Machine learning roadmap from zero to pro

Machine learning is the top trending career in the world. It is a hard skill to learn and get started if you are a self-taught programmer so, I will guide you on how you can gain machine learning skills and help to change the world using your machine learning skill.

What is machine learning?

Machine learning is the science of getting computers to learn like humans do and improve over time using data.

ML Roadmap

Step 1: Learn a programming language

You can learn either Python or R. I suggest you learn Python.

Python is a very powerful and popular language as it has various libraries for machine learning, math, and statistics. for example, Pandas, Numpy, and Scikit learn. You have to learn sci-kit and learn to work with a machine learning algorithm.

Step 2: Learn Linear Algebra

Linear algebra is a sub-field of mathematics concerned with vectors, matrices, and linear transforms. It is a key foundation in the field of machine learning.

Step3: Learn Basic Python libraries

Mainly there are three most important python libraries required to learn machine learning.

There are also many libraries other than these three like TensorFlow, OpenCV-python, PyTorch, etc but these three are prerequisites to learning machine learning using python.

Step 4: Learn Machine learning algorithms

1. Regression Algorithms

   • Linear Regression
   • Logistic Regression
   • Stepwise Regression
   • Multivariate Adaptive Regression Splines (MARS)
   • Locally Estimated Scatterplot Smoothing (LOESS)

2. Instance-Based Algorithms

   • k-Nearest Neighbor (kNN)
   • Self-Organizing Map (SOM)
   • Support Vector Machines (SVM)

3. Clustering Algorithms

   • k-Means
   • Expectation Maximisation (EM)
   • Hierarchical Clustering

4. Bayesian Algorithms

   • Naive Bayes
   • Gaussian Naive Bayes
   • Averaged One-Dependence Estimators (AODE)
   • Bayesian Network (BN)
   • Bayesian Belief Network (BBN)

5. Decision Tree Algorithms

   • Conditional Decision Trees
   • Classification and Regression Tree (CART)
   • Iterative Dichotomiser 3 (ID3)
   • C4.5 and C5.0

6. Regularization Algorithms

   • Ridge Regression
   • Least Absolute Shrinkage and Selection Operator (LASSO)
   • Elastic Net
   • Least-Angle Regression (LARS)

7. Association Rule Learning Algorithms

   • Apriori algorithm
   • Eclat algorithm

8. Ensemble Algorithms

   • Random Forest
   • Boosting
   • Bootstrapped Aggregation (Bagging)
   • AdaBoost
   • Weighted Average (Blending)
   • Stacked Generalization (Stacking)
   • Gradient Boosting Machines (GBM)
   • Gradient Boosted Regression Trees (GBRT)

9. Artificial Neural Network Algorithms

   • Perceptron
   • Multilayer Perceptrons (MLP)
   • Back-Propagation
   • Stochastic Gradient Descent
   • Hopfield Network
   • Radial Basis Function Network (RBFN)

10. Deep Learning Neural Network Algorithms

    • Convolutional Neural Network (CNN)
    • Recurrent Neural Networks (RNNs)
    • Long Short-Term Memory Networks (LSTMs)
    • Stacked Auto-Encoders
    • Deep Boltzmann Machine (DBM)
    • Deep Belief Networks (DBN)
    • Generative Adversarial Networks (GANs)

11. Dimensionality Reduction Algorithms

    • Principal Component Analysis (PCA)
    • Principal Component Regression (PCR)
    • Partial Least Squares Regression (PLSR)
    • Linear Discriminant Analysis (LDA)
    • Sammon Mapping
    • Multidimensional Scaling (MDS)
    • Projection Pursuit