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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

Career in Machine Learning and Data Science

d
dossier Last Updated : 30 Mar, 2021
4 min read
This article was published as a part of the Data Science Blogathon.

Introduction

 

machine learning career
image source-dreamstime.com

Machine Learning and Data Science are one of the advanced and surging technologies to learn. We all are familiar with the names, maybe not with the technologies of machine learning and data science. Before going into this blog, let me tell you why you should choose Machine Learning and Data Science as a career. There is a copious number of factors that makes you choose, that is:

  • Demand

  • Career growth, job prospects, and salaries

  • Experience factor

  • Ease of job hunting due to lack of competition

  • Work options

  • Various training programs and skill upgrade options available

Taking into consideration all the factors, we will make a decision. Choosing a career path is like choosing a life partner, which we work, excel, and explore it till the last breath.

Let’s get into the topic, the ideal objective of machine learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observation and real-world actions. Machine Learning is one of the integral parts of Artificial Intelligence.

Coming to Data Science, as the world is connected with the internet, every day a huge volume of data is generated. All the data are processed through Data Science. Data Science is a blend of various tools, algorithms, and machine learning principles to discover hidden patterns of raw data. Artificial Intelligence is applied based on Machine Learning and Machine Learning is a part of Data Science. Data Science merges a bunch of algorithms obtained from machine learning to develop a solution.

Machine Learning Process

image source-edureka.com

 

Prerequisites:

The Technical knowledge that is required to become a Machine Learning and Data Science specialist are:

  1. Curiosity to learn and explore

  1. Statistics and Probability

  1. Programming knowledge- Python, R, SQL, Hadoop

  1. Data manipulation and Analysis

  1. Data visualization

  1. Structured thinking

  1. Model deployment

The global Machine Learning market is expected to reach $8.8 Billion by 2022 with a growth rate of 43.6% from 2017 to 2022. The usage of Machine learning will be ubiquitous and it is swanky when it is compared with all other technologies.

Some of the roles in Machine Learning and Data Science are:

  1. Data Scientist

  1. Machine Learning Engineer

  1. Statistician

  1. Data Architect

  1. Business Intelligence Developer

  1. Enterprise Architect

  1. Big Data Engineer/ Data Engineer

 

Machine Learning Career Roles
image source-edureka.com

 

Many Industrial Sectors rely on Machine Learning. Some of the major sectors are:

  • Transportation

Machine Learning and Artificial Intelligence are widely used to predict, monitor, manage traffic and mainly employed in self-driving cars to drive accurately by the algorithms, they allow a car to collect data on its surroundings from cameras and other sensors, interpret it, and decide what actions to take. Machine learning even allows cars to learn how to perform these tasks as well as humans.

  •  Health Care

Use of Artificial Intelligence Technology to the next level with advanced processing and understand life science. A patient can be diagnosed in possible ways that are suggested by AI. AI-driven diagnostics collect patient data and suggest possible ways to curate the disease with no effects.

  • Agriculture

These technologies are used to revolutionize farming all over the world to yield better results by helping farmers to optimize sowing timings, seed treatment, NPK regulation according to soil capability.

  • Finance

As the services of Internet Banking and Online trades are processing over the Internet, there will be such incidents of cyber-attacks. To not give any chances to malicious activities, cybersecurity, and Artificial Intelligence are used to provide security and for prediction and analyzing the data from collecting to managing, Transactions to Investments. AI suggests the best methods to take strategic actions.

  • Retail and Customer Service

With these technologies, the world of services is using Chat-Bots and Digital Assistance to assist and for queries related to complaints and suggestions. Artificial intelligence and Machine Learning provide the scope to predict the need for the product and analyze the conditions and requirements of the customer based on data to get a spike in Business.

To excel in any career path, one should have agog Interest, grit, and gumption.

This is the overview of CAREERS IN MACHINE LEARNING AND DATA SCIENCE. In the present scenario, Machine Learning and Data Science are always growing technologies that never fade out. There are many web resources and videos to learn that are open and free for all.

Hope you are now baffle-free after reading this blog.

About Author:

My name is M.Surya Pavan, pursuing my B.Tech final year in Sagi Rama Krishnam Raju Engineering College-Bhimavaram. A good learner and enthusiast in learning new things.

Linkedin profile: https://www.linkedin.com/in/surya-pavan-480164186

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

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dossier

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How Can You Build a Career in Data Science and Machine Learning?
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