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This course starts with the basics of data science and gradually moves towards explaining the concepts of machine learning and various data science algorithms.

Gain the solid skills and knowledge to kickstart a successful career and learn from the experts with this

Duration 1 Days 6 CPD hours This course is intended for This course is intended for individuals who want to become more rational and disciplined thinkers. Overview Upon successful completion of this course, students will boost their performance with an increase in their decision-making ability. In this course, students will learn develop their ability to analyze and evaluate information. 1 - GETTING STARTED Icebreaker Housekeeping Items The Parking Lot Workshop Objectives 2 - COMPONENTS OF CRITICAL THINKING Applying Reason Open Mindedness Analysis Logic Case Study 3 - NON-LINEAR THINKING Step Out of Your Comfort Zone Don't Jump to Conclusions Expect and Initiate Change Being Ready to Adapt Case Study 4 - LOGICAL THINKING Ask the Right Questions Organize the Data Evaluate the Information Draw Conclusions Case Study 5 - CRITICAL THINKERS (I) Active Listening Be Curious Be Disciplined Be Humble Case Study 6 - CRITICAL THINKERS (II) Seeing the Big Picture Objectivity Using Your Emotions Being Self-Aware Case Study 7 - EVALUATE INFORMATION Making Assumptions Watch out for Bias Ask Clarifying Questions SWOT Analysis Case Study 8 - BENEFITS OF CRITICAL THINKING Being More Persuasive Better Communication Better Problem Solving Increased Emotional Intelligence Case Study 9 - CHANGING YOUR PERSPECTIVE Limitations of Your Point of View Considering Others Viewpoint Influences on Bias When New Information Arrives Case Study 10 - PROBLEM SOLVING Identify Inconsistencies Trust Your Instincts Asking Why? Evaluate the Solution(s) Case Study 11 - PUTTING IT ALL TOGETHER Retaining Your New Skills Reflect and Learn From Mistakes Always Ask Questions Practicing Critical Thinking Case Study 12 - WRAPPING UP Words from the Wise Review of Parking Lot Lessons Learned Completion of Action Plans and Evaluations

You will learn Python-based deep learning and machine learning techniques through this course. With numerous real-world case studies, we will go over all the mathematics needed to master deep learning algorithms. We will study Backpropagation, Feed Forward Network, Artificial Neural Networks, CNN, RNN, Transfer Learning, and more.

Welcome to our hands-on video course, where you will learn technologies, such as React, Redux Toolkit, Express, and MongoDB. You will learn how to structure your code using Redux Toolkit, implement powerful features with React, and create a robust backend using Express and MongoDB. An understanding of modern JS fundamentals and the basics of React will be an add-on.

