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
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
2. Python Programming
Introduction to Python
Python data types
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
Scope of variables
Python Math module
Python Matplotlib module
Building basic GUI application
File system with statement
File system with read and write
Random module basics
Building Age Calculator app
3. Machine Learning Basics
4. Types of Machine Learning
5. Multiple Regression
6. KNN Algorithm
7. Decision Trees
8. Unsupervised Learning
Introduction to Unsupervised Learning
Unsupervised Learning algorithms
Applying Unsupervised Learning
9. AHC Algorithm
10. K-means Clustering
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