Programming Private

Python for Machine Learning (PYT_ML)

3 days
Python

This course teaches you to use Python for data analysis, predictive modeling, and machine learning.

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  • Private class for your team
  • Live expert instructor
  • Online or on‑location
  • Customizable agenda
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Course Overview

This course teaches you to use Python for data analysis, predictive modeling, and machine learning. You will get hands-on practice using Python to to forecast trends and outcomes with confidence. Each lesson builds essential skills that will prepare you to handle real business situations and challenges using predictive analytics, transforming raw data into actionable insights that drive decision-making.

The course emphasizes practical understanding, showing you what each model does, when to use it, how to tune it, and how to tell if it's working. By the end of this course, you'll be able to take a real dataset, build a model against it, and trust the results enough to act on them.

Course Benefits

  • Understand the fundamentals of how machine learning models learn from data
  • Classify data into distinct categories using classification techniques
  • Predict continuous outcomes with regression models
  • Group similar data points using clustering methods
  • Differentiate between supervised and unsupervised learning approaches
  • Visualize data effectively with a review of core charting techniques
  • Manipulate and analyze data using Pandas
  • Work with NumPy arrays for efficient data handling
  • Organize data into groups to uncover deeper insights
  • Clean and prepare data for machine learning
  • Scale and standardize data to improve model performance
  • Tune model parameters to improve prediction accuracy
  • Summarize data through aggregation to extract meaningful patterns

Delivery Methods

Private Class
Delivered for your team at your site or online.

Course Outline

  1. Foundational Python Topics
    1. Python syntax and variables (data types, assignment, conversions) 
    2. Control structures (loops, conditionals, boolean logic) 
    3. Data structures (lists, dictionaries, basic manipulation) 
    4. Functions and methods (calling functions, understanding parameters) 
    5. File I/O basics (reading/writing files)  
    6. Jupyter Notebook environment
  2. Data Exploration Review
    1. Basic Plotting: Visualize data to identify trends
    2. Grouping: Organize data for analysis
    3. Data Diagnosis Review: Assess data quality and relevance
    4. Making Predictions Without ML: Understand basic prediction techniques
  3.  Machine Learning Models
    1. Linear Regression: Predict outcomes using linear relationships
    2. Logistic Regression (Classification): Classify data into categories
    3. Properly Preparing Data for ML: Ensure data is ready for analysis
      1. Data Cleaning: Remove inaccuracies and inconsistencies
      2. Handling Nulls: Address missing data effectively
      3. Assessing Data Relevance: Determine which data is useful
      4. Converting Categorical Data to Numerical Data: Prepare data for analysis
      5. Using Dummies: Simplify categorical variables
    4. SKLearn Tools: Utilize essential tools for machine learning
      1. Label Encoder: Convert labels into a format suitable for ML
      2. SimpleImputer: Handle missing values efficiently
      3. Pandas Categorical Data Type: Optimize data storage
    5. Brute Force Techniques: Explore exhaustive search methods
    6. Normalization and Scaling: Standardize data for better model performance
    7. Correlation: Understand relationships between variables
  4. Perfecting Models
    1. Checking Accuracy & Scoring: Evaluate model performance
    2. Relative Frequency in Unique Values: Analyze data distributions
    3. Prediction and Probability Prediction: Make informed predictions
    4. Changing Threshold: Adjust decision boundaries for better results
  5. Evaluation Metrics
    1. Score: Understand how to measure model performance
    2. Accuracy Score: Assess the correctness of predictions
    3. Cross-Validation: Validate model reliability
    4. ROC, AUC: Evaluate model performance visually
    5. Precision: Measure the accuracy of positive predictions
    6. MSE: Calculate the average squared error
    7. Recall: Assess the model's ability to identify relevant instances
    8. Classification Report: Summarize model performance metrics
  6. Testing Various Models
    1. Choosing the Best Estimator: Select the most effective model
    2. DecisionTreeClassifier: Understand decision tree algorithms
    3. RandomForestClassifier: Explore ensemble methods for improved accuracy
  7. Grid Search and Manual Parameter Tuning: Optimize model parameters for better performance
  8. Practice Perfecting Regression Models: Apply techniques to enhance regression accuracy
  9. Other General Useful Techniques
    1. Sentiment Analysis: Analyze text data for insights
    2. XGBoost: Utilize advanced boosting techniques for improved predictions
    3. Prescriptive Analysis: Go beyond predictions to recommend actions.
  10. Clustering
    1. The Elbow Method: Determine the optimal number of clusters
    2. Applying Clusters Practically: Implement clustering techniques in real-world scenarios
  11. Using PCA (Principal Component Analysis): Simplify complex datasets while retaining essential information

Class Materials

Each student receives a comprehensive set of materials, including course notes and all class examples.

Class Prerequisites

Experience in the following is required for this Python class:

Python Basics as taught in our Python Essentials Training

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