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Machine Learning with Python

Duration

2 Months

Career Option

ML Engineer / AI Engineer / Data Scientist / Computational Linguist

Group Size

2-10 Persons

Overview

Syllabus

  • Basics
  • Introduction to Statistics
  • Machine Learning Applications & Landscape
  • Building end-to-end Machine Learning Project
  • Classifications
  • Training Models
  • Support Vector Machines
  • Decision Trees
  • Ensemble Learning and Random Forests
  • Dimensionality Reduction

Detail Study

  • Basics
    1. Introduction to Linux
    2. Introduction to Python
    3. Hands-on using Jupyter
    4. Overview of Linear Algebra
    5. Introduction to NumPy & Pandas
  • Introduction to Statistics
    1. Statistical Inference
    2. Types of Variables
    3. Probability Distribution
    4. Normality
    5. Measures of Central Tendencies
    6. Normal Distribution
  • Machine Learning Applications & Landscape
    1. Introduction to Machine Learning,
    2. Machine Learning Application
    3. Introduction to AI
    4. Different types of Machine Learning – Supervised, Unsupervised
    5. Reinforcement
  • Building end-to-end Machine Learning Project
    1. Machine Learning Projects Checklist
    2. Frame the problem and look at the big picture
    3. Get the data
    4. Explore the data to gain insights
    5. Prepare the data for Machine Learning algorithms
    6. Explore many different models and short-list the best ones
    7. Fine-tune model
    8. Present the solution
    9. Launch, monitor and maintain the system
  • Classifications
    1. Training a Binary classification
    2. Performance Measures
    3. Confusion Matrix
    4. Precision and Recall
    5. Precision/Recall Tradeoff
    6. The ROC Curve
    7. Multiclass Classification
    8. Multilabel Classification
    9. Multi-output Classification
  • Training Models
    1. Linear Regression
    2. Gradient Descent
    3. Polynomial Regression
    4. Learning Curves
    5. Regularized Linear Models
    6. Logistic Regression
  • Support Vector Machines
    1. Linear SVM Classification
    2. Nonlinear SVM Classification
    3. SVM Regression
  • Decision Trees
    1. Training and Visualizing a Decision Tree
    2. Making Predictions
    3. Estimating Class Probabilities
    4. The CART Training Algorithm
    5. Gini Impurity or Entropy
    6. Regularization Hyperparameters
    7. Regression
    8. Instability
  • Ensemble Learning and Random Forests
    1. Voting Classifiers
    2. Bagging and Pasting
    3. Random Patches and Random Subspaces
    4. Random Forests
    5. Boosting
    6. Stacking
  • Dimensionality Reduction
    1. The Curse of Dimensionality
    2. Main Approaches for Dimensionality Reduction
    3. PCA
    4. Kernel PCA
    5. Other Dimensionality Reduction Techniques

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