Python and Machine Learning (ML) – Part 1

Course Overview

This course provides a structured introduction to machine learning, covering the foundational concepts and exploring practical applications using Python. By the end of this course, you will have a solid understanding of key machine learning techniques and how to implement them using popular libraries like Scikit-learn and TensorFlow.

What You Will Learn

• Machine Learning Introduction: Gain a clear understanding of machine learning, its significance, and its role in driving advancements across various industries.
• General Information about Machine Learning: Learn about the different types of machine learning, including supervised, unsupervised, and reinforcement learning, and when to use each type.
• Supervised Learning Techniques:
  • Linear Regression: Understand the basics of linear regression and how it is used to predict continuous values based on input data.
  • Support Vector Machines (SVM): Explore the concepts behind SVMs and how they classify data by finding the optimal hyperplane that separates different classes.
  • Decision Trees: Learn how decision trees work for both classification and regression tasks, using a tree-like model to make decisions.
• Using Machine Learning in Python:
  • Prepare your Data: Learn the essential steps for data preprocessing, including cleaning, normalizing, and transforming data to ensure accurate model training.
  • Choose a Model: Understand the criteria for selecting the appropriate machine learning model based on your data and problem statement.
  • Train the Model: Get hands-on experience with training your model using different algorithms and techniques.
  • Evaluate the Model: Learn how to measure the performance of your model using metrics such as accuracy, precision, recall, and F1 score.
  • Parameter Tuning: Understand how to tune model parameters using techniques like grid search and random search to optimize performance.
  • Deploying a Machine Learning Model: Explore the process of deploying a trained model into production, ensuring it integrates well with existing systems and data pipelines.
• Machine Learning Libraries:
  • Scikit-learn Python Library:
    • Introduction: Gain an overview of Scikit-learn, a popular machine learning library known for its ease of use and extensive features.
    • APIs: Learn about the APIs provided by Scikit-learn for various machine learning tasks, including data preprocessing, model selection, and evaluation.
    • How to Use Scikit-learn Python Library: Get practical experience using Scikit-learn to implement machine learning models, from data preprocessing to model evaluation.
  • TensorFlow Python Library:
    • Introduction: Understand the basics of TensorFlow, a leading open-source library for numerical computation and large-scale machine learning.
    • APIs: Explore the APIs provided by TensorFlow, from low-level operations to high-level abstractions for model building.
    • How to Use TensorFlow Python Library: Learn how to use TensorFlow to build and train deep learning models, leveraging its powerful computational capabilities.