Machine Learning using Python- A Comprehensive Course

Machine Learning using Python: A Comprehensive Course, available at $44.99, has an average rating of 3.35, with 160 lectures, 1 quizzes, based on 208 reviews, and has 39390 subscribers.

You will learn about Learn the A-Z of Machine Learning from scratch Build your career in Machine Learning, Deep Learning, and Data Science Become a top Machine Learning engineer Core concepts of various Machine Learning methods Mathematical concepts and algorithms used in Machine Learning techniques Solve real world problems using Machine Learning Develop new applications based on Machine Learning Apply machine learning techniques on real world problem or to develop AI based application Analyze and implement Regression techniques Linear Algebra basics A-Z of Python Programming and its application in Machine Learning Python programs, Matplotlib, NumPy, basic GUI application File system, Random module, Pandas Build Age Calculator app using Python Machine Learning basics Types of Machine Learning and their application in real-life scenarios Supervised Learning – Classification and Regression Multiple Regression KNN algorithm, Decision Tree algorithms Unsupervised Learning concepts & algorithms AHC algorithm K-means clustering & DBSCAN algorithm and program Solve and implement solutions of Classification problem Understand and implement Unsupervised Learning algorithms This course is ideal for individuals who are Machine Learning Engineers & Artificial Intelligence Engineers or Data Scientists & Data Engineers or Newbies and Beginners aspiring for a career in Data Science and Machine Learning or Machine Learning SMEs & Specialists or Anyone (with or without data background) who wants to become a top ML engineer and/or Data Scientist or Data Analysts and Data Consultants or Data Visualization and Business Intelligence Developers/Analysts or CEOs, CTOs, CMOs of any size organizations or Software Programmers and Application Developers or Senior Machine Learning and Simulation Engineers or Machine Learning Researchers – NLP, Python, Deep Learning or Deep Learning and Machine Learning enthusiasts or Machine Learning Specialists or Machine Learning Research Engineers – Healthcare, Retail, any sector or Python Developers, Machine Learning, IOT, AirFlow, MLflow, Kubef or Computer Vision / Deep Learning Engineers – Python It is particularly useful for Machine Learning Engineers & Artificial Intelligence Engineers or Data Scientists & Data Engineers or Newbies and Beginners aspiring for a career in Data Science and Machine Learning or Machine Learning SMEs & Specialists or Anyone (with or without data background) who wants to become a top ML engineer and/or Data Scientist or Data Analysts and Data Consultants or Data Visualization and Business Intelligence Developers/Analysts or CEOs, CTOs, CMOs of any size organizations or Software Programmers and Application Developers or Senior Machine Learning and Simulation Engineers or Machine Learning Researchers – NLP, Python, Deep Learning or Deep Learning and Machine Learning enthusiasts or Machine Learning Specialists or Machine Learning Research Engineers – Healthcare, Retail, any sector or Python Developers, Machine Learning, IOT, AirFlow, MLflow, Kubef or Computer Vision / Deep Learning Engineers – Python.

Enroll now: Machine Learning using Python: A Comprehensive Course

Summary

Title: Machine Learning using Python: A Comprehensive Course

Price: $44.99

Average Rating: 3.35

Number of Lectures: 160

Number of Quizzes: 1

Number of Published Lectures: 160

Number of Published Quizzes: 1

Number of Curriculum Items: 161

Number of Published Curriculum Objects: 161

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Learn the A-Z of Machine Learning from scratch

Build your career in Machine Learning, Deep Learning, and Data Science

Become a top Machine Learning engineer

Core concepts of various Machine Learning methods

Mathematical concepts and algorithms used in Machine Learning techniques

Solve real world problems using Machine Learning

Develop new applications based on Machine Learning

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

Analyze and implement Regression techniques

Linear Algebra basics

A-Z of Python Programming and its application in Machine Learning

Python programs, Matplotlib, NumPy, basic GUI application

File system, Random module, Pandas

Build Age Calculator app using Python

Machine Learning basics

Types of Machine Learning and their application in real-life scenarios

Supervised Learning – Classification and Regression

Multiple Regression

KNN algorithm, Decision Tree algorithms

Unsupervised Learning concepts & algorithms

AHC algorithm

K-means clustering & DBSCAN algorithm and program

Solve and implement solutions of Classification problem

Understand and implement Unsupervised Learning algorithms

Who Should Attend

Machine Learning Engineers & Artificial Intelligence Engineers

Data Scientists & Data Engineers

Newbies and Beginners aspiring for a career in Data Science and Machine Learning

Machine Learning SMEs & Specialists

Anyone (with or without data background) who wants to become a top ML engineer and/or Data Scientist

