Learn Machine Learning:

Mathematics:

• Linear Algebra
• Calculus
• Optimization Theory

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Programming:

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

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Probability & Statistics:

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

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Algorithms:

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

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Data Collection:

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

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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

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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

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Model Analysis/Evaluation

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Serving the Model

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Making Predictions

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Retraining the Model