A beginners guide to learn Machine Learning (including Hands-on projects - From Basic to Advance Level)
What you'll learn
- Learn how to use NumPy, to do fast mathematical calculations in machine learning.
- Learn what is Machine Learning and Data Wrangling in machine learning.
- Learn how to use scikit-learn for data-preprocessing in machine learning.
- Learn different model selection and feature selections techniques in machine learning.
- Learn about cluster analysis and anomaly detection in machine learning.
- Learn about SVMs for classification, regression and outliers detection in machine learning.
- Learn about the Numpy and the Pandas library.
Requirements
- Basic knowledge of scripting and programming
- Basic knowledge of python programming
Description
If you are looking to start your career in Machine learning then this is the course for you.
This is a course designed in such a way that you will learn all the concepts of machine learning right from basic to advanced levels.
This course has 5 parts as given below:
Introduction & Data Wrangling in machine learning
Linear Models, Trees & Preprocessing in machine learning
Model Evaluation, Feature Selection & Pipelining in machine learning
Bayes, Nearest Neighbors & Clustering in machine learning
SVM, Anomalies, Imbalanced Classes, Ensemble Methods in machine learning
For the code explained in each lecture, you can find a GitHub link in the resources section.
Who's teaching you in this course?
I am Professional Trainer and consultant for Languages C, C++, Python, Java, Scala, Big Data Technologies - PySpark, Spark using Scala Machine Learning & Deep Learning- sci-kit-learn, TensorFlow, TFLearn, Keras, h2o and delivered at corporates like GE, SCIO Health Analytics, Impetus, IBM Bangalore & Hyderabad, Redbus, Schnider, JP Morgan - Singapore & HongKong, CISCO, Flipkart, MindTree, DataGenic, CTS - Chennai, HappiestMinds, Mphasis, Hexaware, Kabbage. I have shared my knowledge that will guide you to understand the holistic approach towards ML.
Author(s): EdYoda Digital University
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