Unleash AI: A Hands-On Guide to Practical Machine Learning with Python Training Course

Introduction

Python has emerged as the language of choice for machine learning, thanks to its simplicity and a vast ecosystem of powerful libraries. This course moves beyond theoretical concepts to focus on the practical application of machine learning algorithms. You will learn to use industry-standard tools to build, evaluate, and deploy models that solve real-world problems, from classification to regression and clustering.

This five-day, project-based training will provide you with a comprehensive, hands-on experience in the entire machine learning pipeline. You will master the process of data preparation, model training, and performance tuning using Python's most popular libraries. By the end, you'll have a strong portfolio of projects and the confidence to apply machine learning to your own data.

Duration 5 days

Target Audience This course is for developers, data analysts, and aspiring data scientists who want to build a strong practical foundation in machine learning. A solid understanding of Python programming is required.

Objectives

  • To master the fundamentals of Python's key data science libraries: NumPy, Pandas, and Matplotlib.
  • To understand the complete machine learning pipeline from data ingestion to model deployment.
  • To learn and apply various supervised learning algorithms for classification and regression.
  • To gain hands-on experience with unsupervised learning algorithms like clustering.
  • To develop a robust process for data cleaning, preprocessing, and feature engineering.
  • To be able to effectively evaluate and compare model performance using key metrics.
  • To understand and implement techniques for hyperparameter tuning and cross-validation.
  • To handle common issues like overfitting, underfitting, and imbalanced data.
  • To build an end-to-end machine learning project using scikit-learn.
  • To gain an introduction to more advanced topics like deep learning and MLOps.

Course Modules

Module 1: Python for Data Science

  • An in-depth review of NumPy for numerical operations.
  • Mastering Pandas for data manipulation and analysis.
  • Creating compelling visualizations with Matplotlib and Seaborn.
  • Setting up your data science environment.
  • A quick overview of Jupyter Notebooks and VS Code.

Module 2: The Machine Learning Pipeline

  • The CRISP-DM methodology overview.
  • The critical steps of a machine learning project.
  • The difference between supervised and unsupervised learning.
  • A conceptual overview of model training and prediction.
  • A discussion on the importance of data quality.

Module 3: Supervised Learning - Classification

  • An introduction to classification problems.
  • The basics of Logistic Regression.
  • A hands-on guide to using k-Nearest Neighbors (k-NN).
  • Building and evaluating a Decision Tree Classifier.
  • Understanding the core concepts of Ensemble Methods.

Module 4: Supervised Learning - Regression

  • An introduction to regression problems.
  • Building a simple Linear Regression model.
  • Using a Support Vector Machine (SVM) for regression.
  • A practical guide to using Random Forest Regressor.
  • The importance of different regression evaluation metrics.

Module 5: Data Preprocessing & Feature Engineering

  • Handling missing data: imputation and deletion.
  • The importance of scaling and normalization.
  • Working with categorical data: one-hot encoding.
  • Creating new features to improve model performance.
  • Strategies for dealing with outliers.

Module 6: Unsupervised Learning - Clustering

  • The concept of finding hidden patterns in data.
  • A deep dive into the k-Means clustering algorithm.
  • Understanding the Elbow Method to find the optimal number of clusters.
  • An introduction to other clustering algorithms like DBSCAN.
  • Practical applications of clustering.

Module 7: Model Evaluation & Metrics

  • The difference between accuracy, precision, and recall.
  • The power of the confusion matrix.
  • Using ROC curves and AUC for classification models.
  • Cross-validation techniques to ensure model robustness.
  • A practical guide to choosing the right metric for your problem.

Module 8: Overfitting and Underfitting

  • Understanding the bias-variance trade-off.
  • The concept of overfitting and how to detect it.
  • Techniques for preventing overfitting: regularization.
  • Identifying and fixing underfitting in your models.
  • The importance of a well-defined validation set.

Module 9: Hyperparameter Tuning

  • What are hyperparameters?
  • A practical guide to using Grid Search.
  • The efficiency of Randomized Search.
  • An introduction to automated hyperparameter tuning libraries.
  • The impact of tuning on model performance.

Module 10: The Scikit-Learn Ecosystem

  • A deep dive into the scikit-learn library.
  • The unified API for all scikit-learn estimators.
  • Creating a complete pipeline with scikit-learn.
  • A discussion on the different modules within the library.
  • Tips and tricks for using scikit-learn effectively.

Module 11: End-to-End Project 1 - Classification

  • A hands-on project to build a classification model.
  • The project will cover data loading, cleaning, and model training.
  • Students will evaluate their model's performance.
  • A discussion on how to deploy the model in a real-world scenario.
  • A review of a well-structured project.

Module 12: End-to-End Project 2 - Regression

  • A second, hands-on project focusing on a regression problem.
  • Students will apply the concepts learned in previous modules.
  • The project will emphasize the importance of data visualization and feature engineering.
  • A discussion on how to interpret regression results.
  • A review of the project and next steps.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport Pick Up is provided by the institute. Accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

 

Unleash Ai: A Hands-on Guide To Practical Machine Learning With Python Training Course in Namibia
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