**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

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**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

- NaÃ¯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:*

*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.

#### You should also check out, Django Developer Roadmap, Python Developer Roadmap, C++ Complete Roadmap, Machine Learning Complete Roadmap, Data Scientist Learning Roadmap, R Developer Roadmap, DevOps Learning Roadmap, and Laravel Developer Roadmap.

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