Duration 4 Days 24 CPD hours This course is intended for The primary audience for this course is as follows: Technical professionals who are responsible for installation, deployment, and maintenance of the Cisco ONS 15454 MSTP network Network operations, planners, and designers Network operations engineers Overview Upon completing this course, the learner will be able to meet these overall objectives: Connect to a Cisco ONS 15454 MSTP chassis using Cisco Transport Controller (CTC) Identify Node configurations according to card population Provision DWDM circuits using the Cisco Transport Controller (CTC) Conduct performance monitoring, alarm verification, and fault isolation Provision M12 WSS in linear and M6 SMR nodes in ring topologies Configuration options for the any rate muxponder and crossponder Perform Raman amplifier initialization Isolate optical network issues In this course, you will learn the skills necessary to deploy a Cisco Network Convergence System (NCS) 2000 Series network. You will also learn how to perform node turnup. The course covers three shelf types, the Cisco ONS 15454 M12 Multiservice Transport Platform (MSTP), NCS 2006, and NCS 2002. You will learn how to deploy linear and ring dense-wavelength-division-multiplexing (DWDM) topologies. The course covers multiplexer-demultiplexer cards, Erbium-doped-fiber amplifier cards, Raman amplifiers, transponder cards, and the newest Cisco Any Rate muxponder cards and crossponder cards. These cards are used in terminal, amplifier, and reconfigurable optical add-drop multiplexer (ROADM) node configurations. You configure wavelength-selective switch (WSS) linear and single-module ROADM (SMR) rings. This Optical Technical Training Intermediate course covers 10-gigabit unprotected circuits and 10-gigabit protection using Y-cable, optical channel transport unit-2 (OTU-2), and protection switch module (PSM) cards. Learn more about this NCS 2000 training course below. Course Outline Lesson 1: CTC Operations Lesson 2: MSTP Topologies Lesson 3: Shelf and Card Installation Lesson 4: Fiber jumper installation Lesson 5: Linear Configurations Lesson 6: Node Turn-Up Lesson 7: Optical Channel Network Connection Circuits Lesson 8: Transponder and Optical Channel Client Connection Circuits Lesson 9: Multishelf Lesson 10: MSTP M6 SMR-Based Rings Lesson 11: 10-Gigabit Muxponder and Transponder Cards Lesson 12: 10-Gigabit with Y-Cable Protection Lesson 13: Alternative 10-GB Protection (PSM and OTU-2) Lesson 14: Any Rate Muxponder and Crossponder Lesson 15: Raman Amplifier Lesson 16: 40- and 100-Gigabit Transponder and Muxponder Lesson 17: Troubleshooting Additional course details: Nexus Humans Cisco Optical Technology Intermediate (OPT200) 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 Optical Technology Intermediate (OPT200) 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.

