Machine Learning

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.

Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI.

In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.

Course Content:

• Python for Machine Learning
• Programming Basics
• Python Data Types
• Structures and conditional statements
• Python core packages
• Introduction to Jupyter Notebook
• Introduction to Numpy and Pandas
• Data filtering and selecting
• Find duplicates and treating missing values
• Concatenate and transform data
• Setting up and installations
• Installing python
• Setting up Python environment for development
• Installation of Jupyter Notebook
• How to access our course material
• Write your first program in python
•  Python object and data structures operations
• Introduction to Python objects
• Number objects and operations
• Variable assignment and keywords
• String objects and operations
• Print formatting with strings
•  Python statements
• Introduction to Python statements
• If, else-if and else statements
• Comparison operators
• Chained comparison operators
• What are loops?
• For loops
• While loops
• File and exception handling
• Process files using python
• Read/write and append file object
• File functions
• File pointer and operations
• Introduction to error handling
• Try, except and finally
• Basic Statistics for Machine Learning
• Basic Statistics and Exploratory Analysis
• Descriptive summary statistics with Numpy
• Summarize continous and categorical data
• Outlier analysis
• Introduction to Machine Learning
• Overview of Supervised and Unsupervised Machine Learning
• Linear Regression
• Clustering with K-means
• Naive Bayes Classification
• Introduction to Neural Networks
•  Data Processing for Machine Learning
• Advanced Data Mugging
• Outlier Analysis
• Treating for missing values
• Normalization vs Standardization of data
•  Machine Learning Algorithms
• Supervised Machine Learning algorithms
• K-Nearest Neighbors (KNN) concept and application
• Naive Bayes concept and application
• Logistic Regression concept and application
• Classification Trees concept and application
• Unsupervised Machine Learning algorithms
• Clustering with K-means concept and application
• Hierarchial Clustering concept and application
•  Building and Training Machine Learning models
• Setting up the project with ML workflow.
• Data Preprocessing and statistical exploration
• Building , Training and evaluation of Machine Learning Model