Course Description

Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. 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.

Machine Learning143

What You Will Learn

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.

Watch our "5 min Quick Introduction"


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

What you will get

Guaranteed Internship

  • Internship letter from Verzeo

  • Guaranteed Internship offers from tied up companies
Certified Professionals

  • Opportunity to be taught by Certified Professionals.

  • Opportunity to work on Major and Minor Projects mentored by the Corporate Trainers.
Projects

  • 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.
Case Studies and Applications

  • Apply learning algorithms to building smart robots, text understanding, computer vision, medical informatics, audio, database mining, and other areas.
Certification
  • Microsoft Technology Associate Completion Certificate on Python.
  • MTA certificates signed by the CEO of Microsoft, Satya Nadella.
  • Internship Certificate from Verzeo

Online Classroom

₹11,000

₹5,500 (50% OFF)

₹11000

  1. Online Portal

  2. Mentorship & Placement Mobilizer

  3. Access to Webinars

  4. Projects & Guranteed Internships

  5. Certification from Industry Partners

Hybrid Classroom

₹12,998

₹6,499 (50% OFF)

₹12998

  1. Duration: Offline: 7 days
    Online: 12 Month Access

  2. Placement Mobilizer

  3. Access to Webinars

  4. Projects and Guranteed Internships

  5. Certification from Industry Partners



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