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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Best Online Microsoft Azure Certification

Aayush Tyagi
Aayush Tyagi Last Updated : 22 Apr, 2024
4 min read

Introduction

Looking to boost your career in cloud computing? Microsoft Azure certifications are the way to go! In today’s digital era, businesses are rapidly adopting cloud solutions, and Azure is at the forefront of this transformation. Whether you’re a seasoned IT professional or just starting out, getting certified in Azure can open up a world of opportunities. In this article, we’ll explore the best online Azure certification and how it can benefit your career.

Best Online Microsoft Azure Certification

Table of contents

What is Microsoft Azure?

Microsoft Azure is a cloud computing platform that allows users to build, deploy, and manage applications and services through Microsoft-managed data centers. It provides a wide array of cloud services, including computing, storage, databases, networking, and more, enabling businesses to innovate and scale efficiently.

Key Features and Services Offered by Azure

  • Compute Services: Azure offers virtual machines, containers, and serverless computing options for flexible and scalable compute solutions.
  • Storage Solutions: Azure provides Blob Storage, File Storage, and Queue Storage for secure and durable cloud storage.
  • Networking Services: Azure offers Virtual Network (VNet), Azure Load Balancer, and Azure DNS for building secure and scalable networks.
  • Database Services: Azure includes Azure SQL Database, Cosmos DB, and Azure Database for PostgreSQL and MySQL for managed database solutions.
  • AI and Machine Learning: Azure Cognitive Services and Azure Machine Learning empower developers with pre-built AI capabilities and tools for building, training, and deploying ML models.

Also Read: Understanding the Basics of Cloud Computing

Advantages of Using Azure for Businesses and Developers

The adoption of Azure brings numerous benefits to businesses and developers alike. These include scalability, flexibility, cost-effectiveness, security, and seamless integration with existing Microsoft technologies. Azure’s global presence and extensive network of data centers ensure high availability and reliability, making it a preferred choice for businesses of all sizes.

Best Online Microsoft Azure Certification by Analytics Vidhya

Microsoft Azure Certification

Now, let’s dive into the heart of our discussion: the free course on Microsoft Azure. This course is designed to provide beginners with a solid foundation in Azure fundamentals, equipping them with the knowledge and skills needed to embark on their cloud journey confidently.

Overview of the Course Structure

The Azure Fundamentals course is structured to provide learners with a step-by-step introduction to Azure concepts and services. Through a series of modules, participants will learn about core Azure services, security, compliance, pricing, and support options, gaining a comprehensive understanding of the Azure ecosystem.

Target Audience for the Course

This course is designed for beginners who are new to cloud computing or those looking to expand their knowledge of Microsoft Azure. Whether you’re an IT professional, developer, student, or business owner, this course is tailored to equip you with the essential skills needed to navigate the Azure platform confidently.

Prerequisites for taking the Course

To enroll in the Azure Fundamentals course, no prior experience with Azure or cloud computing is required. However, a basic understanding of IT concepts and familiarity with the internet and web browsers will be beneficial. Additionally, having access to a computer with internet connectivity is essential for completing the course exercises and labs.

Explore our Microsoft Azure course here.

Course Curriculum

The course curriculum you’ve provided seems to be structured to provide a comprehensive understanding of Microsoft Azure, a cloud computing platform. Here’s a breakdown of each section:

Module 1. Introduction to the Course

  • Provides prerequisites for the course.
  • Offers course handouts.
  • Introduces the course content.

Module 2. Fundamentals of Cloud Computing

  • Covers the basics of cloud computing.
  • Explains cloud deployment models and service models.
  • Includes a quiz to assess understanding.

Module 3. Introduction to Microsoft Azure

  • Introduces Microsoft Azure.
  • Outlines a learning path.
  • Discusses Azure regions, availability zones, and account creation.
  • Includes a quiz.

Module 4. Azure Compute Options

  • Explores Azure compute options, including virtual machines.
  • Discusses security services and pricing options.
  • Includes demonstrations and quizzes.

Module 5. Azure Networking Options

  • Covers networking fundamentals in Azure.
  • Explains virtual networks, VPN gateways, and peering options.
  • Includes a quiz.

Module 6. Azure Security Options

  • Discusses security in Azure, including the shared responsibility model.
  • Covers Azure Active Directory and role-based access control.
  • Includes a quiz.

Module 7. Azure Storage Options

  • Covers various types of cloud storage in Azure.
  • Demonstrates creating storage accounts and using different storage options like blob storage and queues.
  • Includes a quiz.

Module 8. Azure Databases

  • Explores Azure database services, including relational databases and Cosmos DB.
  • Includes a quiz.

Module 9. Azure Functions

  • Introduces Azure Functions and triggers.
  • Discusses input and output bindings.
  • Includes a quiz.

Module 10. Management

  • Covers Azure Resource Manager and Azure Monitor for managing Azure resources.
  • Includes a quiz.

Module 11. Azure Pricing:

  • Discusses Azure pricing models and support options.
  • Introduces the pricing calculator and best practices for cost optimization.

The curriculum seems to provide a structured learning path covering essential aspects of Microsoft Azure, including its fundamentals, compute, networking, security, storage, databases, functions, management, and pricing. Each section includes quizzes or demonstrations to reinforce learning and assess understanding.

Conclusion

Exploring Microsoft Azure offers boundless opportunities for businesses and developers. Microsoft Azure Certification by Analytics Vidhya serves as an excellent starting point for beginners, covering concepts without any prior experience.

Explore the program today!

Microsoft Azure CourseOnline Azure courseOnline Microsoft Azure Certification
Aayush Tyagi
Aayush Tyagi

Data Analyst with over 2 years of experience in leveraging data insights to drive informed decisions. Passionate about solving complex problems and exploring new trends in analytics. When not diving deep into data, I enjoy playing chess, singing, and writing shayari.

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Free Access to Fundamentals of Microsoft Azure Course
Free Access to Fundamentals of Microsoft Azure Course
15 Azure Interview Questions for Beginners
15 Azure Interview Questions for Beginners

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