Machine Learning Concepts and Application of ML using Python
Learn core concepts of Machine Learning. Apply ML techniques to realworld problems and develop AI/ML based applications
Rating: 0.0 out of 5.0
language: EnglishDuration:63.5 hours
3 downloadable resources
Full lifetime access
Access on mobile and TV
Platform: udemy
$129.99
Requirement
 Enthusiasm and determination to make your mark on the world!
The course targeted for:
 Machine Learning Engineers & Artificial Intelligence EngineersData Scientists & Data EngineersNewbies and Beginners aspiring for a career in Data Science and Machine LearningMachine Learning SMEs & SpecialistsAnyone (with or without data background) who wants to become a top ML engineer and/or Data ScientistData Analysts and Data ConsultantsData Visualization and Business Intelligence Developers/AnalystsCEOs, CTOs, CMOs of any size organizationsSoftware Programmers and Application DevelopersSenior Machine Learning and Simulation EngineersMachine Learning Researchers – NLP, Python, Deep LearningDeep Learning and Machine Learning enthusiastsMachine Learning SpecialistsMachine Learning Research Engineers – Healthcare, Retail, any sectorPython Developers, Machine Learning, IOT, AirFlow, MLflow, KubefComputer Vision / Deep Learning Engineers – Python
what you will learn:

Learn the AZ of Machine Learning from scratchBuild your career in Machine Learning, Deep Learning, and Data ScienceBecome a top Machine Learning engineerCore concepts of various Machine Learning methodsMathematical concepts and algorithms used in Machine Learning techniquesSolve real world problems using Machine LearningDevelop new applications based on Machine LearningApply machine learning techniques on real world problem or to develop AI based applicationAnalyze and implement Regression techniquesLinear Algebra basicsAZ of Python Programming and its application in Machine LearningPython programs, Matplotlib, NumPy, basic GUI applicationFile system, Random module, PandasBuild Age Calculator app using PythonMachine Learning basicsTypes of Machine Learning and their application in reallife scenariosSupervised Learning – Classification and RegressionMultiple RegressionKNN algorithm, Decision Tree algorithmsUnsupervised Learning concepts & algorithmsAHC algorithmKmeans clustering & DBSCAN algorithm and programSolve and implement solutions of Classification problemUnderstand and implement Unsupervised Learning algorithmsShow moreShow less
Description
Uplatz offers this indepth 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: KNearest Neighbor, Decision Trees, Regression: Model Representation, Linear Regression.
 Unsupervised and Reinforcement Learning
Clustering: KMeans Clustering, Hierarchical clustering, DensityBased 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
 Buildin 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. Kmeans Clustering
 Introduction to Kmeans clustering
 Kmeans clustering algorithms in detail
11. DBSCAN
 Introduction to DBSCAN algorithm
 Understand DBSCAN algorithm in detail
 DBSCAN program
Instructor
Created by Uplatz Training
3.9 Rating for Instructor
5,429 Reviews for the courses
235,286 Students Enrolled
79 Number of courses
Coupon Code: ML_FULL_UPLATZ