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:
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Understand about the roles & responsibilities that a Machine Learning Engineer plays
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Python may be used to automate data analysis
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Explain what machine learning is
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Work with data that is updated in real time
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Learn about predictive modelling tools and methodologies
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Discuss machine learning algorithms and how to put them into practice
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Validate the algorithms of machine learning
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Explain what a time series is and how it is linked to other ideas
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Learn how to conduct business in the future while living in the now
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Apply machine learning techniques on real world problem or to develop AI based application
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Analyze and Implement Regression techniques
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Solve and Implement solution of Classification problem
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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
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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.
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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.
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Types of Machine Learning
Supervised Learning, Unsupervised Learning, Reinforcement Learning. Machine Learning Lifecycle.
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Supervised Learning : Classification and Regression
Classification: K-Nearest Neighbor, Decision Trees, Regression: Model Representation, Linear Regression.
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Unsupervised and Reinforcement Learning
Clustering:K-Means Clustering, Hierarchical clustering, Density-Based Clustering.
Machine Learning – Course Syllabus
1. Linear Algebra
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Basics of Linear Algebra
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Applying Linear Algebra to solve problems
2. Python Programming
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Introduction to Python
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Python data types
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Python operators
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Advanced data types
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Writing simple Python program
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Python conditional statements
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Python looping statements
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Break and Continue keywords in Python
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Functions in Python
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Function arguments and Function required arguments
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Default arguments
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Variable arguments
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Build-in functions
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Scope of variables
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Python Math module
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Python Matplotlib module
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Building basic GUI application
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NumPy basics
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File system
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File system with statement
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File system with read and write
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Random module basics
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Pandas basics
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Matplotlib basics
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Building Age Calculator app
3. Machine Learning Basics
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Get introduced to Machine Learning basics
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Machine Learning basics in detail
4. Types of Machine Learning
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Get introduced to Machine Learning types
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Types of Machine Learning in detail
5. Multiple Regression
6. KNN Algorithm
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KNN intro
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KNN algorithm
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Introduction to Confusion Matrix
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Splitting dataset using TRAINTESTSPLIT
7. Decision Trees
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Introduction to Decision Tree
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Decision Tree algorithms
8. Unsupervised Learning
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Introduction to Unsupervised Learning
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Unsupervised Learning algorithms
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Applying Unsupervised Learning
9. AHC Algorithm
10. K-means Clustering
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Introduction to K-means clustering
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K-means clustering algorithms in detail
11. DBSCAN
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Introduction to DBSCAN algorithm
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Understand DBSCAN algorithm in detail
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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
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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
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