Machine Learning Concepts and Application of ML using Python

Machine Learning Concepts and Application of ML using Python 16.5 GB

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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