Udemy Machine Learning Concepts and Application of ML using Python

Learn core concepts of Machine Learning. Apply ML techniques to real-world problems and develop AI/ML based applications

What you'll 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

Requirements

• Enthusiasm and determination to make your mark on the world!

Description
Uplatz offers this in-depth course on Machine Learning concepts and implementing machine learning with Python.

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.

Course Outcomes: After completion of this course, student will be able to:
1. Apply machine learning techniques on real world problem or to develop AI based application
2. Analyze and Implement Regression techniques
3. Solve and Implement solution of Classification problem
4. Understand and implement Unsupervised learning algorithms

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.


Detailed Syllabus of Machine Learning Course

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

Who this course is for:

• 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