Machine Learning with Python (basic to advanced)

A warm welcome to the Machine Learning with Python course by Uplatz.

The Machine Learning with Python course aims to teach students/course participants some of the core ideas in machine learning, data science, and AI that will help them go from a real-world business problem to a first-cut, working, and deployable AI solution to the problem. Our main goal is to enable participants use the skills they acquire in this course to create real-world AI solutions. We'll aim to strike a balance between theory and practice, with a focus on the practical and applied elements of ML.

This Python-based Machine Learning training course is designed to help you grasp the fundamentals of machine learning. It will provide you a thorough knowledge of Machine Learning and how it works. As a Data Scientist or Machine Learning engineer, you'll learn about the relevance of Machine Learning and how to use it in the Python programming language. Machine Learning Algorithms will allow you to automate real-life events. We will explore different practical Machine Learning use cases and practical scenarios at the end of this Machine Learning online course and will build some of them.

In this Machine Learning course, you'll master the fundamentals of machine learning using Python, a popular programming language. Learn about data exploration and machine learning techniques such as supervised and unsupervised learning, regression, and classifications, among others. Experiment with Python and built-in tools like Pandas, Matplotlib, and Scikit-Learn to explore and visualize data. Regression, classification, clustering, and sci-kit learn are all sought-after machine learning abilities to add to your skills and CV. To demonstrate your competence, add fresh projects to your portfolio and obtain a certificate in machine learning.

Machine Learning Certification training in Python will teach you about regression, clustering, decision trees, random forests, Nave Bayes, and Q-Learning, among other machine learning methods. This Machine Learning course will also teach you about statistics, time series, and the many types of machine learning algorithms, such as supervised, unsupervised, and reinforcement algorithms. You'll be solving real-life case studies in media, healthcare, social media, aviation, and human resources throughout the Python Machine Learning Training.

Course Outcomes: After completion of this course, student will be able to:

• Understand about the roles & responsibilities that a Machine Learning Engineer plays

• Python may be used to automate data analysis

• Explain what machine learning is

• Work with data that is updated in real time

• Learn about predictive modelling tools and methodologies

• Discuss machine learning algorithms and how to put them into practice

• Validate the algorithms of machine learning

• Explain what a time series is and how it is linked to other ideas

• Learn how to conduct business in the future while living in the now

• Apply machine learning techniques on real world problem or to develop AI based application

• Analyze and Implement Regression techniques

• Solve and Implement solution of Classification problem

• Understand and implement Unsupervised learning algorithms

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.

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.

Machine Learning - Course Syllabus

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