Machine Learning Project Guidelines

A complete guide for developing ML projects with a well-defined methodology and best practices.


What you'll learn
A deeper understanding of the 11 stages involved in developing and implementing ML projects
Best practices to be followed while doing ML projects
Building a template that you can use for your future ML projects
Guidelines to Select Evaluation Metrics
Guidelines to choose ML algorithms to solve specific problem(s)
How you can visually compare the performances of ML models and select the best-performing model?
What is data leakage and how to detect, prevent, and minimize it?
Importance of converting business problems into analytical problems before building ML models
How to understand datasets using Exploratory Data Analysis using various tools?
Detailed approach to Data preprocessing
How do various Regression and Classification algorithms (Linear, Non-linear, and Ensembles) and Clustering algorithms (K-Means and RFM Analysis) work?
How to use various ML algorithms such as Linear Regression, Logistic Regression, Gaussian Naïve Bayes, K-Nearest Neighbors, and Support Vector Machines?
How to use Decision Trees, Random Forest, Gradient Boosting, Extreme Gradient Boosting, K-Means? and so on
How to apply ML algorithms in Python using Scikit-learn, XGBoost, and other ML libraries?
How to perform Error Analysis and Troubleshoot Prediction Errors?
How to tune Hyperparameters to improve Model Performances?
How to build appealing visualization using Matplotlib, Seaborn, and Plotly?
And, much more



Requirements
Must have:
• Fundamentals of computer science and programming
• High school-level basic mathematics
Good to have:
• Basic Python programming
• Basics of Linear Algebra
• Basics of Statistics
• Basics of Probability Theory
• Basics of Object-Oriented Programming (OOPs)

Description
This course is designed by an industry expert who has over 2 decades of IT industry experience including 1.5 decades of project/ program management experience, and over a decade of experience in independent study and research in the fields of Machine Learning and Data Science.
The course will equip students with a solid understanding of the theory and practical skills necessary to work with machine learning algorithms and models.
This course is designed based on a whitepaper and the book “Machine Learning Project Guidelines” written by the author of this course.
When building a high-performing ML model, it’s not just about how many algorithms you know; instead, it’s about how well you use what you already know.
You will also learn that:
There is NO single best algorithm that would work well for all predictive modeling problems
And, the factors that determine which algorithm to choose for what type of problem(s)
Even simple algorithms may outperform complex algorithms if you know how to handle model errors and refine the models through hyperparameter tuning
Throughout the course, I have used appealing visualization and animations to explain the concepts so that you understand them without any ambiguity.
This course contains 13 sections:
Introduction
Business Understanding
Data Understanding
Research
Data Preprocessing
Model Development
Model Training
Model Refinement
Model Evaluation
Final Model Selection
Model Validation & Model Deployment
ML Projects Hands-on
ML Project Template Building
ML Project 1 (Classification)
ML Project 2 (Regression)
ML Project 3 (Classification)
ML Project 4 (Clustering - KMeans)
ML Project 5 (Clustering – RFM Analysis)
13. Congratulatory and Closing Note
This course includes 48 lectures, 17 hands-on sessions, and 29 downloadable assets.
By the end of this course, I am confident that you will outperform in your job interviews much better than those who have not taken this course, for sure.
Who this course is for:
Beginners with little programming experience and basic mathematics
Experienced programmers who want to pursue a career in ML/ Data Science/ AI
People who have already taken other Machine Learning courses who want to strengthen their skills further and use a well-defined methodology in ML projects with best practices using a standardized project template

More Info