Machine Learning 360 - II
Learn with Yogesh Parthasarathy
15 modules
English
Lifetime access
Take your machine learning skills to the next level with advanced concepts and real-world applications.
Overview
Embark on an exhilarating journey into the dynamic realm of Machine Learning with our cutting-edge online course, "Machine Learning 360." This immersive, instructor-led program has been meticulously crafted to provide you with a hands-on learning experience, ensuring not only a deep understanding of ML concepts but also active application in real-world scenarios.
đ Course Highlights:
- Comprehensive Coverage: From basic to advanced statistics, Python programming, and in-depth exploration of Machine Learning and deep learning algorithms.
- Practical Application: Gain valuable experience through real-world projects that reinforce your theoretical knowledge.
- Business Intelligence Tools: Master BI tools such as Power BI, Tableau, and Excel to enhance your data visualization and analysis skills.
- Database Management: Learn essential skills in SQL and other database management tools, ensuring proficiency in handling and analyzing data.
- Model Deployment: Acquire expertise in deploying models using cutting-edge technologies like SageMaker, FastAPI, and Docker.
- Placement Preparation: Benefit from an intensive guide designed to prepare you for job interviews, providing the confidence to excel in recruitment processes.
đ What Sets Us Apart:
- Hands-On Learning: Engage in practical exercises and projects to reinforce theoretical concepts.
- Expert Instructors: Learn from industry professionals with extensive experience in the field.
- Placement Support: Our course includes a comprehensive guide to help you navigate and succeed in job interviews.
Whether you are a beginner or looking to enhance your skills, "Machine Learning 360" is your gateway to mastering the intricacies of Machine Learning. Join us and unlock a world of opportunities in the ever-evolving field of data science. Enroll now to transform your career! đđĄ
Key Highlights
Advanced concepts and techniques
Hands-on projects and case studies
Comprehensive coverage of machine learning algorithms
Practical applications and real-world examples
What you will learn
Master advanced machine learning concepts
Explore advanced topics like ensemble methods, deep learning, and reinforcement learning to enhance your understanding of machine learning techniques.
Hands-on experience with projects
Apply your knowledge to real-world projects and gain practical experience in implementing machine learning algorithms and analyzing complex data sets.
In-depth coverage of algorithms
Gain a deep understanding of various machine learning algorithms such as support vector machines, decision trees, clustering algorithms, and neural networks.
Real-world applications and case studies
Learn how machine learning is applied in various industries with the help of case studies and practical examples.
Modules
Foundation - Python Basics
8 attachments
Introduction to Python for Machine Learning
Data Structures in Python
Functions and Modules
File Handling in Python
Python Libraries for Machine Learning
Applying Python to Machine Learning Tasks
Best Practices and Optimization
Data Visualization
Data Preprocessing
5 attachments
Data cleaning
Coming Soon
Handling missing data
Coming Soon
Feature selection
Coming Soon
Feature scaling
Coming Soon
Data transformation
Coming Soon
Foundations - Basic Statistics
9 attachments
Introduction to Statistics
Recording Introduction To Statistics 1 - 64cdf568e4b0793e4cf7bfc3
Descriptive Statistics
Probability Basics
Inferential Statistics
Statistical Techniques for Machine Learning
ANOVA (Analysis of Variance)
Feature Scaling and Transformation
Optimization Techniques
Bi tools
10 attachments
Introduction to Data Visualization
Excel for Data Analysis
Power BI Essentials
Tableau Basics
Integrating Data Visualization with Machine Learning
Real-world Projects
Best Practices and Optimization
Integration with Machine Learning
Data Import and Transformation
Advanced Features
Databases
8 attachments
Fundamentals of SQL
SQL for Data Preprocessing
Structured Query Language Basics
Retrieving Data with SQL
Data Manipulation with SQL
Advanced SQL Queries
Best Practices and Optimization
Real-World SQL Projects
Introduction to Machine Learning
5 attachments
Definition and basic concepts
Coming Soon
Types of machine learning algorithms
Coming Soon
Supervised learning
Coming Soon
Unsupervised learning
Coming Soon
Reinforcement learning
Coming Soon
Regression
3 attachments
Linear regression
Coming Soon
Multiple regression
Coming Soon
Evaluation and interpretation of regression models
Coming Soon
Classification
5 attachments
Binary classification
Coming Soon
Multiclass classification
Coming Soon
Decision trees
Coming Soon
Support vector machines
Coming Soon
Evaluation and interpretation of classification models
Coming Soon
Clustering
4 attachments
K-means clustering
Coming Soon
Hierarchical clustering
Coming Soon
Density-based clustering
Coming Soon
Evaluation of clustering results
Coming Soon
Dimensionality Reduction
2 attachments
Principal Component Analysis (PCA)
Coming Soon
Evaluation of dimensionality reduction techniques
Coming Soon
Ensemble Learning
4 attachments
Bagging and random forests
Coming Soon
Boosting algorithms
Coming Soon
Stacking models
Coming Soon
Evaluation of ensemble models
Coming Soon
Model Evaluation and Selection
5 attachments
Cross-validation
Coming Soon
Performance metrics for classification
Coming Soon
Performance metrics for regression
Coming Soon
Hyperparameter tuning
Coming Soon
Overfitting and underfitting
Coming Soon
Introduction to Deep Learning
4 attachments
Neural networks
Coming Soon
Convolutional neural networks
Coming Soon
Recurrent neural networks
Coming Soon
Transfer learning
Coming Soon
Advanced Topics in Machine Learning
3 attachments
Time series analysis
Coming Soon
Imbalanced data handling
Coming Soon
Model deployment
Coming Soon
Introduction to FastAPI
6 attachments
Building API Endpoints
Integrating Machine Learning Models
Asynchronous Programming with FastAPI
Deployment and Dockerization
Real-World FastAPI Projects
Best Practices and Optimization
FAQs
How can I enrol in a course?
Enrolling in a course is simple! Just browse through our website, select the course you're interested in, and click on the "Enrol Now" button. Follow the prompts to complete the enrolment process, and you'll gain immediate access to the course materials.
Can I access the course materials on any device?
Yes, our platform is designed to be accessible on various devices, including computers, laptops, tablets, and smartphones. You can access the course materials anytime, anywhere, as long as you have an internet connection.
How can I access the course materials?
Once you enrol in a course, you will gain access to a dedicated online learning platform. All course materials, including video lessons, lecture notes, and supplementary resources, can be accessed conveniently through the platform at any time.
Can I interact with the instructor during the course?
Absolutely! we are committed to providing an engaging and interactive learning experience. You will have opportunities to interact with them through our community. Take full advantage to enhance your understanding and gain insights directly from the expert.
About the creator
Learn with Yogesh Parthasarathy
I am a seasoned data scientist, adept at the art of transmuting raw data into actionable insights. Armed with a master's degree in statistics and boasting a wealth of over 8 years' hands-on experience, my trajectory in the data science sphere has been marked by multifariousness.
Along with my tenure as a data savant, bestowing the bounty of my wisdom and experiences upon burgeoning data scientists has emerged as an extraordinarily gratifying facet of my professional odyssey.
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