Machine Learning Concepts and Application of ML using Python [Free Online Course] - TechCracked

Learn core concepts of Machine Learning. Apply ML techniques to real-world problems and develop AI/ML based applications

This course includes:

• 63.5 hours on-demand video
• 3 downloadable resources
• Full lifetime access
• Access on mobile and TV
• Certificate of completion

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

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.

Also See : Artificial Intelligence in App Creation: Beginners Edition

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