Overview This comprehensive course on Statistics & Probability for Data Science & Machine Learning will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Statistics & Probability for Data Science & Machine Learning comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Statistics & Probability for Data Science & Machine Learning. It is available to all students, of all academic backgrounds. Requirements Our Statistics & Probability for Data Science & Machine Learning is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 10 sections • 89 lectures • 11:27:00 total length •Welcome!: 00:02:00 •What will you learn in this course?: 00:06:00 •How can you get the most out of it?: 00:06:00 •Intro: 00:03:00 •Mean: 00:06:00 •Median: 00:05:00 •Mode: 00:04:00 •Mean or Median?: 00:08:00 •Skewness: 00:08:00 •Practice: Skewness: 00:01:00 •Solution: Skewness: 00:03:00 •Range & IQR: 00:10:00 •Sample vs. Population: 00:05:00 •Variance & Standard deviation: 00:11:00 •Impact of Scaling & Shifting: 00:19:00 •Statistical moments: 00:06:00 •What is a distribution?: 00:10:00 •Normal distribution: 00:09:00 •Z-Scores: 00:13:00 •Practice: Normal distribution: 00:04:00 •Solution: Normal distribution: 00:07:00 •Intro: 00:01:00 •Probability Basics: 00:10:00 •Calculating simple Probabilities: 00:05:00 •Practice: Simple Probabilities: 00:01:00 •Quick solution: Simple Probabilities: 00:01:00 •Detailed solution: Simple Probabilities: 00:06:00 •Rule of addition: 00:13:00 •Practice: Rule of addition: 00:02:00 •Quick solution: Rule of addition: 00:01:00 •Detailed solution: Rule of addition: 00:07:00 •Rule of multiplication: 00:11:00 •Practice: Rule of multiplication: 00:01:00 •Solution: Rule of multiplication: 00:03:00 •Bayes Theorem: 00:10:00 •Bayes Theorem - Practical example: 00:07:00 •Expected value: 00:11:00 •Practice: Expected value: 00:01:00 •Solution: Expected value: 00:03:00 •Law of Large Numbers: 00:08:00 •Central Limit Theorem - Theory: 00:10:00 •Central Limit Theorem - Intuition: 00:08:00 •Central Limit Theorem - Challenge: 00:11:00 •Central Limit Theorem - Exercise: 00:02:00 •Central Limit Theorem - Solution: 00:14:00 •Binomial distribution: 00:16:00 •Poisson distribution: 00:17:00 •Real life problems: 00:15:00 •Intro: 00:01:00 •What is a hypothesis?: 00:19:00 •Significance level and p-value: 00:06:00 •Type I and Type II errors: 00:05:00 •Confidence intervals and margin of error: 00:15:00 •Excursion: Calculating sample size & power: 00:11:00 •Performing the hypothesis test: 00:20:00 •Practice: Hypothesis test: 00:01:00 •Solution: Hypothesis test: 00:06:00 •T-test and t-distribution: 00:13:00 •Proportion testing: 00:10:00 •Important p-z pairs: 00:08:00 •Intro: 00:02:00 •Linear Regression: 00:11:00 •Correlation coefficient: 00:10:00 •Practice: Correlation: 00:02:00 •Solution: Correlation: 00:08:00 •Practice: Linear Regression: 00:01:00 •Solution: Linear Regression: 00:07:00 •Residual, MSE & MAE: 00:08:00 •Practice: MSE & MAE: 00:01:00 •Solution: MSE & MAE: 00:03:00 •Coefficient of determination: 00:12:00 •Root Mean Square Error: 00:06:00 •Practice: RMSE: 00:01:00 •Solution: RMSE: 00:02:00 •Multiple Linear Regression: 00:16:00 •Overfitting: 00:05:00 •Polynomial Regression: 00:13:00 •Logistic Regression: 00:09:00 •Decision Trees: 00:21:00 •Regression Trees: 00:14:00 •Random Forests: 00:13:00 •Dealing with missing data: 00:10:00 •ANOVA - Basics & Assumptions: 00:06:00 •One-way ANOVA: 00:12:00 •F-Distribution: 00:10:00 •Two-way ANOVA - Sum of Squares: 00:16:00 •Two-way ANOVA - F-ratio & conclusions: 00:11:00 •Wrap up: 00:01:00 •Assignment - Statistics & Probability for Data Science & Machine Learning: 00:00:00

