNN, and RNN
ResNet for Computer Vision
Encoders and Autoencoders
Exercise: RNN and ResNet

Applied Deep Learning: Generative Adversarial Networks and Q-Learning
Course: 45 Minutes

Course Overview
Implement Autoencoder Using Keras
Implementing Generative Adversarial Networks
Building GAN Model Using Python and Keras
Generative Adversarial Network Challenges
Deep Reinforcement Learning
Deep RL and Deep Learning Comparison
Generative Adversarial Network Variations
Deep Q-Learning
Deep Q-Learning in Python
Exercise: Implementing GAN and Deep Q-Learning

Advanced Reinforcement Learning: Principles
Course: 1 Hour, 13 Minutes

Course Overview
Reinforcement Learning Concepts
Comparing Reinforcement and Machine Learning
Reinforcement Learning Use Cases
Reinforcement Learning Terms and Workflow
Reinforcement Learning Implementation Approaches
Reinforcement Learning Algorithms
Markov Decision Process and Its Variants
Markov Reward Process and Value Functions
Markov Decision Process Toolbox Capabilities
Exercise: Reinforcement Learning and MDP Toolbox

Advanced Reinforcement Learning: Implementation
Course: 1 Hour, 35 Minutes

Course Overview
Installing the Markov Decision Process Toolbox
Rewards and Discounts
Multi-Armed Bandit Problem
Dynamic Programming and Bellman Equation
Reinforcement Learning Agent and Its Components
Reinforcement Learning with OpenAI Gym and Keras
Reinforcement Learning Taxonomy by OpenAI
Deep Q-Learning Implementation
Training DNN Using Reinforcement Learning
Exercise: Implementing Deep Q-Learning

ML/DL Best Practices: Machine Learning Workflow Best Practices
Course: 53 Minutes

Course Overview
Installing the Markov Decision Process Toolbox
Rewards and Discounts
Multi-Armed Bandit Problem
Dynamic Programming and Bellman Equation
Reinforcement Learning Agent and Its Components
Reinforcement Learning with OpenAI Gym and Keras
Reinforcement Learning Taxonomy by OpenAI
Deep Q-Learning Implementation
Training DNN Using Reinforcement Learning
Exercise: Implementing Deep Q-Learning

ML/DL Best Practices: Building Pipelines with Applied Rules
Course: 1 Hour, 4 Minutes

Course Overview
Troubleshooting Deep Learning and Using Checklists
ML Technical Challenges and Best Practices
Case Study to Analyze Impacts of Best Practices
Deployment Challenges and Patterns
Case Study of Deployment Approaches
Architecting and Building ML Pipelines
Rules for Building Machine Learning Pipelines
Feature Engineering Rules
Training-Serving Skew
Rules for Managing Optimization Refinement
ML Project Checklists for Project Managers
Course Summary

Research Topics in ML and DL
Course: 42 Minutes

Course Overview
Prevent Neural Networks from Overfitting
Multi-Label Learning Algorithms
Deep Residual Learning for Image Recognition
Transferable Features in Deep Neural Networks
Large-Scale Video Classification
Common Objects in Context
Generative Adversarial Nets
Scalable Nearest Neighbor Algorithms
Face Alignment with Ensemble of Regression Trees
Learning Deep Features for Scene Recognition
Extreme Learning Machine (ELM)
Exercise: Recognize Research Topics in ML and DL

Deep Learning with Keras
Course: 1 Hour, 56 Minutes

Course Overview
Neural Networks
Introduction to Keras
Keras Backend
Set up Keras
Model Types in Keras
Keras Layers
Regression Classification
Image Classification
Keras Metrics
Jupyter Notebooks
Dataset for Neural Network
Explore Your Dataset
Data Preparation
Compiling the Model
Training and Testing Neural Networks
Evaluate the Model
Making Predictions
Exercise: Using a Neural Network

Online Mentor

You can reach your Mentor by entering chats or submitting an email.

Final Exam assessment

Estimated duration: 90 minutes

Practice Labs: Architecting Advanced ML/DL Apps with Python (estimated duration: 8 hours)

Perform advanced ML/DL app architecture tasks using Python, such as loading a data set to train a simple multilayer perceptron (MLP), a Convolutional Neural Network (CNN) and an LSTM model. Then, test your skills by answering assessment questions after performing image and text classification using CNN.

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