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
Requirements
Understanding of Python programming
Description
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....
Who this course is for:
Executives, software developers, Analysts who are in the transition of evolving career of Machine learning
Students to jumpstart in exciting and lucrative future of Machine learning
Managers to upgrade their skills in this demanding Machine learning field
Academics for understanding core concepts of Machine learning

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