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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

Machine Learning Basics For Data Science Enthusiasts

Pranshu Sharma
Pranshu Sharma Last Updated : 19 Apr, 2021
6 min read
This article was published as a part of the Data Science Blogathon.

Overview

  • Introduction to Machine Learning Basics
  • Need of Machine Learning In Modern World
  • Various Concepts Related To Machine Learning
  • Resources to Learn Machine Learning
  • Scope Of Machine Learning

Machine learning is an emerging technology that enables computers to learn automatically from past historical data.

Machine learning deploys various mathematical algorithms for building models and giving output as predictions.

It is being applied in various fields such as image recognition, speech recognition, email filtering, Artificial Intelligence,  Netflix recommender system, Self-Driven Cars, and many more

Machine Learning basics

 

Table of Contents

1 What Is Machine Learning

2 How Does Machine Learning works

3 Need For Machine Learning

4 Key Features of Machine Learning

5 Types Of Machine Learning

6 Scope Of Machine Learning

7 Applications in Real World

8 Prerequisites For Machine Learning

9 Resources

1. What Is Machine Learning

 

What Is Machine Learning basics

Machine Learning is all about enabling the machine to learn from data automatically and improvise on previous experiences and predicting things without being given instruction or explicitly programmed to do so.

Making Machines behave like humans is what Machine Learning signifies. We, as humans, are bound to learn from our experiences and make decisions based on our learning, but on the other hand, Machines are always being given instructions to work. Machine Learning Aims at Such ways to make Machine learn from previous data like a human and make accurate, effective predictions

2. How does Machine Learning works

Machine Learning works in the following manner-

  1. A machine is being given traditional data as an Input
  2. Machine Learns from the input data being given
  3. Machine Builds the Prediction Model
  4. Machine trains the model with the input data
  5. After training, the Machine tests the model by predicting the output for the next set of input data
  6. Machine evaluates the accuracy percentage of prediction and improvises itself if the percentage is below the desired number
  7. Machine Improve the Accuracy and learn from previous data /experiences
How does Machine Learning basics works

3. Need For Machine Learning

We, as humans, have limitations and restrictions. With the world being data-driven, It’s hard for a human to process such an amount of data manually. This is where Machine learning is applied as machines are capable of solving such complex problems.

Machine Learning algorithms are being trained upon such huge amounts of data and are being used for making effective predictions and outputs, thus making it easy for a human to handle such complexities of data.

More is the amount of Data, More effective is the Machine Learning performance, thus the need for Machine Learning becomes necessary in the modern era for solving various complex problems.


4. Key Features Of Machine Learning

  1. Solving Various Complex Problems, which are beyond human capabilities to interpret and solve
  2. Identifying Various Pattern in the Data, Thus extracting useful insights to the various problems
  3. Making Effective predictions
  4. Improvise or learn from all the past data, thus enhancing the output accuracy
  5. Comes Under Data-Driven Technology

5. Types Of Machine Learning

Machine Learning can be broadly classified into 3 types namely

a. Supervised learning

b. Unsupervised learning

c. Reinforcement learning

a. Supervised Learning

Supervised learning is a type of machine learning technique in which we train the machine by providing Labeled data(data being tagged by one or more labels/data being categorized) and on that basis, the machine predicts output.

The Machine is being trained with the labeled data under our supervision, just like Teacher trains his/her student, and

The goal of supervised learning is to map the input data with the output data. Once the Model gets trained, it gives prediction as its output for the datasets

It’s of two types:

1 Regression:  Used to Predict continuous values like price, salary, etc.

2 Classification:  Used to Classify discrete values like True/False, Male/Female, etc.

Supervised Learning machine learning basics

b. Unsupervised learning

Unsupervised Learning is a learning method in which A machine is being trained without any sort of supervision.

In this, the Machine is being trained on data that is not being labeled, categorized and learning is done without any supervision. In this, Machine tries to find a useful pattern, insights from the huge amount of data and restructures/ segregate the data on basis of feature or similarities

It’s of two types:

  1. Clustering: Splits the data into various groups based on similarities.
  2. Association:  identifies  the sets of items/ data points that often occur together in the  dataset

 

Unsupervised learning

c. Reinforcement Learning

Reinforcement learning is a learning method, in which a Machine gets reward points for each right prediction and gets a negative point/penalty for each wrong prediction. The Machine learns automatically with these feedback points and improves its accuracy.

In reinforcement learning, the machine explores the environment it interacts with. The main goal is to get the most reward points, and improving its accuracy

Reinforcement Learning

6. Scope Of Machine Learning

There’s a brilliant Scope Of Machine Learning In the Modern World, Being Data-Driven, including various Sectors like Finance, Business, etc. With The Data being produced at a massive rate, various job roles constitute Machine learning as an important skill. Those Job roles are

1. Machine Learning Engineer – They are professionals who develop the systems and various machine learning algorithms that learn, train on the input data being given, and give various predictions, output, and improvise on the previous data, experiences.

2. Deep Learning Engineer – They are professionals that specialize in advanced Machine Learning Concepts, deep learning, to develop various models, software related to artificial intelligence. Their main goal is to be able to make the machine behave, mimic, think, make decisions like humans.

3. Data Scientist – These are the professionals who extract meaningful insights from data and analyze those insights and giving respective business solutions based on those hidden patterns.

4. Computer Vision Engineer – They are software developers who design such algorithms for recognizing various patterns in images.

With Various Industries investing billions of dollars in the R&D of Artificial Intelligence, which is an application of Machine Learning, The demand and need for exploration in the field of Machine Learning will always be there.

Reinforcement Learning

7. Applications In Real World

There are many Real-world Applications Of Machine Learning, such as

1 Self-driving car,

2 Amazon Alexa Voice Assistant

3 Apple Siri Voice Assistant,

4 Netflix Recommender system

5 Image Recognition

6 Speech Recognition

7 Google Translator

8 Fraud Detection and many more.

Machine learning models can be deployed for various prediction models, including Weather prediction Model, Disease prediction Model, Stock Market Analysis, Car Price Prediction, Real Estate Prediction, etc.

 

Applications In Real World

                                                            Various Voice Assistants

 

Prerequisites

 


8. Prerequisites For Machine Learning

  1. Fundamental knowledge Of Probability & Linear algebra.
  2. Familiarity with coding in computer language, especially in Python.
  3. Fundamental Knowledge of Calculus
  4. Well Acquaintances with Statistics

9. Resources

MOOCs: Udacity Python Course, Coursera Python Course

YouTube Channel: Krish Naik, Code Basics

Blogs: Analytics Vidhya, KD Nuggets

With this I finish this blog.
Hello Everyone, Namaste
My name is Pranshu Sharma and I am a Data Science Enthusiast
Thank you so much for taking your precious time to read this blog. Feel free to point out any mistake(I’m a learner after all) and provide respective feedback or leave a comment.
Dhanyvaad!!
Feedback:
Email: [email protected]

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

blogathonmachine learningMachine Learning basics
Pranshu Sharma
Pranshu Sharma

Aspiring Data Scientist | M.TECH, CSE at NIT DURGAPUR

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Responses From Readers

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Amazing article buddy, would highly recommend this to all my friends who are starting out in this field to give this a read. Machine learning is a form of artificial intelligence (AI) that teaches computers to think in a similar way to how humans do: Learning and improving upon past experiences. It works by exploring data and identifying patterns, and involves minimal human intervention.

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