Machine and Deep Learning Algorithms E-Learning Training 

Bestel deze unieke E-Learning cursus Machine and Deep Learning Algorithms online, 1 jaar 24/ 7 toegang tot rijke interactieve video’s, spraak, voortgangsbewaking door rapportages en testen per hoofdstuk om de kennis direct te toetsen.

Cursusinhoud

Balancing the Four Vs of Data: The Four Vs of Data

Course: 40 Minutes

• Course Overview
• Overview of the Four Vs
• The Importance of Volume
• The Importance of Variety
• The Importance of Velocity
• The Importance of Veracity
• The Relationship Between the Four Vs
• Variety and Data Structure
• Validity and Volatility
• Finding Balance in the Four Vs
• Use Cases
• Extracting Value from the Four Vs
• Exercise: Describe the Four Vs of Big Data

Data Driven Organizations

Course: 1 Hour, 15 Minutes

• Course Overview
• Data Driven Organizations
• Decision Making
• Analytic Maturity
• Analytic Roles
• Data Source Priority
• Facets of Data Quality
• Power BI Data Visualization
• Missing Data
• Duplicate Data
• Truncated Data
• Data Provenance
• Exercise: Use Informatica Data Quality

Raw Data to Insights: Data Ingestion & Statistical Analysis

Course: 54 Minutes

• Course Overview
• Statistical Analysis
• Data Correction
• Outlier Detection
• Data Architecture Pattern
• Data Ingestion Tools
• Kafka and Apache NiFi
• Apache Sqoop Ingest
• Ingest Using WaveFront
• Exercise: Detecting Outliers and Ingesting Data

Raw Data to Insights: Data Management & Decision Making

Course: 57 Minutes

• Course Overview
• Data-driven Decision Making Framework
• Loading Data into R
• Preparing Data
• Data Correction Approach
• Data Correction Using Simple Transformation
• Data Correction Using Deductive Correction
• Distributed Data Management
• Data Analytics
• Data Analytics Using R
• Predictive Modeling
• Exercise: Correcting and Modelling Data

Tableau Desktop: Real Time Dashboards

Course: 1 Hour, 8 Minutes

• Course Overview
• Introducing Real Time Dashboards
• Creating Real Time Dashboards with Tableau
• Build a Tableau Dashboard
• Real Time Dashboard Updates in Tableau
• Organizing Your Tableau Dashboard
• Formatting Your Tableau Dashboard
• Interactive Tableau Dashboard
• Tableau Dashboard Starters
• Tableau Dashboard Extensions
• Tableau Dashboards and Story Points
• Sharing your Tableau Dashboard
• Exercise: Creating a Tableau Dashboard Starter

Storytelling with Data: Introduction

Course: 47 Minutes

• Course Overview
• Storytelling Process
• Interpreting Context
• Analysis Types
• Who, What, and How of Storytelling
• Visualization for Storytelling
• Graphical Tools for Data Elaboration
• Storytelling Scenarios
• Storyboarding
• Exercise: Visualization and Graphical Tool

Storytelling with Data: Tableau & PowerBI

Course: 57 Minutes

• Course Overview
• Visual Selection
• Slopegraphs
• Bar Charts and Types of Bar Charts
• Clutter and Clutter Elimination
• Gestalt Principle
• Story Design Best Practices
• Tools for Storytelling
• Decluttering
• Crafting Visual Data
• Visual Design Concerns
• Storytelling with Power BI
• Model Visual and Tableau
• Exercise: Storytelling with Power BI

Python for Data Science: Basic Data Visualization Using Seaborn

Course: 1 Hour, 7 Minutes

• Course Overview
• Introduction to Seaborn
• Install Seaborn
• Simple Univariate Distributions
• Configure Univariate Distribution Plots
• Simple Bivariate Distributions
• Explore Different Types of Bivariate Distributions
• Analyze Multiple Variable Pairs
• Regression Plots
• Themes and Styles in Seaborn
• Exercise: Basic Data Visualization Using Seaborn

Python for Data Science: Advanced Data Visualization Using Seaborn

Course: 1 Hour, 4 Minutes

• Course Overview
• Searching for Patterns in a Dataset
• Configuring Plot Aesthetics
• Normal Distribution and Outliers
• Distributions Within Categories - Part 1
• Distributions Within Categories - Part 2
• Analyzing Categories with Facet Grids - Part 1
• Analyzing Categories with Facet Grids - Part 2
• Introducing Color Palettes
• Using Color Palette8
• Exercise: Advanced Data Visualization Using Seaborn

Data Science Statistics: Using Python to Compute & Visualize Statistics

Course: 1 Hour, 16 Minutes

• Course Overview
• An Introduction to Matplotlib
• Analyzing Data Using NumPy and Pandas
• Visualizing Univariate and Bivariate Distributions
• Summary Statistics Using Native Python Functions
• Summary Statistics Using NumPy
• Summary Statistics Using the SciPy Library
• Correlation and Covariance
• Z-score
• Exercise: Compute and Visualize Statistics6 MinutesCompletedActions

R for Data Science: Data Visualization

Course: 33 Minutes

• Course Overview
• Using Scatter Plots
• Using Line Graphs
• Using Bar Charts
• Using Box and Whisker Plots
• Using Histograms
• Using a Bubble Plot
• Exercise: Data Visualization

Advanced Visualizations & Dashboards: Visualization Using Python

Course: 38 Minutes

• Course Overview
• Relevance of Data Visualization for Business
• Libraries for Data Visualization in Python
• Python Data Visualization Environment Configuration
• Matplotlib Libraries for Visualization
• Bar Chart Using ggplot
• Bokeh and Pygal
• Select Visualization Libraries
• Interactive Graphs and Image Files
• Plot Graphs
• Multiple Lines in Graphs
• Exercise: Create Line Charts with Pygal

