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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

Top 7 Cloud Computing Prerequisites to Learn in 2022

M
Meetu_Arora Last Updated : 06 Sep, 2022
4 min read

Introduction

If I have to place the finger at any lucrative and promising domains ruling the trending job market at the moment, it has to be Cloud Computing. The scope of cloud computing is only moving faster, strength by strength, and has a brighter future ahead for everyone. There is a surging need for medium and large-scale enterprises to move to the cloud after being impressed with its particular importance. It has paved the way for these enterprises to integrate cloud solutions into their business operations and created an influx of cloud computing job opportunities.

The average salary for an Indian cloud computing professional can even touch 10 million a year. This number alone establishes that students and professionals consider cloud computing one of the hottest and in-demand job domains.

However, getting into cloud computing takes a detailed understanding of some components. I have listed some of the top cloud computing prerequisites for anyone looking to kick-start their cloud computing learning journey. Read to the bottom to unleash the prerequisites of cloud computing and set your professional foot on the right note.

cloud computing prerequisites

Source: DotNetTricks

Prerequisites of Cloud Computing

Since cloud computing is a subject that deals with a couple of technology-rich topics, anyone planning to study cloud computing needs to have a close brush-up with such topics. Here are the cloud computing prerequisites to learn for aspiring cloud computing professionals:

 

  1. Basic Programming Skills

Since a cloud computing professional is required to work closely with several programming languages, it becomes imperative for the incumbent to have solid programming skills. There are diverse programming languages that cloud computing professionals use. Some of these include Java, Python, PHP, and .NET.

While each programming language has distinct features and advantages, the incumbent should have a basic familiarity with each. For instance, Python has multiple libraries to work with to simplify and make it easier to manoeuvring specific programming tasks. While on the other side, PHP is a crucial programming language for developing cloud computing applications.

Besides, the knowledge of creating a program and the required coding skills will also come in handy while learning to be a cloud computing professional. Starting with Python is easier for beginners because it has a simplified syntax and requires no prior coding experience.

 

  1. Understanding of Databases

Databases are the key to easily storing, accessing and retrieving critical and large volumes of information. Cloud computing professionals create and manage databases run and managed on the cloud for easier manoeuvring of data and information from anywhere. Understanding the way databases work and their functions is a primary cloud computing prerequisite.

Cloud-managed databases have secured a special place and are increasingly favoured by organizations as they don’t require the necessary hardware. Running a cloud-based database is also cost-efficient for organizations without spending on the necessary hardware. Besides learning database basics, having an overview of related technologies like SQL, Apache Hadoop, MySQL, and MongoDB becomes essential.

If you’re a newbie to the world of programming languages, I recommend learning the fundamentals of SQL.

 

  1. Security & Privacy Basics

With digital and technological advancement growing daily, the need for robust organizational digital security and privacy is palpable. Hacking incidents are a significant concern for businesses as, on average, one person is hacked every 39 seconds. Organizations are more alarmed than ever to secure their cloud-stored data and cloud-run applications from hackers and other maligned cyber-attacks.

Understanding cyber security and its fundamentals are crucial for all cloud computing aspirants. Learn the best principles of avoiding and mitigating a cyber-security threat as one of the cloud computing prerequisites. Focus on encryption and cryptography to start with and move on to learning cybersecurity in detail.

 

  1. Understanding Agile Development

A software development methodology, Agile Development is focused on product development through cross-functional team collaboration and their users. This methodology encapsulates releasing the product after reaching a definite stage of development and refining it periodically based on user feedback.

Agile Development relies heavily on user feedback as they are the ones who can point out the product’s weak areas after substantial use. The best example of agile development is mobile apps, which are released for consumption and constantly updated to enhance user experience.

To be a cloud computing professional, you should learn the principles, terminology, and implementation of agile development.

 

  1. Understanding of Operating Systems

Another cloud computing prerequisite is understanding operating systems like Windows, macOS, and Linux. Linux here takes precedence over others as it is a widely used operating system by organizations for implementing cloud-based solutions.

This is one of the most simple cloud computing prerequisites, as learning to operate an operating system is quite basic. You can easily learn and master it if you’re a PC or similar device at home.

 

  1. Knowledge of Virtualization

Virtualization is the process of creating virtual devices, servers, and infrastructures on a single computing device. Creating a software-based virtual representation of any device, like virtual storage, application, or network, comes under this process.

It is an immensely beneficial proposition for companies as they can go on multiplying the available software devices without having to increase the hardware.

As a cloud computing prerequisite, it’s helpful to learn the basics of operating systems before learning visualization. Knowing how to create a virtual machine on your computer and multiple VMs is handy while learning visualization.

 

  1. Networking Basics

After virtualization, the next step in cloud computing prerequisites is getting the hang of networking. Networking in cloud computing stands for hosting all network devices from the cloud. The process includes virtual firewalls, network management software, bandwidth, virtual routers, etc.

To begin with, a cloud computing aspirant should understand the IP addresses and their working mechanism. To delve deeper into learning the advanced topics of networking, it is vital to understand the concept of IP addresses.

 

Conclusion

The field of cloud computing is getting bigger by the day, and it is an excellent time to start certification in cloud computing. Organizations are moving to the cloud due to a massive uproar in the domain. While learning cloud computing can get daunting without prior exposure, familiarity with the above prerequisites helps understand cloud computing faster.

Want to learn a computer vision course? Check it out here.

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