Machine Learning with Python

Machine Learning with Python

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Course Description

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. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.

What you will learn

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include:

  • Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
  • Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
  • Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

What you will get

  • Guaranteed Internship
  • Internship letter from Verzeo after successful completion of Projects.
  • Microsoft Technology Associate Completion Certificate on Python.
  • Internship offers from different tied up companies based on your performance.
  • MTA certificates signed by the CEO of Microsoft, Satya Nadella.
  • Opportunity to be taught by Certified Professionals.
  • Opportunity to work on Major and Minor Projects mentored by these Corporate Trainers.
  • Gaining access to exclusive course content, etc., as part of the course.
  • Total of 2 projects, one major and one minor will be completed by the students under Mentor supervision.

Curriculum


  • Difference Between Python 2 and Python 3
  • Print function and strings
  • Math function and programming basics.
  • Variables and Loops introduction
  • Loops detailed
  • Functions and Function Parameters
  • Global and Local Variables
  • Packages and Modules with PIP
  • Writing/Reading/Appending to a file
  • Common pythonic errors
  • Getting user Input
  • Stats with python
  • Module Import
  • List and Multidimensional lists
  • Reading from CSV
  • Multi Line Print
  • Dictionaries
  • Built in functions
  • Built in Modules
  • Regular expression
  • cx freeze
  • Matplotlib intro
  • Numpy
  • Introduction to pandas
  • pandas basics
  • concatenating and appending dataframes.
  • Joining and merging dataframes.
  • What is Machine Learning
  • Difference between a rule based algorithm and a machine learning algorithm.
  • Supervised vs Unsupervised learning.
  • Classification vs Regression
  • Practical Machine Learning
  • Training and testing Data
  • features and labels
  • pickling and scaling
  • Linear Regression
  • Forecasting and prediction using regression
  • Logistic Regression
  • K-NN classification
  • Support Vector Machines
  • K-Means Clustering
  • Random Forest
  • Implementation of all the algorithms using SKlearn.
  • Introduction to NLTK
  • Stopwords
  • Stemming
  • Lemmetization
  • Named entity recognition
  • Text classification
  • Sentiment analysis