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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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