Overview What You Will Learn Curriculum Instructor

Course Overview

The field of machine learning (ML) is continuously evolving, and the increasing demand for ML is because it revolutionizes data automation by analyzing patterns to effectively predict outcomes and building outstanding solutions.  

This course begins with an introduction to data science and machine learning principles and will help you set up the Python environment for Machine Learning. You will import, explore, visualize, and clean datasets for your machine learning modelling using popular Python libraries such as Pandas, Matplotlib, and Seaborn. Further, you will learn data classification and regression by training your own algorithms. You will be importing sci-kit-learn, organizing data into sets, and building an efficient classifier in Python. Further, you will learn to modify parameters and improve the performance of models.  You will also learn to expose ML models as APIs and integrate them with a web application.  

By the end of this course, you will learn how to implement ML algorithms and use Python libraries to solve complex problems encountered by data scientists.  

The necessary resources for this course are in the "Resources" section of Video 1.1. You can also access them through this direct link - https://github.com/ec-council-learning/Machine-Learning-with-Python

What You Will Learn

  • Understand Machine learning core concepts
  • End-to-end Machine learning workflow
  • Build
  • train
  • and evaluate ML models using Python Scikit-learn library
  • Serve Ml models by implementing APIs
  • Explore
  • visualize
  • and clean data for ML models using Python
  • Pandas
  • Matplotlib
  • Seaborn library

Program Curriculum

  • Understanding Machine Learning
  • Understanding Machine Learning Process
  • Understanding Types of Machine Learning
  • Explore Python for Machine Learning
  • Development Environment Setup for ML
  • $7 Million Cybersecurity Scholarship by EC-Council
  • Chapter 1 Quiz

  • Data Pre-processing Overview
  • Understanding Data Pre-processing
  • Data Pre-processing Steps
  • Demo – Pre-processing a Dataset Using Python, Pandas, NumPy, and Sklearn
  • Chapter 2 Quiz

  • Module Overview
  • Understanding Classification
  • Understanding Types of Classification
  • Implement an ML Classifier for Diabetes Classification
  • Understanding Evaluation of Classification Models
  • Summary
  • Chapter 3 Quiz

  • Module Overview
  • Understanding Regression – Why We Use It?
  • Understanding Different Types of Regression
  • Demo – Build a Regression Model Using TensorFlow
  • Understanding Regularization and Types
  • Chapter 4 Quiz

  • Module Overview
  • What Fine-Tuning of Models Means in Machine Learning?
  • Understanding Hyperparameter Tuning
  • Demo – Performing Hyperparameters Tuning
  • Understanding Cross-validation Method
  • Chapter 5 Quiz

  • Module Overview
  • How to Export Machine Learning Models?
  • Build a Rest API to Server H5 Machine Learning Model
  • Deploy Machine Learning Model with TensorFlow Serving
  • Summary
  • Chapter 6 Quiz

  • Module Overview
  • What is Deep Learning?
  • How Does Deep Learning Work?
  • Basic Concepts and Terms in Deep Learning
  • Deep Learning Applications
  • Demo - Build a Deep Learning Model Using TensorFlow
  • Summary
  • Chapter 7 Quiz
Load more modules

Instructor

Abdul Rehman

Abdul Rehman is a Machine learning engineer with years of industry-level experience for building power and intelligent machine learning applications, and the founder at Pythnist.org. Which is a place to learn Python technologies, including web development, machine learning, and Data Science.

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