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