12 Unsupervised Machine Learning courses

This Python Machine Learning online instructor led course is an excellent introduction to popular machine learning algorithms. -------------------------------------------------------------------------------- Python Machine Learning 2-day Course Prerequisites: Basic knowledge of Python coding is a pre-requisite. Who Should Attend? This course is an overview of machine learning and machine learning algorithms in Python SciKitLearn. Practical: * We cover the below listed algorithms, which is only a small collection of what is available. However, it will give you a good understanding, to plan your Machine Learning project * We create, experiment and run machine learning sample code to implement a short selected but representative list of available the algorithms.  Course Outline: Supervised Machine Learning: * Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine * Regression Algorithms: Linear, Polynomial Unsupervised Machine Learning: * Clustering Algorithms: K-means clustering, Hierarchical Clustering * Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA) * Association Machine Learning Algorithms: Apriori, Euclat Other machine learning Algorithms: * Ensemble Methods ( Stacking, bagging, boosting ) Algorithms: Random Forest, Gradient Boosting * Reinforcement learning Algorithms: Q-Learning * Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN) Data Exploration and Preprocessing: * The first part of a Machine Learning project understands the data and the problem at hand. * Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy. What is included in this Python Machine Learning: * Python Machine Learning Certificate on completion   * Python Machine Learning notes * Practical Python Machine Learning exercises and code examples * After the course, 1 free, online session for questions or revision Python Machine Learning. * Max group size on this Python Machine Learning is 4. -------------------------------------------------------------------------------- REFUND POLICY No Refunds

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. PYTHON FOR DATA SCIENCE * ? Using Modules * ? Listing Methods in a Module * ? Creating Your Own Modules * ? List Comprehension * ? Dictionary Comprehension * ? String Comprehension * ? Python 2 vs Python 3 * ? Sets (Python 3+) * ? Python Idioms * ? Python Data Science ?Ecosystem? * ? NumPy * ? NumPy Arrays * ? NumPy Idioms * ? pandas * ? Data Wrangling with pandas' DataFrame * ? SciPy * ? Scikit-learn * ? SciPy or scikit-learn? * ? Matplotlib * ? Python vs R * ? Python on Apache Spark * ? Python Dev Tools and REPLs * ? Anaconda * ? IPython * ? Visual Studio Code * ? Jupyter * ? Jupyter Basic Commands * ? Summary APPLIED DATA SCIENCE * ? What is Data Science? * ? Data Science Ecosystem * ? Data Mining vs. Data Science * ? Business Analytics vs. Data Science * ? Data Science, Machine Learning, AI? * ? Who is a Data Scientist? * ? Data Science Skill Sets Venn Diagram * ? Data Scientists at Work * ? Examples of Data Science Projects * ? An Example of a Data Product * ? Applied Data Science at Google * ? Data Science Gotchas * ? Summary DATA ANALYTICS LIFE-CYCLE PHASES * ? Big Data Analytics Pipeline * ? Data Discovery Phase * ? Data Harvesting Phase * ? Data Priming Phase * ? Data Logistics and Data Governance * ? Exploratory Data Analysis * ? Model Planning Phase * ? Model Building Phase * ? Communicating the Results * ? Production Roll-out * ? Summary REPAIRING AND NORMALIZING DATA * ? Repairing and Normalizing Data * ? Dealing with the Missing Data * ? Sample Data Set * ? Getting Info on Null Data * ? Dropping a Column * ? Interpolating Missing Data in pandas * ? Replacing the Missing Values with the Mean Value * ? Scaling (Normalizing) the Data * ? Data Preprocessing with scikit-learn * ? Scaling with the scale() Function * ? The MinMaxScaler Object * ? Summary DESCRIPTIVE STATISTICS COMPUTING FEATURES IN PYTHON * ? Descriptive Statistics * ? Non-uniformity of a Probability Distribution * ? Using NumPy for Calculating Descriptive Statistics Measures * ? Finding Min and Max in NumPy * ? Using pandas for Calculating Descriptive Statistics Measures * ? Correlation * ? Regression and Correlation * ? Covariance * ? Getting Pairwise Correlation and Covariance Measures * ? Finding Min and Max in pandas DataFrame * ? Summary DATA AGGREGATION AND GROUPING * ? Data Aggregation and Grouping * ? Sample Data Set * ? The pandas.core.groupby.SeriesGroupBy Object * ? Grouping by Two or More Columns * ? Emulating the SQL's WHERE Clause * ? The Pivot Tables * ? Cross-Tabulation * ? Summary DATA VISUALIZATION WITH MATPLOTLIB * ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary DATA SCIENCE AND ML ALGORITHMS IN SCIKIT-LEARN * ? Data Science, Machine Learning, AI? * ? Types of Machine Learning * ? Terminology: Features and Observations * ? Continuous and Categorical Features (Variables) * ? Terminology: Axis * ? The scikit-learn Package * ? scikit-learn Estimators * ? Models, Estimators, and Predictors * ? Common Distance Metrics * ? The Euclidean Metric * ? The LIBSVM format * ? Scaling of the Features * ? The Curse of Dimensionality * ? Supervised vs Unsupervised Machine Learning * ? Supervised Machine Learning Algorithms * ? Unsupervised Machine Learning Algorithms * ? Choose the Right Algorithm * ? Life-cycles of Machine Learning Development * ? Data Split for Training and Test Data Sets * ? Data Splitting in scikit-learn * ? Hands-on Exercise * ? Classification Examples * ? Classifying with k-Nearest Neighbors (SL) * ? k-Nearest Neighbors Algorithm * ? k-Nearest Neighbors Algorithm * ? The Error Rate * ? Hands-on Exercise * ? Dimensionality Reduction * ? The Advantages of Dimensionality Reduction * ? Principal component analysis (PCA) * ? Hands-on Exercise * ? Data Blending * ? Decision Trees (SL) * ? Decision Tree Terminology * ? Decision Tree Classification in Context of Information Theory * ? Information Entropy Defined * ? The Shannon Entropy Formula * ? The Simplified Decision Tree Algorithm * ? Using Decision Trees * ? Random Forests * ? SVM * ? Naive Bayes Classifier (SL) * ? Naive Bayesian Probabilistic Model in a Nutshell * ? Bayes Formula * ? Classification of Documents with Naive Bayes * ? Unsupervised Learning Type: Clustering * ? Clustering Examples * ? k-Means Clustering (UL) * ? k-Means Clustering in a Nutshell * ? k-Means Characteristics * ? Regression Analysis * ? Simple Linear Regression Model * ? Linear vs Non-Linear Regression * ? Linear Regression Illustration * ? Major Underlying Assumptions for Regression Analysis * ? Least-Squares Method (LSM) * ? Locally Weighted Linear Regression * ? Regression Models in Excel * ? Multiple Regression Analysis * ? Logistic Regression * ? Regression vs Classification * ? Time-Series Analysis * ? Decomposing Time-Series * ? Summary LAB EXERCISES * Lab 1 - Learning the Lab Environment * Lab 2 - Using Jupyter Notebook * Lab 3 - Repairing and Normalizing Data * Lab 4 - Computing Descriptive Statistics * Lab 5 - Data Grouping and Aggregation * Lab 6 - Data Visualization with matplotlib * Lab 7 - Data Splitting * Lab 8 - k-Nearest Neighbors Algorithm * Lab 9 - The k-means Algorithm * Lab 10 - The Random Forest Algorithm

