Learning Supervised & Unsupervised ML algorithms and implementation in Python
What you'll learn:
-> Context of machine learning in today’s business world
-> Understanding machine learning life cycle
-> Techniques used in various phases of ML life cycle
-> Core understanding of ML algorithms
-> Implementation of ML algorithms
-> Model interpretation & optimization
Yes, you are exploring the right course in the exciting field of machine learning.
Let us find the reasons in this course – Why to learn ML?
Let us find the path of ML learning – What to learn in ML?
Let us find the way of ML learning – How to learn ML?
In my 28 years of experience in software field, machine learning is one of my most exciting techno- managerial area to work and teach. In my opinion this skill will be the need of most of the business stake holders in every field. Machine learning is the core component of Artificial Intelligence and Data Science.
That’s why, in this course we will be learning core concepts of various algorithms in in simple language. You need good understanding of algorithms/models for correct implementation of it. Also, that will help in effective Optimization, Interpretation and Communication of the output of the model to various stake holders.
In this course, you will understand the various steps of model implementation in Python.
This course lectures consists of many supervised and unsupervised algorithms like Regression, Logistic regression, KNN, SVM, Naïve Bayes, Decision Tree, Random Forest, K-Means, Hierarchical clustering, etc. with core concepts and Python implementation of various ML life cycle.
So are you thrilled…..then why are you waiting for…. Let us explore this course….