Machine Learning Complete Roadmap 2023 [UPDATED] | Machine Learning Roadmap 2023 - Codexashish


Machine Learning Roadmap 2023

Machine Learning Complete Roadmap 2023

In this tutorial, we will see a complete roadmap of Machine Learning from basic to advance. Machine Learning Roadmap 2023.

    What is Machine Learning?

    Machine Learning was firstly introduced by Arthur Samuel in 1959. Machine Learning is the study of computer algorithms that enables a machine to automatically learn from the data, improve performance from expressions, and predict things without being exactly programmed. Machine learning is part of artificial intelligence and is used to make predictions and decisions without being explicitly programmed. Machine learning algorithms use old data as input to predict something new outputs.

    Features of Machine Learning:-

    • ML uses data to detect various patterns in a given dataset automatically
    • Machine learning can learn from past data and improve automatically
    • ML is a data-driven technology
    • It is much similar to data mining as it also deals with a huge amount of the data

    What is the Need for Machine Learning:-

    • To solve complex problems
    • Decision making in various sectors including finance
    • Finding hidden patterns and extracting useful information from the data
    • Rapid increment in the production of data

    Applications of Machine Learning:-

    • Self-driving cars
    • Stock market trading
    • Image Recognition
    • Speech Recognition
    • Traffic Prediction
    • Online Fraud Detection
    • Medical Diagnosis
    • Email spam and malware filtering
    • Automatic Language Translation

    Life-Cycle of Machine Learning:-

    • Gathering Data
    • Data Preparation
    • Data Wrangling
    • Analyse Data
    • Train Model
    • Test Model
    • Deployment


    If you want to learn Machine Learning with Python, you should know these concepts:-

    • Variables
    • Mathematical Operators
    • Control Statements
    • Data Structures (List, Set, Dict, etc.)
    • Work with files
    • Functions
    • Object-Oriented Programming


    You will have to pick a programming language to learn Machine Learning. In my point of view, Python Programming is the best language for machine learning. You 

    should learn the first python then you can start your Machine Learning Journey or Machine Learning Roadmap.

    Step 1:Introduction of Machine Learning

    • What is Machine Learning?
    • History of ML
    • Features of ML
    • Need for ML
    • Application of ML
    • Life Cycle of ML
    • Best Python libraries for ML
    • AI vs ML
    • Deep Learning vs ML


    Step 2. Basics of Machine Learning

    Classification of ML

    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning

    ML Data Set

    Knowledge of Mean, Mode, and Median

    Standard Deviation

    Variance

    Overfitting

    Underfitting 

    Bias and Variance

    Trade-off

    Gradient:

    • Batch
    • Stochastic

    Dependent Variable

    Independent Variable


    Step 3. Supervised Machine Learning

    What is Supervised Learning?

    Application of Supervised Learning

    Types of Supervised Learning:

    1. Regression

    2. Classification

    What is regression?

    Application of Regression

    Regression:

    • Linear Regression
    • Regression Trees
    • Non-Linear Regression
    • Bayesian Linear Regression
    • Decision Tree Regression
    • Polynomial Regression
    • Random Forest Regression
    • Ridge Regression
    • Lasso Regression


    What is Classification?

    Application of Classification method 

    Classification Algorithm:

    • Random Forest
    • Decision Trees
    • Logistic Regression
    • Support vector Machines
    • K-Nearest Neighbours
    • Kernel SVM
    • Na├»ve Bayes


    Step 4. Unsupervised Machine Learning

    What is Unsupervised Learning?

    Application of Unsupervised Learning

    Types of Unsupervised Learning:

    1. Clustering

    2. Association

    What is Clustering?

    Application of Clustering

    Clustering Algorithm:

    • K-means clustering algorithm
    • K-NN (k nearest neighbors)
    • Partitioning Clustering
    • Density-Based Clustering
    • Mean-Shift Clustering
    • DBSCAN – Density-based clustering
    • Fuzzy Clustering
    • Spectral Clustering
    • OPTICS Clustering
    • Hierarchical clustering
    • Distribution Model-Based Clustering

    What is Association?

    Application of Association


    Step 5. Reinforcement Machine Learning

    • What is Reinforcement learning?
    • Application of Reinforcement Learning
    • Introduction to Thompson Sampling
    • Genetic Algorithm for Reinforcement Learning
    • SARSA Reinforcement Learning


    Step 6. Advance Concept of Machine Learning

    Linear discriminant analysis (LDA)

    Principal Component Analysis(PCA)

    Learning Vector Quantization (LVQ)

    Generalized Additive Models (GAMs)

    Multivariate Adaptive Regression Splines(MARS)

    Regularization methods:

    • Ridge
    • LASSO

    Kernel smoothing methods

    Ensemble learning:

    • Bagging
    • boosting
    • stacking
    • blending

    Ordinary least squares

    Partial Least squares

    Kernel density Estimation

    Radial basis functions

    Multi co-linearity

    CHAID

    AIC, BIC

    ARIMA

    ID3

    K-fold cross-validation

    C4.5 and C5.0

    Gradient boosting


    Step 7. Natural Language Processing

    • What is NLP?
    • Main Components of NLP
    • Real-Life Applications of NLP
    • What is Word sense disambiguation?
    • What is Pronoun resolution?
    • Basic of NLP APIs
    • Machine translation with NLP
    • What are the Phases of NLP?
    • What is Tokenization?
    • Regular expressions in NLP
    • What is Stemming?
    • What is Lemmatization?
    • What is Lemmatization with NLTK?
    • Lemmatization with TextBlob in NLP


    Step 8.Evaluation Metrics for Machine Learning

    • Accuracy
    • Area Under the ROC Curve (AUC)
    • Precision-Recall Curve
    • Specificity
    • Log/Cross Entropy Loss
    • Mean Squared Error
    • Mean Absolute Error


    Step 9. Deep Learning

    • What is Deep Learning?
    • What is a Neural Network?
    • Deep Boltzmann Machine(DBM)
    • Deep Belief Networks(DBN)
    • Deep Learning Frameworks
    • Deep Learning Algorithms
    • Convolutional Neural Network
    • Recurrent Neural Network
    • Bayesian neural nets


    Step 10.Machine Learning Projects

    Create your own emoji Project

    Housing Prices Prediction with Python

    Iris Flowers Classification with Python.

    Fake News Detection using Python

    Handwritten Character Recognition Project

    Uber Data Analysis using Python Project.

    Sentiment Analysis using Python in ML


    Conclusion:

    We learned Machine Learning Complete Roadmap for beginners. Machine Learning Complete Syllabus. Machine Learning Chapters. Machine Learning Roadmap is the overview of all concepts of ML. This article provides a simple overview of the Machine Learning Concept. Machine Learning Roadmap to learn for beginners. Machine Learning.

    Do you have any queries related to This Article, Please mention them in the Comment Section of this Article, We will contact you soon.

    Thank you for reading this blog. I wish you the best in your journey in learning and mastering Machine Learning.

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    Ashish Yadav

    Hi, I am Ashish Yadav, The founder of the codexashish.com website. I am a Data Analyst by profession and a Blogger, and YouTuber by choice and I love sharing my knowledge with needy people like You. I love coding and blogging.

    1 Comments

    1. I appreciate you taking the time and effort to share your knowledge. This material proved to be really efficient and beneficial to me. Thank you very much for providing this information. Continue to write your blog.

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