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 middel van rapporten en testen per hoofdstuk om je kennis direct te toetsen.
✔️ Na de cursus ontvang je een certificaat van deelname.

Waarom kiezen voor deze opleiding?

Machine Learning (ML) en Deep Learning (DL) zijn de fundamenten van de kunstmatige intelligentie die steeds vaker wordt toegepast in verschillende sectoren zoals gezondheidszorg, finance, technologie en meer. Het begrijpen van de algoritmes die deze systemen aandrijven, is essentieel voor iedereen die actief is in de wereld van datawetenschap en AI.

Deze cursus biedt een uitgebreide inleiding tot de basisprincipes van machine learning en deep learning, waarbij de nadruk ligt op de algoritmes die deze krachtige technologieën aandrijven. Je leert de fundamentele concepten en krijgt hands-on ervaring met het bouwen en implementeren van algoritmes die kunnen worden toegepast op echte zakelijke uitdagingen.

Wat je zult leren:

• Basisprincipes van Machine Learning en Deep Learning: Begrijp de verschillen, toepassingen en mogelijkheden van ML en DL.
• Belangrijkste Algoritmes in Machine Learning: Leer over regressie, classificatie, clustering en andere veelgebruikte algoritmes.
• Deep Learning Algoritmes: Krijg inzicht in neurale netwerken, convolutionele netwerken (CNN's), recurrente netwerken (RNN's) en meer geavanceerde deep learning-algoritmes.
• Evaluatie van Modellen: Ontdek technieken voor het testen, evalueren en verbeteren van machine learning- en deep learning-modellen.
• Praktische toepassingen van ML en DL: Leer hoe je algoritmes kunt toepassen in diverse scenario's, van beeld- en spraakherkenning tot voorspellingen en data-analyse.

Wie zou moeten deelnemen?

Deze opleiding is ideaal voor:

• Data scientists die hun kennis van machine learning en deep learning willen verdiepen.
• Softwareontwikkelaars die AI en ML willen integreren in hun applicaties.
• Machine Learning-enthousiastelingen die een grondige kennis van de onderliggende algoritmes willen opbouwen.
• Business Intelligence-professionals die willen leren hoe ze machine learning-algoritmes kunnen inzetten voor betere bedrijfsbeslissingen.
• IT-professionals die AI-oplossingen willen ontwikkelen of implementeren voor hun organisatie.

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

Ga aan de slag met Machine and Deep Learning Algorithms!

✔️ Leer op je eigen tempo met interactieve video's en spraakopdrachten.
✔️ Test je kennis per hoofdstuk met toetsen om je voortgang bij te houden.
✔️ Ontvang praktische vaardigheden in het bouwen van machine learning en deep learning modellen.
✔️ Krijg een certificaat van deelname na succesvolle afronding van de cursus.

Bestel nu jouw cursus en word een expert in machine learning en deep learning algoritmes!