Python for Data Science and Machine Learning

Course Number:

NTVPYT012

The Python for Data Science and Machine Learning course builds on concepts from the Python for Data Analytics courses. Students will learn to prepare data for machine learning, implement machine learning algorithms, train machine learning models, and use evaluation metrics on their machine learning models. In the end, students will obtain the skills necessary to utilize machine learning for predictive analytics.

Audience:

This course is intended for data analysts with some experience in Python and basic data analysis.
Course Duration:
3 days

Prerequisites:

The following are prerequisites for this course:

  • Moderate programming knowledge in Python
  • Python for Data Analytics course or understanding of the concepts
    • Advanced Data Structures
    • Descriptive Analytics in Python
    • Statistical Analysis in Python
    • Data Visualization in Python
Course Objectives:

Upon completion of this course, participants should:

  • Prepare data for machine learning
  • Implement machine learning algorithms
  • Use SciKit-Learn, Tensorflow, and Keras for machine learning tasks
  • Successfully train and fine-tune machine learning models
  • Evaluate machine learning models
Course Outline:

Part 1: Introduction to Machine Learning

  • Applications of Machine Learning
  • Challenges of Machine Learning
  • Big Data
  • Python Libraries for Machine Learning
  • Artificial Neurons
  • Supervised vs. Unsupervised Machine Learning

Part 1: Supervised Machine Learning

  • Classification and Regression
  • Generalization, Overfitting, and Underfitting
  • Model Complexity and Dataset Size
  • Linear Models
  • Naive Bayes Classifiers
  • Decision Trees
  • Neural Networks and Deep Learning
  • Random Forests

Part 2: Unsupervised Machine Learning

  • Challenges in Unsupervised Machine Learning
  • Preprocessing
    • Handling Missing Data
    • Handling Categorical Data
  • k-means Clustering and Data Mining
  • Dimensionality Reduction

Part 4: Training Models

  • Linear Regression
  • Gradient Decent
  • Polynomial Regression
  • Logistic Regression

Part 3: Model Evaluation and Improvement

  • Cross Evaluation
  • Grid Searching
  • Evaluation Metrics
  • CSV
  • Bytes

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