Machine Learning using Python: A Comprehensive Course

About Machine Learning using Python: A Comprehensive Course

A warm welcome to the

Machine Learning using Python: A Comprehensive Course

by

Uplatz

. The Machine Learning with Python course aims to teach students/course participants some of the core ideas in machine learning, data science, and AI that will help them go from a real-world business problem to a first-cut, working, and deployable AI solution to the problem. Our main goal is to enable participants use the skills they acquire in this course to create real-world AI solutions. We'll aim to strike a balance between theory and practice, with a focus on the practical and applied elements of ML. This Python-based Machine Learning training course is designed to help you grasp the fundamentals of machine learning. It will provide you a thorough knowledge of Machine Learning and how it works. As a Data Scientist or Machine Learning engineer, you'll learn about the relevance of Machine Learning and how to use it in the Python programming language. Machine Learning Algorithms will allow you to automate real-life events. We will explore different practical Machine Learning use cases and practical scenarios at the end of this Machine Learning online course and will build some of them. In this Machine Learning course, you'll master the fundamentals of machine learning using Python, a popular programming language. Learn about data exploration and machine learning techniques such as supervised and unsupervised learning, regression, and classifications, among others. Experiment with Python and built-in tools like Pandas, Matplotlib, and Scikit-Learn to explore and visualize data. Regression, classification, clustering, and sci-kit learn are all sought-after machine learning abilities to add to your skills and CV. To demonstrate your competence, add fresh projects to your portfolio and obtain a certificate in machine learning. Machine Learning Certification training in Python will teach you about regression, clustering, decision trees, random forests, Nave Bayes, and Q-Learning, among other machine learning methods. This Machine Learning course will also teach you about statistics, time series, and the many types of machine learning algorithms, such as supervised, unsupervised, and reinforcement algorithms. You'll be solving real-life case studies in media, healthcare, social media, aviation, and human resources throughout the Python Machine Learning Training.

Course Outcomes:

After completion of this course, student will be able to:

Understand about the roles & responsibilities that a Machine Learning Engineer plays

Python may be used to automate data analysis

Explain what machine learning is

Work with data that is updated in real time

Learn about predictive modelling tools and methodologies

Discuss machine learning algorithms and how to put them into practice

Validate the algorithms of machine learning

Explain what a time series is and how it is linked to other ideas

Learn how to conduct business in the future while living in the now

Apply machine learning techniques on real world problem or to develop AI based application

Analyze and Implement Regression techniques

Solve and Implement solution of Classification problem

Understand and implement Unsupervised learning algorithms

Objective:

Learning basic concepts of various machine learning methods is primary objective of this course. This course specifically make student able to learn mathematical concepts, and algorithms used in machine learning techniques for solving real world problems and developing new applications based on machine learning.

Topics

Python for Machine Learning

Introduction of Python for ML, Python modules for ML, Dataset, Apply Algorithms on datasets, Result Analysis from dataset, Future Scope of ML.

Introduction to Machine Learning

What is Machine Learning, Basic Terminologies of Machine Learning, Applications of ML, different Machine learning techniques, Difference between Data Mining and Predictive Analysis, Tools and Techniques of Machine Learning.

Types of Machine Learning

Supervised Learning, Unsupervised Learning, Reinforcement Learning. Machine Learning Lifecycle.

Supervised Learning : Classification and Regression

Classification: K-Nearest Neighbor, Decision Trees, Regression: Model Representation, Linear Regression.

Unsupervised and Reinforcement Learning

Clustering

:

K-Means Clustering, Hierarchical clustering, Density-Based Clustering.

Machine Learning - Course Syllabus

1. Linear Algebra

Basics of Linear Algebra

Applying Linear Algebra to solve problems

2. Python Programming

Introduction to Python

Python data types

Python operators

Advanced data types

Writing simple Python program

Python conditional statements

Python looping statements

Break and Continue keywords in Python

Functions in Python

Function arguments and Function required arguments

Default arguments

Variable arguments

Build-in functions

Scope of variables

Python Math module

Python Matplotlib module

Building basic GUI application

NumPy basics

File system

File system with statement

File system with read and write

Random module basics

Pandas basics

Matplotlib basics

Building Age Calculator app

3. Machine Learning Basics

Get introduced to Machine Learning basics

Machine Learning basics in detail

4. Types of Machine Learning

Get introduced to Machine Learning types

Types of Machine Learning in detail

5. Multiple Regression

6. KNN Algorithm

KNN intro

KNN algorithm

Introduction to Confusion Matrix

Splitting dataset using TRAINTESTSPLIT

7. Decision Trees

Introduction to Decision Tree

Decision Tree algorithms

8. Unsupervised Learning

Introduction to Unsupervised Learning

Unsupervised Learning algorithms

Applying Unsupervised Learning

9. AHC Algorithm

10. K-means Clustering

Introduction to K-means clustering

K-means clustering algorithms in detail

11. DBSCAN

Introduction to DBSCAN algorithm

Understand DBSCAN algorithm in detail

DBSCAN program