Machine Learning & Data Science : Python Practical Hands-on - Code, Develop, Validate & Deploy Machine Learning Models. Become a Champion with Machine Learning Algorithms.
- Created by Abilash Nair
- English [Auto]
What you'll learn
- Machine Learning Algorithms such as Supervised & Unsupervised algorithms
- It will provide you with ability to execute Regression and Classification Algorithms
- Create Machine Learning Models - Practical Sessions
- KNN & K- Means Algorithm based model creation and validation accuracy determination
- Cross Validation & Random Sampling
- Data Processing with Pandas & Python
- Feature Engineering & Data Pre-Processing
- Working with Multiple Data Sets and Algorithm building in Kaggle Cloud.
Interested in the field of Machine Learning? Then this course is for you!
Designed & Crafted by AI Solution Expert with 15 + years of relevant and hands on experience into Training , Coaching and Development.
Complete Hands-on AI Model Development with Python.
Course Contents are:
Fundamentals of Machine Learning
Machine learning project Life Cycle
Supervised & Unsupervised Learning
Data Sampling and Cross Validation
Model Training and Validation
K -Nearest Neighbor Algorithm
K- Means Algorithm
Visualization using Seaborn
You will be trained to develop various algorithms for supervised & unsupervised methods such as KNN , K-Means , Random Forest, XGBoost model development.
Understanding the fundamentals and core concepts of machine learning model building process with validation and accuracy metric calculation. Determining the optimum model and algorithm.
Cross validation and sampling methods would be understood.
Data processing concepts with practical guidance and code examples provided through the course.
Feature Engineering as critical machine learning process would be explained in easy to understand and yet effective manner.
We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.
Who this course is for:
- Data Science Begineers
- Researchers & PhD Scholars
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