Machine and Deep Learning Algorithms E-Learning

Order this unique E-Learning course on Machine and Deep Learning Algorithms online!
✔️ 1 year 24/7 access to rich interactive videos, voice, progress monitoring through reports and tests per chapter to test your knowledge directly.
✔️ After the course, you will receive a certificate of attendance.

Why choose this course?

Machine Learning (ML) and Deep Learning (DL) are the foundations of artificial intelligence that is increasingly used in various sectors such as healthcare, finance, technology and more. Understanding the algorithms that drive these systems is essential for anyone active in the world of data science and AI.

This course provides a comprehensive introduction to the basics of machine learning and deep learning, focusing on the algorithms that drive these powerful technologies. You will learn the fundamental concepts and get hands-on experience building and implementing algorithms that can be applied to real business challenges.

What you will learn:

• Basics of Machine Learning and Deep Learning: Understand the differences, applications and capabilities of ML and DL.
• Key Algorithms in Machine Learning: Learn about regression, classification, clustering and other commonly used algorithms.
• Deep Learning Algorithms: Gain insight into neural networks, convolutional networks (CNNs), recurrent networks (RNNs) and more advanced deep learning algorithms.
• Evaluation of Models: Discover techniques for testing, evaluating and improving machine learning and deep learning models.
• Practical applications of ML and DL: Learn how to apply algorithms in various scenarios, from image and speech recognition to prediction and data analysis.

Who should participate?

This course is ideal for:

• Data scientists who want to deepen their knowledge of machine learning and deep learning.
• Software developers who want to integrate AI and ML into their applications.
• Machine Learning enthusiasts who want to build a thorough knowledge of the underlying algorithms.
• Business Intelligence professionals who want to learn how to leverage machine learning algorithms for better business decisions.
• IT professionals who want to develop or implement AI solutions for their organisation.

Demo Machine and Deep Learning Algorithms Training

Course content

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

Get started with Machine and Deep Learning Algorithms!

✔️ Learn at your own pace with interactive videos and voice commands.
✔️ Test your knowledge chapter by chapter with tests to track your progress.
✔️ Get hands-on skills in building machine learning and deep learning models.
✔️ Get a certificate of participation upon successful completion of the course.

Order your course now and become an expert in machine learning and deep learning algorithms!