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
A warm welcome to the Machine Learning using Python: A Comprehensive 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 GUIapplication
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
KNNalgorithm
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. AHCAlgorithm
10. K-means Clustering
Introduction to K-means clustering
K-means clustering algorithms in detail
11. DBSCAN
Introduction to DBSCAN algorithm
Understand DBSCANalgorithm in detail
DBSCANprogram
