Learn core concepts of Machine Learning. Apply ML techniques to real-world problems and develop AI/ML based applications
Platform: Udemy
Status: Available
Duration: 63.5 Hours
Price: $129.99 $0.00
Note: Udemy FREE coupon codes are valid for maximum 3 days only. Look for "Get Coupon" orange button at the end of Description.
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!
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
