Interested in of Machine Learning? Then this course is for you! This course has been designed for aspirants of machine learning where you learn can learn complex theory, algorithms and coding libraries in a simple way. We take you step-by-step into the World of Machine Learning. During every session, you will develop new skills and improve your understanding of this challenging sub-field of Data Science. During this course, we will dive deep into Machine Learning.
It will involve the steps like Data Preprocessing, Regression, Clustering, Association Rule Learning, Reinforcement Learning, Deep Learning, Dimensionality Reduction, and Model Selection & Boosting. Moreover, we have packed this course with practical exercises which are based on real-life examples. You will also get some hands-on practice by building your own models. And in addition, this course includes both Python learning.
Introduction and Overview of Machine Learning Concepts, History of Machine Learning
Applications of Machine Learning .
Types of Machine Learning :Supervised ,Unsupervised and Semi Supervised Learning Techniques
Loops and conditional statements.
Supervised vs Unsupervised Learning
Data Analysis: Ways for the data analysis , Benefits of Data Analysis ,Examples.
Data Visualization: Scatter plot, Line chart ,Bar Chart ,Pie Chart ,Hexbin map ,Histogram, Heat Map .
Introduction to Python.
Basic Python Programming.
Dictionaries and Tuples.
Functions & Modules.
Object - Oriented Programming.
Exceptions and Files.
Regular Expressions.
Python libraries suitable for Machine Learning .
Classification.
K-Nearest Neighbours.
Association analysis using Apriori algorithm.
Decision Trees.
Support Vector Machine.
Normal Distribution.
Binomial Distribution.
Variance and Covariance.