Do you want to learn form basic to advance machine learning but don't know where to start? In this tutorial, we will see complete roadmap from basic to advance for machine learning. You can follow this roadmap to know main concept of machine learning. Let's start:-
Machine Learning Complete Roadmap
Step 1:Introduction
What is Machine Learning?
History of ML
Features of ML
Need for ML
Application of ML
Life Cycle of ML
AI vs ML
Deep Learning vs ML
Step 2.Basics of ML
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 Learning
What is Supervised Learning?
Application of Supervised Learning
Types of Supervised Learning:
1. Regression
2. Classification
What is reggression?
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
Naïve Bayes
Step 4.Unsupervised 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 Learning
What is Reinforcement learning?
Application of Reinforcement Learning
Introduction to Thompson Sampling
Genetic Algorithm for Reinforcement Learning
SARSA Reinforcement Learning
Step 6.Some Advance Concept
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.NLP
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 ML
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
Cartoonify Images with Machine Learning Project
Create your own emoji Project
Housing Prices Prediction with Python
Iris Flowers Classification with Python
Stock Price Prediction Project
Fake News Detection using Python
Handwritten Character Recognition Project
Uber Data Analysis using Python Project
Sentiment Analysis using Python in ML
Thank you for reading this blog.
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