Machine Learning Concepts and Application of ML using Python

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
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, 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!

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 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

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