Advanced Visualizations & Dashboards: Visualization Using R

Course: 35 Minutes

• Course Overview
• Chart Types
• Stacked Bar Plot
• Animate Plots with Matplotlib
• Plotting in Jupyter Notebook
• Graphics in R
• Heat Map and Scatter Plot in R
• Correlogram and Area Chart in R
• ggplot2 Capabilities
• Customize ggplot2 Graphs
• Exercise: Creating Heat Maps and Scatter Plots

Powering Recommendation Engines: Recommendation Engines

Course: 1 Hour, 5 Minutes

• Course Overview
• Describing Recommendation Engines
• Comparing the Types of Recommendation Engines
• Collecting and Manipulating Data
• Manipulating Data in R
• Describing Similarity and Neighborhoods
• Creating a Recommendation Engine
• Recommending Another Item
• Finding Items to Recommend
• Recommending Items Based on Other Items
• Evaluating a Recommendation System
• Validating a Recommendation System
• Exercise: Creating a Recommendation Engine

Data Insights, Anomalies, & Verification: Handling Anomalies

Course: 46 Minutes

• Course Overview
• Data and Anomaly Sources
• Decomposition and Forecasting
• Examine Data Using Randomization Tests
• Anomaly Detection
• Anomaly Detection Techniques
• Anomaly Detection with scikit-learn
• Anomaly Detection Tools
• Anomaly Detection Rules
• Exercise: Detecting Anomalies

Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools

Course: 51 Minutes

• Course Overview
• Machine Learning Anomaly Detection Techniques
• Comparing Anomaly Detection Algorithms
• Anomaly Detection Using R5
• Online Anomaly Detection Components
• Online Anomaly Detection Approaches
• Anomaly Detection Use Cases
• Anomaly Detection with Visualization Tools
• Anomaly Detection with Mathematical Approaches
• Cluster-Based Anomaly Detection
• Exercise: Detecting Anomalies

Data Science Statistics: Applied Inferential Statistics

Course: 1 Hour, 19 Minutes

• Course Overview
• The One-Sample T-test
• Independent and Paired T-tests
• Testing Hypotheses with T-tests
• Loading and Analyzing a Skewed Dataset
• Measuring Skewness and Kurtosis
• Preparing a Dataset for Regression
• Simple Linear Regression
• Multiple Linear Regression
• Exercise: Applied Inferential Statistics

Data Research Techniques

Course: 33 Minutes

• Course Overview
• Data Research Fundamentals
• Data Research Steps
• Values, Variables, and Observations
• JMP Scale of Measurement
• Non-experimental and Experimental Research
• Descriptive and Inferential Statistical Analysis
• Inferential Tests
• Case Study of Clinical Data Research
• Data Research in Sales Management
• Exercise: Implement Data Research

Data Research Exploration Techniques

Course: 50 Minutes

• Course Overview
• Fundamentals of Exploratory Data Analysis
• Data Exploration Types
• Working with R
• Data Exploration in R
• Data Exploration Using Plots
• Python Packages for Data Exploration
• Data Exploration Using Python
• Data Research Using Linear Algebra
• Linear Algebra for Data Research
• Exercise: R and Python for Data Exploration

Data Research Statistical Approaches

Course: 43 Minutes

• Course Overview
• Role of Statistics in Data Research
• Discrete vs. Continuous Distribution
• PDF and CDF
• Binomial Distribution
• Interval Estimation
• Point and Interval Estimation
• Data Visualization Techniques
• Data Visualization Using R
• Data Integration Techniques
• Creating Plots
• Missing Values and Outliers
• Exercise: Statistical Methods for Data Research

Machine & Deep Learning Algorithms: Introduction

Course: 46 Minutes

• Course Overview
• Machine Learning Algorithms
• How Machine Learning Works
• Introduction to Pandas ML
• Support Vector Machines
• Overfitting
• Exercise: Machine Learning and Classification

Machine & Deep Learning Algorithms: Regression & Clustering

Course: 49 Minutes

• Course Overview
• The Confusion Matrix
• An Introduction to Regression
• Applications of Regression
• Supervised and Unsupervised Learning
• Clustering
• Principal Component Analysis
• Exercise: Regression and Clustering

Machine & Deep Learning Algorithms: Data Preperation in Pandas ML

Course: 1 Hour, 4 Minutes

• Course Overview
• Data Preparation in scikit-learn
• Training and Evaluating Models in scikit-learn
• Introducing the Pandas ML ModelFrame
• Training and Evaluating Models in Pandas ML
• Preparing Data for Regression
• Evaluating Regression Models
• Preparing Data for Clustering
• The K-Means Clustering Algorithm
• Exercise: Regression, Classification, and Clustering

Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML

Course: 1 Hour, 24 Minutes

• Course Overview
• Analyzing an Imbalanced Dataset
• The RandomOverSampler
• The SMOTE Oversampler
• Undersampling Using imbalanced-learn
• Ensemble Classifiers for Imbalanced Data
• Combination Samplers
• Finding Correlations in a Dataset
• Building a Multi-Label Classification Model
• Dimensionality Reduction with PCA
• Imbalanced Learn and PCA

Creating Data APIs Using Node.js

Course: 1 Hour, 31 Minutes

• Course Overview
• API Prerequisites
• Building a RESTful API Using Node.js and Express.js
• RESTful API with OAuth
• HTTP Server with Hapi.js
• API Modules
• Returning Data with JSON
• Nodemon for Development Workflow
• API Requests
• POSTman for API
• Deploying APIs
• Social Media APIs
• Exercise: Building RESTful APIs