Data Analysts and Data Consultants

Data Visualization and Business Intelligence Developers/Analysts

CEOs, CTOs, CMOs of any size organizations

Software Programmers and Application Developers

Senior Machine Learning and Simulation Engineers

Machine Learning Researchers – NLP, Python, Deep Learning

Deep Learning and Machine Learning enthusiasts

Machine Learning Specialists

Machine Learning Research Engineers – Healthcare, Retail, any sector

Python Developers, Machine Learning, IOT, AirFlow, MLflow, Kubef

Computer Vision / Deep Learning Engineers – Python

Target Audiences

Machine Learning Engineers & Artificial Intelligence Engineers

Data Scientists & Data Engineers

Newbies and Beginners aspiring for a career in Data Science and Machine Learning

Machine Learning SMEs & Specialists

Anyone (with or without data background) who wants to become a top ML engineer and/or Data Scientist

Data Analysts and Data Consultants

Data Visualization and Business Intelligence Developers/Analysts

CEOs, CTOs, CMOs of any size organizations

Software Programmers and Application Developers

Senior Machine Learning and Simulation Engineers

Machine Learning Researchers – NLP, Python, Deep Learning

Deep Learning and Machine Learning enthusiasts

Machine Learning Specialists

Machine Learning Research Engineers – Healthcare, Retail, any sector

Python Developers, Machine Learning, IOT, AirFlow, MLflow, Kubef

Computer Vision / Deep Learning Engineers – Python

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

Course Curriculum

Chapter 1: LINEAR ALGEBRA FOR MACHINE LEARNING

Lecture 1: PART 1 – INTRODUCTION TO LINEAR ALGEBRA

Lecture 2: PART 2 – INTRODUCTION TO LINEAR ALGEBRA

Lecture 3: PART 1 – LINEAR ALGEBRA BASICS

Lecture 4: PART 2 – LINEAR ALGEBRA BASICS

Lecture 5: PART 3 – LINEAR ALGEBRA BASICS

Lecture 6: PART 4 – LINEAR ALGEBRA BASICS

Lecture 7: PART 5 – LINEAR ALGEBRA BASICS

Lecture 8: PART 6 – LINEAR ALGEBRA BASICS

Lecture 9: PART 7 – LINEAR ALGEBRA BASICS

Lecture 10: PART 8 – LINEAR ALGEBRA BASICS

Lecture 11: PART 9 – LINEAR ALGEBRA BASICS

Lecture 12: PART 10 – LINEAR ALGEBRA BASICS

Lecture 13: PART 11 – LINEAR ALGEBRA BASICS

Lecture 14: PART 12 – LINEAR ALGEBRA BASICS

Lecture 15: PART 13 – LINEAR ALGEBRA BASICS

Chapter 2: PYTHON PROGRAMMING

Lecture 1: PART 1 – INTRODUCTION TO PYTHON

Lecture 2: PART 2 – INTRODUCTION TO PYTHON

Lecture 3: PYTHON DATATYPES

Lecture 4: PYTHON OPERATORS

Lecture 5: ADVANCED DATA TYPES

Lecture 6: SIMPLE PYTHON PROGRAM

Lecture 7: PYTHON CONDITION STATEMENTS

Lecture 8: PYTHON LOOPING STATEMENTS

Lecture 9: BREAK AND CONTINUE KEYWORDS IN PYTHON

Lecture 10: FUNCTIONS IN PYTHON

Lecture 11: FUNCTION ARGUMENTS

Lecture 12: FUNCTION REQUIRED ARGUMENTS

Lecture 13: DEFAULT ARGUMENTS

Lecture 14: VARIABLE ARGUMENTS

Lecture 15: PART 1 – BUILT-IN FUNCTIONS