Overview This comprehensive course on Python for Data Analysis will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Python for Data Analysis comes with accredited certification, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is this course for? There is no experience or previous qualifications required for enrolment on this Python for Data Analysis. It is available to all students, of all academic backgrounds. Requirements Our Python for Data Analysis is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career path Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 19 sections • 99 lectures • 00:08:00 total length •Welcome & Course Overview: 00:07:00 •Set-up the Environment for the Course (lecture 1): 00:09:00 •Set-up the Environment for the Course (lecture 2): 00:25:00 •Two other options to setup environment: 00:04:00 •Python data types Part 1: 00:21:00 •Python Data Types Part 2: 00:15:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1): 00:16:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2): 00:20:00 •Python Essentials Exercises Overview: 00:02:00 •Python Essentials Exercises Solutions: 00:22:00 •What is Numpy? A brief introduction and installation instructions.: 00:03:00 •NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.: 00:28:00 •NumPy Essentials - Indexing, slicing, broadcasting & boolean masking: 00:26:00 •NumPy Essentials - Arithmetic Operations & Universal Functions: 00:07:00 •NumPy Essentials Exercises Overview: 00:02:00 •NumPy Essentials Exercises Solutions: 00:25:00 •What is pandas? A brief introduction and installation instructions.: 00:02:00 •Pandas Introduction: 00:02:00 •Pandas Essentials - Pandas Data Structures - Series: 00:20:00 •Pandas Essentials - Pandas Data Structures - DataFrame: 00:30:00 •Pandas Essentials - Handling Missing Data: 00:12:00 •Pandas Essentials - Data Wrangling - Combining, merging, joining: 00:20:00 •Pandas Essentials - Groupby: 00:10:00 •Pandas Essentials - Useful Methods and Operations: 00:26:00 •Pandas Essentials - Project 1 (Overview) Customer Purchases Data: 00:08:00 •Pandas Essentials - Project 1 (Solutions) Customer Purchases Data: 00:31:00 •Pandas Essentials - Project 2 (Overview) Chicago Payroll Data: 00:04:00 •Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data: 00:18:00 •Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach: 00:13:00 •Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials - Exercises Overview: 00:06:00 •Matplotlib Essentials - Exercises Solutions: 00:21:00 •Seaborn - Introduction & Installation: 00:04:00 •Seaborn - Distribution Plots: 00:25:00 •Seaborn - Categorical Plots (Part 1): 00:21:00 •Seaborn - Categorical Plots (Part 2): 00:16:00 •Seborn-Axis Grids: 00:25:00 •Seaborn - Matrix Plots: 00:13:00 •Seaborn - Regression Plots: 00:11:00 •Seaborn - Controlling Figure Aesthetics: 00:10:00 •Seaborn - Exercises Overview: 00:04:00 •Seaborn - Exercise Solutions: 00:19:00 •Pandas Built-in Data Visualization: 00:34:00 •Pandas Data Visualization Exercises Overview: 00:03:00 •Panda Data Visualization Exercises Solutions: 00:13:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1): 00:19:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2): 00:14:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview): 00:11:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions): 00:37:00 •Project 1 - Oil vs Banks Stock Price during recession (Overview): 00:15:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3): 00:17:00 •Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview): 00:03:00 •Introduction to ML - What, Why and Types..: 00:15:00 •Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff: 00:15:00 •scikit-learn - Linear Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Linear Regression Model Hands-on (Part 2): 00:19:00 •Good to know! How to save and load your trained Machine Learning Model!: 00:01:00 •scikit-learn - Linear Regression Model (Insurance Data Project Overview): 00:08:00 •scikit-learn - Linear Regression Model (Insurance Data Project Solutions): 00:30:00 •Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc.: 00:10:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 2): 00:20:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 3): 00:11:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Overview): 00:05:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Solutions): 00:15:00 •Theory: K Nearest Neighbors, Curse of dimensionality .: 00:08:00 •scikit-learn - K Nearest Neighbors - Hands-on: 00:25:00 •scikt-learn - K Nearest Neighbors (Project Overview): 00:04:00 •scikit-learn - K Nearest Neighbors (Project Solutions): 00:14:00 •Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging.: 00:18:00 •scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1): 00:19:00 •scikit-learn - Decision Tree and Random Forests (Project Overview): 00:05:00 •scikit-learn - Decision Tree and Random Forests (Project Solutions): 00:15:00 •Support Vector Machines (SVMs) - (Theory Lecture): 00:07:00 •scikit-learn - Support Vector Machines - Hands-on (SVMs): 00:30:00 •scikit-learn - Support Vector Machines (Project 1 Overview): 00:07:00 •scikit-learn - Support Vector Machines (Project 1 Solutions): 00:20:00 •scikit-learn - Support Vector Machines (Optional Project 2 - Overview): 00:02:00 •Theory: K Means Clustering, Elbow method ..: 00:11:00 •scikit-learn - K Means Clustering - Hands-on: 00:23:00 •scikit-learn - K Means Clustering (Project Overview): 00:07:00 •scikit-learn - K Means Clustering (Project Solutions): 00:22:00 •Theory: Principal Component Analysis (PCA): 00:09:00 •scikit-learn - Principal Component Analysis (PCA) - Hands-on: 00:22:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Overview): 00:02:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Solutions): 00:17:00 •Theory: Recommender Systems their Types and Importance: 00:06:00 •Python for Recommender Systems - Hands-on (Part 1): 00:18:00 •Python for Recommender Systems - - Hands-on (Part 2): 00:19:00 •Natural Language Processing (NLP) - (Theory Lecture): 00:13:00 •NLTK - NLP-Challenges, Data Sources, Data Processing ..: 00:13:00 •NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing: 00:19:00 •NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW.: 00:19:00 •NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes : 00:13:00 •NLTK - NLP - Pipeline feature to assemble several steps for cross-validation: 00:09:00 •Resources- Python for Data Analysis: 00:00:00

This course will help you to gain a mastery level understanding of the fundamentals of Android Studio, Android app development, and the Kotlin programming language by building six full-fledged applications as well as many more 'learning' applications throughout the course.

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