very comprehensive course.
Python Machine Learning and Data Science Course
Machine Learning and Data Science for programming beginners using Python with scikit-learn, SciPy, Matplotlib, and Pandas
Development,Python,Machine Learning
Lectures -92
Resources -2
Duration -10 hours
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Course Description
Python Machine Learning and Data Science are crucial and this course will walk you through them. Machine Learning, Artificial Intelligence, and Deep Learning neural networks are the most frequently used terms and also the most muddled and misunderstood terms. Neural networks and machine learning are two subsets of the broad machine learning platform.
Python Machine Learning and Data Science for Beginners Overview
When we were young, we began to think logically about a variety of topics and to experience emotions, among other things. We persisted in thinking and came up with fixes for issues we encountered every day. The scientists working on deep learning neural networks are aiming towards that. a device that thinks.
Nonetheless, the primary area of focus in this course is machine learning. We are getting our machine ready for a prediction test during this course. It works exactly like how you would prepare for a maths exam in school or college. We develop our skills and practice ourselves by resolving as many similar mathematical puzzles as we can. Let's refer to these hypothetical examples of comparable issues and their solutions as the "Training Input" and "Training Output," respectively. And then the day of the test finally arrives. We will be given a brand-new set of issues to tackle, but they will be quite similar to the problems we learned. We must solve them based on our prior practice and learning experiences.
These issues might be referred to as "Testing Input" and our solutions as "Predicted Output." These responses will then be evaluated by our professor and compared to the actual responses, which we refer to as the "Test Output." Following that, a grade will be assigned based on the correct responses. We refer to this as our "Accuracy" mark. A machine learning engineer's and data scientist's livelihood is devoted to using various methods and evaluation criteria to increase this accuracy as much as feasible.
These are the main subjects covered in this course. Python is the programming language we are employing. Python is a fantastic tool for creating programs that analyze and predict data. It offers a ton of classes and features that carry out intricate mathematical analyses and provide solutions in straightforward one or two lines of code, allowing anyone to learn data science and machine learning without needing to be an expert statistician or mathematician. Python considerably streamlines the process.
Goals
What will you learn in this course:
Beginners who are interested in Machine Learning using Python
Learn fundamentals of machine learning and data science using Python
Develop the skills you need to apply machine learning and data science to real-world problems
Prepare for a career in machine learning or data science
Understand the scikit-learn machine learning library
Learn how data visualization works
Understand how natural language processing works
Understand how deep learning works
Prerequisites
What are the prerequisites for this course?
- A medium-configuration computer and the willingness to indulge in the world of Machine Learning
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
3 Lectures
- Course Overview & Table of Contents 09:08 09:08
- Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 04:37 04:37
- Introduction to Machine Learning - Part 2 - Classifications and Applications 05:54 05:54
System and Environment preparation
3 Lectures
Learn Basics of python
4 Lectures
Learn Basics of NumPy
3 Lectures
Learn Basics of Matplotlib
1 Lectures
Learn Basics of Pandas
2 Lectures
CSV data file
4 Lectures
Dataset Summary
4 Lectures
Dataset Visualization
2 Lectures
Multivariate Dataset Visualization
3 Lectures
Data Preparation (Pre-Processing)
7 Lectures
Feature Selection
6 Lectures
Refresher Session - The Mechanism of Re-sampling, Training and Testing
1 Lectures
Algorithm Evaluation Techniques
5 Lectures
Algorithm Evaluation Metrics
10 Lectures
Classification Algorithm Spot Check
13 Lectures
Compare Algorithms
2 Lectures
Pipelines
2 Lectures
Performance Improvement
5 Lectures
Export, Save and Load Machine Learning Models
2 Lectures
Finalizing Model
2 Lectures
Quick Session: Imbalanced Data Set - Issue Overview And Steps
1 Lectures
Iris Dataset : Finalizing Multi-Class Dataset
1 Lectures
Finalizing a Regression Model - The Boston Housing Price Dataset
1 Lectures
Real-time Predictions
3 Lectures
SOURCE CODE ATTACHED
1 Lectures
Instructor Details
Abhilash Nelson
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