Machine Learning using Python Certification Training

Machine Learning using Python Certification Training

Machine Learning is an application of Artificial Intelligence which provides system the capability of learning automatically and improve from previous experience without being programmed explicitly. It basically emphasis on the development of computer programs that can access data. Zarantech offers Machine Learning with Python Certification which helps you to obtain the expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning.
Start Date Duration Time (CST) Type Mode of Training Enroll
17-Mar-2019 55 Hrs 09:00 PM Online INSTRUCTOR LED TRAINING Enquiry Now


Humans learn from experience and machines learn from data. Machine learning is the science of teaching computers to perform certain tasks. The algorithm learns with data without being explicitly programmed. We use it almost everywhere without actually knowing that machine learning is behind it. In this course, you will learn about some of the widely used algorithms and the methodologies for implementing them. Anybody with an elementary knowledge of statistics and basic programming skills is fit to take this course. With this course, you can develop your skills in using machine learning algorithms to solve real-world problems.

Why should you update your resume with Machine Learning Skills? 2.5 quintillion bytes of data are created every day and it is expected to grow at a staggering pace in the days to come! Machine learning was always just around the corner since generations, however, from past few decades, with the steep growth in technology, it has been the topmost priority. That is why, according to Gartner’s survey of Indian CIO’s, leading companies are willing to commit more than a third of their budget towards digital transformations involving data analytics and cloud infrastructures. India’s IT sector is all set to grow by at least 50% over the next couple of years, with at least 180,000 more jobs expectancy in the current year alone.


  1.  Basic programming skills
  2.  Basic high school mathematics – Linear Algebra, Probability theory, basic calculus and basic statistics.

Who should attend this Training?

Following professionals can enroll into Machine Learning and Artificial Intelligence course:

  1. Analytics professionals
  2.  Data Science professionals
  3.  Software professionals interested to switch their career into field of analytics
  4.  Graduates looking to start career in Machine Learning and Data Science
  5.  Other professionals working with online customer based organizations

Course Objective

After completion of this course, trainee will:
  1.  Python for Data Science (python, Numpy Pandas, Scikit Learn and Matplotlib).
  2.  Fundamentals of Machine Learning
  3.  Building and training Machine Learning Algorithm

Prepare for Certification!

Our training and certification program gives you a solid understanding of the key topics covered on the Machine Learning and Artificial Intelligence Certification. In addition to boosting your income potential, getting certified in Machine Learning demonstrates your knowledge of the skills necessary to be an effective Professional. The certification validates your ability to produce reliable, high-quality results with increased efficiency and consistency.

Module 1

Unit I: Machine Learning Introduction

  1. Introduction to ML problems
  2. ML terminologies
  3. ML project workflow
  4. ML real life examples

Unit II: Jupyter Notebook introduction

  1. Working with Jupyter notebooks
  2. Markdown and Code blocks
  3. Keyboard shortcuts

Unit III: Python Basics

  1. Python syntax
  2. Basic data types
  3. Basic data structures

Unit IV: Python advanced

  1. Numpy Arrays
  2. Plotting using Matplotlib
  3. Pandas Dataframes
  4. Introduction to Scikit Learn package

Module 2

Unit I: Regression Modeling

  1. Introduction
  2. Modeling concept
  3. Example problem - Housing price

Unit II: Simple Linear Regression

  1. Error metric - SSE, MSE, R Squared
  2. Least Square algorithm
  3. Gradient Descent Algorithm
  4. Implementation using scikit-learn

Unit III: Multiple Linear Regression

  1. Dummy variables
  2. Error metric - SSE, MSE, R Squared
  3. Gradient Descent Algorithm
  4. Feature Selection (Incremental)
  5. Implementation using scikit-learn

Unit IV: Polynomial Regression

  1. Non-linear relationship
  2. Higher order terms
  3. Feature selection
  4. Modeling concepts - Avoid overfitting
  5. Implementation using scikit-learn

Module 3

Unit I: Classification Modeling

  1. Introduction to Classification Models
  2. Error Metrics : Accuracy Score
  3. Confusion Matrix
  4. Type1 and Type 2 errors
  5. Decision boundaries

Unit II: Logistic Regression

  1. Discrete outcomes
  2. Logit function
  3. Probability scores
  4. Implementation using scikit-learn

Unit III: Support Vector Machines

  1. Support Vectors
  2. Decision boundary
  3. Kernel trick
  4. Hyperparameters and Model tuning
  5. Implementation using scikit-learn

Unit IV: Decision Trees

  1. Entropy
  2. Using Entropy in classification
  3. Information Gain
  4. Tree pruning
  5. Implementation using scikit-learn

Unit V: Random Forests

  1. Bias variance errors
  2. Ensembling
  3. Randomness in Random Forest
  4. Hyperparameters
  5. Implementation using scikit-learn

Module 4

Unit I: Cluster Modeling

  1. Introduction to clustering
  2. Distance measures
  3. Error metrics
  4. Analysing cluster outputs

Unit II: Hierarchical Clustering

  1. Agglomerative method
  2. Divisive method
  3. Understanding Dendrogram
  4. Cutting the dendrogram for obtaining clusters
  5. Implementation using scikit-learn

Unit III: K-Means Clustering

  1. Distance measures
  2. Centroids and their importance
  3. Steps involved in K-Means
  4. Local optima problem
  5. Implementation using scikit-learn

We do not have a standard certification process neither are we affiliated with any university for certifying our course. We provide only the course attendance/completion certificate.

+How do you provide training?
Generic Training FAQs ZaranTech provides Role-based Instructor Led Live and Self Paced Video Training and certification programs by industry expert trainer’s using Online meeting tools like Citrix GotoWebinar.
+What is the difference between live training, video training and self-paced video training?
We offer Three different modes of training – Click on the below links to know the differences between live training, video training, self-paced video training:
  1. Instructor Led Live Training (ILLT) –
  2. Instructor Led Video Training (ILVT) –
  3. Self-paced Video Training (SPVT) –
+What are the Technical Requirement for taking the Online Live training?
  1. Operating System: Windows XP or newer
  2. Browser: Internet Explorer 6.x or newer
  3. CPU: P350 MHz, recommended P500+ MHz
  4. Memory: 128 MB, recommended 256+ MB RAM
  5. Free Disk Space: 40 MB, recommended 200+ MB for content and recordings
  6. Internet Connection: 28.8 Kbps, recommended 128+ Kbps
  7. Monitor: 16 bit colors (high color)
  8. Other: Sound card, microphone, and speakers OR headset with microphone
+What internet speed is required to attend the LIVE session?
1Mbps of internet speed is recommended to attend the live classes. Even with lesser internet speed students can attend live session.