Lecture 16: PART 2 – BUILT-IN FUNCTIONS

Lecture 17: SCOPE OF VARIABLES

Lecture 18: PART 1 – PYTHON MATH MODULE

Lecture 19: PART 2 – PYTHON MATH MODULE

Lecture 20: PYTHON MATPLOTLIB MODULE

Lecture 21: PART 1 – A BASIC GUI APPLICATION

Lecture 22: PART 2 – A BASIC GUI APPLICATION

Lecture 23: PART 1 – NUMPY BASICS

Lecture 24: PART 2 – NUMPY BASICS

Lecture 25: PART 3 – NUMPY BASICS

Lecture 26: PART 4 – NUMPY BASICS

Lecture 27: PART 5 – NUMPY BASICS

Lecture 28: PART 6 – NUMPY BASICS

Lecture 29: PART 7 – NUMPY BASICS

Lecture 30: PART 8 – NUMPY BASICS

Lecture 31: PART 9 – NUMPY BASICS

Lecture 32: PART 10 – NUMPY BASICS

Lecture 33: PART 11 – NUMPY BASICS

Lecture 34: PART 12 – NUMPY BASICS

Lecture 35: PART 13 – NUMPY BASICS

Lecture 36: PART 14 – NUMPY BASICS

Lecture 37: PART 15 – NUMPY BASICS

Lecture 38: PART 16 – NUMPY BASICS

Lecture 39: PART 17 – NUMPY BASICS

Lecture 40: PART 18 – NUMPY BASICS

Lecture 41: PART 19 – NUMPY BASICS

Lecture 42: PART 20 – NUMPY BASICS

Lecture 43: PART 21 – NUMPY BASICS

Lecture 44: PART 22 – NUMPY BASICS

Lecture 45: PART 23 – NUMPY BASICS

Lecture 46: PART 24 – NUMPY BASICS

Lecture 47: PART 25 – NUMPY BASICS

Lecture 48: PART 26 – NUMPY BASICS

Lecture 49: PART 27 – NUMPY BASICS

Lecture 50: PART 28 – NUMPY BASICS

Lecture 51: PART 29 – NUMPY BASICS

Lecture 52: FILE SYSTEM

Lecture 53: FILE SYSTEM WITH STATEMENT

Lecture 54: FILE SYSTEM READ AND WRITE

Lecture 55: PART 1 – RANDOM MODULE BASICS

Lecture 56: PART 2 – RANDOM MODULE BASICS

Lecture 57: PART 3 – RANDOM MODULE BASICS

Lecture 58: PART 4 – RANDOM MODULE BASICS

Lecture 59: PART 5 – RANDOM MODULE BASICS

Lecture 60: PART 6 – RANDOM MODULE BASICS

Lecture 61: PART 7 – RANDOM MODULE BASICS

Lecture 62: PART 1 – PANDAS BASICS

Lecture 63: PART 2 – PANDAS BASICS

Lecture 64: PART 3 – PANDAS BASICS

Lecture 65: PART 4 – PANDAS BASICS

Lecture 66: PART 5 – PANDAS BASICS

Lecture 67: PART 6 – PANDAS BASICS

Lecture 68: PART 7 – PANDAS BASICS

Lecture 69: PART 8 – PANDAS BASICS

Lecture 70: PART 1 – MATPLOTLIB BASICS

Lecture 71: PART 2 – MATPLOTLIB BASICS

Lecture 72: PART 3 – MATPLOTLIB BASICS

Lecture 73: PART 4 – MATPLOTLIB BASICS

Lecture 74: PART 5 – MATPLOTLIB BASICS

Lecture 75: PART 6 – MATPLOTLIB BASICS

Lecture 76: PART 7 – MATPLOTLIB BASICS

Lecture 77: PART 8 – MATPLOTLIB BASICS

Lecture 78: PART 9 – MATPLOTLIB BASICS

Lecture 79: PART 10 – MATPLOTLIB BASICS

Lecture 80: PART 11 – MATPLOTLIB BASICS

Lecture 81: PART 12 – MATPLOTLIB BASICS

Lecture 82: PART 1 – AGE CALCULATOR APP

Lecture 83: PART 2 – AGE CALCULATOR APP

Instructors

Uplatz Training
Fastest growing global Technology & Cloud Training Provider

Rating Distribution

1 stars: 17 votes

2 stars: 12 votes

3 stars: 46 votes

4 stars: 59 votes

5 stars: 74 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!