Duration 3 Days 18 CPD hours This course is intended for Network Security Operations Workload Application Administrators Security Operations Field Engineers Network Engineers Systems Engineers Technical Solutions Architects Cisco Integrators and Partners Overview After taking this course, you should be able to: Define the Cisco telemetry and analytics approach. Explore common scenarios that Cisco Tetration Analytics can solve. Describe how the Cisco Tetration Analytics platform collects telemetry and other context information. Discuss how relative agents are installed and configured. Explore the operational aspects of the Cisco Tetration Analytics platform. Describe the Cisco Tetration Analytics support for application visibility or application insight based on the Application Dependency Mapping (ADM) feature. List the concepts of the intent-based declarative network management automation model. Describe the Cisco Tetration policy enforcement pipeline, components, functions, and implementation of application policy. Describe how to use Cisco Tetration Analytics for workload protection in order to provide a secure infrastructure for business-critical applications and data. Describe Cisco Tetration Analytics platform use cases in the modern heterogeneous, multicloud data center. List the options for the Cisco Tetration Analytics platform enhancements. Explain how to perform the Cisco Tetration Analytics administration. This course teaches how to deploy, use, and operate Cisco© Tetration Analytics? platform for comprehensive workload-protection and application and network insights across a multicloud infrastructure. You will learn how the Cisco Tetration Analytics platform uses streaming telemetry, behavioral analysis, unsupervised machine learning, analytical intelligence, and big data analytics to deliver pervasive visibility, automated intent-based policy, workload protection, and performance management. EXPLORING CISCO TETRATION * Data Center Challenges * Define and Position Cisco Tetration * Cisco Tetration Features * Cisco Tetration Architecture * Cisco Tetration Deployment Models * Cisco Tetration GUI Overview IMPLEMENTING AND OPERATING CISCO TETRATION * Explore Data Collection * Install the Software Agent * Install the Hardware Agent * Import Context Data * Describe Cisco Tetration Operational Concepts EXAMINING CISCO TETRATION ADM AND APPLICATION INSIGHT * Describe Cisco Tetration Application Insight * Perform ADM * Interpret ADM Results Application Visibility EXAMINING CISCO TETRATION INTENT-BASED NETWORKING * Describe Intent-Based Policy * Examine Policy Features * Implement Policies ENFORCING TETRATION POLICY PIPELINE AND COMPLIANCE * Examine Policy Enforcement * Implement Application Policy * Examine Policy Compliance Verification and Simulation EXAMINING TETRATION SECURITY USE CASES * Examine Workload Security * Attack Prevention * Attack Detection * Attack Remediation EXAMINING IT OPERATIONS USE CASES * Key Features and IT Operations Use Cases * Performing Operations in Neighborhood App-based Use Cases EXAMINING PLATFORM ENHANCEMENT USE CASES * Integrations and Advanced Features * Third-party Integration Examples * Explore Data Platform Capabilities EXPLORING CISCO TETRATION ANALYTICS ADMINISTRATION * Examine User Authentication and Authorization * Examine Cluster Management * Configure Alerts and Syslog ADDITIONAL COURSE DETAILS: Nexus Humans Cisco Implementing Cisco Tetration Analytics v1.0 (DCITET) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Cisco Implementing Cisco Tetration Analytics v1.0 (DCITET) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

