**Machine
Learning Concepts and Application of ML using Python**

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

**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 a primary objective of this course. This a 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**

·

Introduction of Python for ML, Python
modules for ML, Dataset, Apply Algorithms on datasets, Result Analysis from
dataset, Future Scope of ML.

·

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.

·

Supervised Learning, Unsupervised Learning,
Reinforcement Learning. Machine Learning Lifecycle.

·

Classification: K-Nearest Neighbor,
Decision Trees, Regression: Model Representation, Linear Regression.

·

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 a system with reading and writing

· 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

**What you Learn from this Course?**

- 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 a 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 can Enroll in this
Course?**

- 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

**What will Requirement for this Course?**

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

**How can Learn Course
this Course?**

1.
**Create
Account / Login on Udemy.com**

2.
**Learn
Course by Enroll in this Course **

** **

**Coupon Code for This Course: ****ML_FULL_UPLATZ**

**Hurry Up!**

** Coupon Code Valid for
Limited Time**

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