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Learn Machine Learning:

Mathematics:

  • Linear Algebra
  • Calculus
  • Optimization Theory

Programming:

  • Python Tutorials
  • Python Skills
  • Python Pipeline
  • Python Topics
  • Python Libraries

Probability & Statistics:

  • Probability Distributions
  • Bayesian Statistics
  • Time Series
  • Linear Models
  • Multivariate Statistics
  • Sampling
  • Hypothesis Testing

Algorithms:

  • Data Structures
  • Search Methods
  • Sorting
  • Hash Tables
  • Heaps
  • Trees
    • Binary Trees
    • AVL Trees
    • Red-Black Trees
  • Graphs
  • Lists
  • Stacks
  • Queues

Data Collection:

  • Questions to ask
  • Data Mining
  • Types of data
    • Structured Data
      • Nominal/Categorical Data
      • Numerical Data
      • Ordinal Data
      • Time Series Data
    • Unstructured Data

Data Preparation:

  • Exploratory Data Analysis
  • Data Pre-Processing
    • Tokenization
    • Handling missing values
      • Feature Imputation
      • Missing value prediction
      • Omitting the columns
      • Creating the category
      • Choosing an algorithm that supports the missing values
    • Featurizing
      • Feature Selection
      • Feature Encoding
      • Feature Normalization
      • Feature Engineering
    • Data Wrangling
    • Vectorizing
    • Dealing with Imbalances
  • Data Splitting

Training the Model:

Choosing an algorithm:

  • Neural Networks
    • Feedforward Neural Networks
    • Convolutional Neural Networks (CNNs)
    • Recurrent Neural Networks (RNNs)
    • Autoencoders
    • Long Short Term Memory (LSTM) Cells
    • Restricted Boltzmann Machines (RBMs)
    • Generative Adversarial Networks (GANs)

  • Supervised Learning Methods
    • Classification:
      • Naive Bayes
      • Decision Trees
      • k-Nearest Neighbors (kNN)
      • Support Vector Machines (SVMs)
      • Logistic Regression
      • Random Forest
    • Regression
      • Linear Regression
      • Support Vector Regression
      • Polynomial Regression
      • Ordinary Least Squares
        • Lasso Regression
        • Ridge Regression
        • ElasticNet Regression

  • Unsupervised Learning Methods
    • Association Rule Learning
      • Apriori
      • Eclat
    • Clustering
      • k-Means Clustering
      • Spectral Clustering
      • Hierarchical Cluster Analysis (HCA)
      • Expectation Maximization
    • Visualization and Dimensionality Reduction
      • Principal Component Analysis (PCA)
      • Kernel PCA
      • Locally-Linear Embedding (LLE)
      • t-distributed Stochastic Neighbor Embedding (t-SNE)
      • Manifold Learning

  • Semi-Supervised Learning Methods
    • Reinforcement Learning
    • Generative Models

  • Self-Supervised Learning Methods

  • Types of Learning
    • Batch Learning
    • Online Learning
    • Transfer Learning
    • Active Learning
    • Ensemble Methods
  • Underfitting
  • Overfitting
  • Regularization
    • L1 Regularization
    • L2 Regularization
  • Dropout
  • Hyperparameter Tuning

Model Analysis/Evaluation


Serving the Model


Making Predictions


Retraining the Model

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