This course aims to teach you how to use Python for machine learning and data science.

Duration 2 Days 12 CPD hours This course is intended for Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. WHAT IS R * ? What is R? * ? Positioning of R in the Data Science Space * ? The Legal Aspects * ? Microsoft R Open * ? R Integrated Development Environments * ? Running R * ? Running RStudio * ? Getting Help * ? General Notes on R Commands and Statements * ? Assignment Operators * ? R Core Data Structures * ? Assignment Example * ? R Objects and Workspace * ? Printing Objects * ? Arithmetic Operators * ? Logical Operators * ? System Date and Time * ? Operations * ? User-defined Functions * ? Control Statements * ? Conditional Execution * ? Repetitive Execution * ? Repetitive execution * ? Built-in Functions * ? Summary INTRODUCTION TO FUNCTIONAL PROGRAMMING WITH R * ? What is Functional Programming (FP)? * ? Terminology: Higher-Order Functions * ? A Short List of Languages that Support FP * ? Functional Programming in R * ? Vector and Matrix Arithmetic * ? Vector Arithmetic Example * ? More Examples of FP in R * ? Summary MANAGING YOUR ENVIRONMENT * ? Getting and Setting the Working Directory * ? Getting the List of Files in a Directory * ? The R Home Directory * ? Executing External R commands * ? Loading External Scripts in RStudio * ? Listing Objects in Workspace * ? Removing Objects in Workspace * ? Saving Your Workspace in R * ? Saving Your Workspace in RStudio * ? Saving Your Workspace in R GUI * ? Loading Your Workspace * ? Diverting Output to a File * ? Batch (Unattended) Processing * ? Controlling Global Options * ? Summary R TYPE SYSTEM AND STRUCTURES * ? The R Data Types * ? System Date and Time * ? Formatting Date and Time * ? Using the mode() Function * ? R Data Structures * ? What is the Type of My Data Structure? * ? Creating Vectors * ? Logical Vectors * ? Character Vectors * ? Factorization * ? Multi-Mode Vectors * ? The Length of the Vector * ? Getting Vector Elements * ? Lists * ? A List with Element Names * ? Extracting List Elements * ? Adding to a List * ? Matrix Data Structure * ? Creating Matrices * ? Creating Matrices with cbind() and rbind() * ? Working with Data Frames * ? Matrices vs Data Frames * ? A Data Frame Sample * ? Creating a Data Frame * ? Accessing Data Cells * ? Getting Info About a Data Frame * ? Selecting Columns in Data Frames * ? Selecting Rows in Data Frames * ? Getting a Subset of a Data Frame * ? Sorting (ordering) Data in Data Frames by Attribute(s) * ? Editing Data Frames * ? The str() Function * ? Type Conversion (Coercion) * ? The summary() Function * ? Checking an Object's Type * ? Summary EXTENDING R * ? The Base R Packages * ? Loading Packages * ? What is the Difference between Package and Library? * ? Extending R * ? The CRAN Web Site * ? Extending R in R GUI * ? Extending R in RStudio * ? Installing and Removing Packages from Command-Line * ? Summary READ-WRITE AND IMPORT-EXPORT OPERATIONS IN R * ? Reading Data from a File into a Vector * ? Example of Reading Data from a File into A Vector * ? Writing Data to a File * ? Example of Writing Data to a File * ? Reading Data into A Data Frame * ? Writing CSV Files * ? Importing Data into R * ? Exporting Data from R * ? Summary STATISTICAL COMPUTING FEATURES IN R * ? Statistical Computing Features * ? Descriptive Statistics * ? Basic Statistical Functions * ? Examples of Using Basic Statistical Functions * ? Non-uniformity of a Probability Distribution * ? Writing Your Own skew and kurtosis Functions * ? Generating Normally Distributed Random Numbers * ? Generating Uniformly Distributed Random Numbers * ? Using the summary() Function * ? Math Functions Used in Data Analysis * ? Examples of Using Math Functions * ? Correlations * ? Correlation Example * ? Testing Correlation Coefficient for Significance * ? The cor.test() Function * ? The cor.test() Example * ? Regression Analysis * ? Types of Regression * ? Simple Linear Regression Model * ? Least-Squares Method (LSM) * ? LSM Assumptions * ? Fitting Linear Regression Models in R * ? Example of Using lm() * ? Confidence Intervals for Model Parameters * ? Example of Using lm() with a Data Frame * ? Regression Models in Excel * ? Multiple Regression Analysis * ? Summary DATA MANIPULATION AND TRANSFORMATION IN R * ? Applying Functions to Matrices and Data Frames * ? The apply() Function * ? Using apply() * ? Using apply() with a User-Defined Function * ? apply() Variants * ? Using tapply() * ? Adding a Column to a Data Frame * ? Dropping A Column in a Data Frame * ? The attach() and detach() Functions * ? Sampling * ? Using sample() for Generating Labels * ? Set Operations * ? Example of Using Set Operations * ? The dplyr Package * ? Object Masking (Shadowing) Considerations * ? Getting More Information on dplyr in RStudio * ? The search() or searchpaths() Functions * ? Handling Large Data Sets in R with the data.table Package * ? The fread() and fwrite() functions from the data.table Package * ? Using the Data Table Structure * ? Summary DATA VISUALIZATION IN R * ? Data Visualization * ? Data Visualization in R * ? The ggplot2 Data Visualization Package * ? Creating Bar Plots in R * ? Creating Horizontal Bar Plots * ? Using barplot() with Matrices * ? Using barplot() with Matrices Example * ? Customizing Plots * ? Histograms in R * ? Building Histograms with hist() * ? Example of using hist() * ? Pie Charts in R * ? Examples of using pie() * ? Generic X-Y Plotting * ? Examples of the plot() function * ? Dot Plots in R * ? Saving Your Work * ? Supported Export Options * ? Plots in RStudio * ? Saving a Plot as an Image * ? Summary USING R EFFICIENTLY * ? Object Memory Allocation Considerations * ? Garbage Collection * ? Finding Out About Loaded Packages * ? Using the conflicts() Function * ? Getting Information About the Object Source Package with the pryr Package * ? Using the where() Function from the pryr Package * ? Timing Your Code * ? Timing Your Code with system.time() * ? Timing Your Code with System.time() * ? Sleeping a Program * ? Handling Large Data Sets in R with the data.table Package * ? Passing System-Level Parameters to R * ? Summary LAB EXERCISES * Lab 1 - Getting Started with R * Lab 2 - Learning the R Type System and Structures * Lab 3 - Read and Write Operations in R * Lab 4 - Data Import and Export in R * Lab 5 - k-Nearest Neighbors Algorithm * Lab 6 - Creating Your Own Statistical Functions * Lab 7 - Simple Linear Regression * Lab 8 - Monte-Carlo Simulation (Method) * Lab 9 - Data Processing with R * Lab 10 - Using R Graphics Package * Lab 11 - Using R Efficiently

A step-by-step guide that walks you through the fundamentals of Python programming followed using Python libraries to create random forest from scratch. A comprehensive course designed for both beginners with some programming experience or even those who know nothing about ML and random forest!

In this course, we will process massive streams of real-time data using Spark Streaming and create Spark applications using the Scala programming language (v2.12). We will also get our hands-on with some real live Twitter data, simulated streams of Apache access logs, and even data used to train machine learning models.

The learner will be able to recognise the skills, knowledge and competence required in order to work with ante and post natal clients in an unsupervised manner. There will be focus on the considerations for safe and effective exercise and how to plan and adapt exercise for these particular clients.

Explore the world of Artificial Intelligence with our comprehensive Foundations Course. From understanding the basics of AI and essential mathematical principles to delving into advanced topics like Deep Learning, Natural Language Processing, and Robotics – this course equips you with the knowledge and skills needed to navigate the dynamic landscape of AI. Whether you're a student, professional, or enthusiast, join us on a journey to build a solid foundation in AI and develop practical applications that shape the future. Enroll now and empower yourself to contribute to the exciting field of Artificial Intelligence.