Machine Learning with Python

Machine learning is another core part of AI. Machine learning allows computers to find hidden insights without being explicitly programmed where to look.

Increasing demand for machine learning in today’s world

We have a variety of data available around us which is easily available and very powerful in providing more accurate predictions. With all this available data it’s possible to quickly and automatically produce figures & models that can analyze bigger, more complex data and deliver faster, more accurate results, even on a very large scale. The result of this is high-value predictions at a very high speed that can guide better decisions and smart actions in real time without human intervention.

The goal of machine learning is to create an accurate model based off of past data then use that model to predict future events. 

Who should attend?

  • Programmers
  • Anyone with an engineering background
  • Computer Science Graduates
  • Software developers

Course Outline- Machine Learning with Python-50 Hours

Getting Started

  • Data Science- The big picture
  • What is Machine Learning? and possibilities
  • Key Concepts
  • Supervised/Unsupervised machine learning
  • Real life examples
  • Linear Algebra Refresher
  • Statistics and Probability Refresher
  • Course Overview
  • Python tool: Anaconda and how to install- Spyder, Jupyter Notebook

Python for Data Science

  • Basic Python
  • Data Structures
  • Functions and Packages
  • Numpy
  • Pandas
  • Control Flow
  • Data visualization with Matplotlib and other libraries
  • Scikit-learn: machine learning in Python

Data Preprocessing

  • Getting the data and importing libraries
  • Data types
  • Determining Features
  • Manipulating Data
  • Data Munging
  • Exploring Data
  • Data Transformation
  • Cleaning Data
  • Splitting the Dataset into the Training and Test sets
  • Mini Project 1

Data Modeling: Regression

  • Multiple Linear Regression- Least Squares
  • Ploynomial Regression
  • Decision Tree Regression
  • Evaluating Regression Models
  • Mini Project 2

Data Modeling: Classification

  • Logistic Regression
  • K-Nearest Neighbors
  • Support Vector Machine (SVM)
  • Evaluating Classification Models
  • Mini Project 3

Data Modeling: Classification (Cont)

  • Naive Bayes
  • Decision Tree
  • Ensemble Learning
  • Random Forest
  • Mini Project 3 cont.

Data Modeling: Clustering and Association Rule

  •  K-Means Clustering
  • Feature Scaling
  • Apriori Algorithm
  • Mini Project 4

Practical Topics in Machine Learning

  • Dealing with Real-World Data
  • The problem of over fitting and under fitting
  • Regularized Regression
  • Model Selection
  • Final Project Presentations 1

Advanced Topics in Machine Learning

  • Natural Language Processing
  • Basic Text Processing
  • Word Tokenization
  • Text Classification
  • Deep Learning and Neural Networks
  • Artificial and Convolational Neural Networks
  • Final Project Presentations 2

 

 

 

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