** Machine Learning Concepts and Application of ML using Python 16.5 GB**

Genre: eLearning | Language: English + .srt | Duration: 160 lectures (63h 24m) | Size: 16.5 GB

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, MatDescriptionlib, 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 MatDescriptionlib module

Building basic GUI application

NumPy basics

File system

File system with statement

File system with read and write

Random module basics

Pandas basics

MatDescriptionlib 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

MachLearn-ConcepandApplic

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