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