Post Graduate Program In AI And Machine Learning by Simplilearn Purdue University, West Lafayette: Fee, Duration, How to Apply

Quick Facts

particular	details
Collaborators IBM	Medium of instructions English	Mode of learning Self study, Virtual Classroom	Mode of Delivery Video and Text Based

Course overview

The Machine Learning using Python course is a 12-part course for candidates interested in Machine learning, as it strives to make the candidates industry-ready from day one. The course provides interactive learning for the candidates, with Jupyter notebook available to them, which they can use for practicing right after a session to solidify the concepts.

Candidates also have the freedom to learn at their own pace. Machine Learning using Python Course by Simplilearn offers an in-depth overview of working with real-time data, developing algorithms using supervised & unsupervised learning, time series modelling, regression, and classification. The course will also contain more than 25 hands-on exercises for the candidates. The online classrooms will consist of 44 hours of instructor-led training. There is also an option for corporate training so that businesses can train their employees.

Moreover, candidates will learn to seamlessly apply the concepts learnt with the array of practice and industry projects, which will take care of both the theoretical and practical nuances of Machine learning. Apart from the Lab sessions, students can also work with industry projects provided, where they’ll need to design industry-scale machine learning models for companies like Amazon, Uber, and IDB.

Course and certificate fees

Fees information

₹ 149,999 (Inclusive of GST)

Machine Learning Certification fee details have been mentioned below.

Particulars	Course Fee
Online Bootcamp	₹ 1,49,999

certificate availability

Yes

certificate providing authority

Simplilearn

Who it is for

Data analyst Data scientist Business analyst

Machine Learning using Python Course by Simplilearn is best suited for professionals looking to widen their horizons by entering into the field of machine learning. Some common profiles include:

Analytics Manager
Data Scientist
Data Analyst
Web Developers
IT professionals
Business Analysts

Eligibility criteria

Skills

Machine Learning using Python Course by Simplilearn will require the candidates to have a basic understanding of college-level statistics and mathematics. It is also recommended that the candidates should have some idea about the basics of Python programming before starting the course. To get this knowledge, candidates can first complete the Python for Data Science, and Statistics essential for data science, and Math refresher courses.

Certification Qualifying Detail

To get the Machine Learning using Python Course by Simplilearn, after attending an online classroom, you need to attend a complete batch of Machine Learning training and submit one completed project.

For offline classes, complete 85% of the course and submit at least one project.

What you will learn

Machine learning Knowledge of artificial intelligence Knowledge of deep learning

Learn about unsupervised and supervised learning algorithms along with random forest classification, Naive Bayes, Kernel SVM, and decision trees
Use modelling techniques, linear, and logistic regression to build a foundation for the more advanced algorithms and models
Use the clustering algorithm to accurately group seemingly random, unlabelled data, into something legible
Learn to use pre-processing concepts like importing data, data wrangling, data manipulation, and many more routines until the data is finally ready for the model
Learn Natural Language Processing (NLP) which deals with the interaction between humans and computers using the natural language
With proficiency in mastering feature engineering, reducing the computational load, and making sense of the data with ease
Dive into the world of deep learning, and understand deep neural networks and deep neural learning

The syllabus

Course Introduction

Course Introduction
Accessing Practice Lab

Introduction to AI and Machine Learning

Learning Objectives
The emergence of Artificial Intelligence
Artificial Intelligence in Practice
Sci-Fi Movies with the Concept of AI
Recommender Systems
Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A
Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part B
Definition and Features of Machine Learning
Machine Learning Approaches
Machine Learning Techniques
Applications of Machine Learning: Part A
Applications of Machine Learning: Part B
Key Takeaways
Knowledge Check

Data Pre-processing

Learning Objectives
Data Exploration Loading Files: Part A
Data Exploration Loading Files: Part B
Demo: Importing and Storing Data
Practice: Automobile Data Exploration - A
Data Exploration Techniques: Part A
Data Exploration Techniques: Part B
Seaborn
Demo: Correlation Analysis
Practice: Automobile Data Exploration - B
Data Wrangling
Missing Values in a Dataset
Outlier Values in a Dataset
Demo: Outlier and Missing Value Treatment
Practice: Data Exploration - C
Data Manipulation
Functionalities of Data Object in Python: Part A
Functionalities of Data Object in Python: Part B
Different Types of Joins
Typecasting
Demo: Labor Hours Comparison
Practice: Data Manipulation
Key Takeaways
Knowledge Check
Storing Test Results

Supervised Learning

Learning Objectives
Supervised Learning
Supervised Learning- Real-Life Scenario
Understanding the Algorithm
Supervised Learning Flow
Types of Supervised Learning: Part A
Types of Supervised Learning: Part B
Types of Classification Algorithms
Types of Regression Algorithms: Part A
Regression Use Case
Accuracy Metrics
Cost Function
Evaluating Coefficients
Demo: Linear Regression
Practice: Boston Homes - A
Challenges in Prediction
Types of Regression Algorithms: Part B
Demo: Bigmart
Practice: Boston Homes - B
Logistic Regression: Part A
Logistic Regression: Part B
Sigmoid Probability
Accuracy Matrix
Demo: Survival of Titanic Passengers
Practice: Iris Species
Key Takeaways
Knowledge Check
Health Insurance Cost

Feature Engineering

Learning Objectives
Feature Selection
Regression
Factor Analysis
Factor Analysis Process
Principal Component Analysis (PCA)
First Principal Component
Eigenvalues and PCA
Demo: Feature Reduction
Practice: PCA Transformation
Linear Discriminant Analysis
Maximum Separable Line
Find Maximum Separable Line
Demo: Labeled Feature Reduction
Practice: LDA Transformation
Key Takeaways
Knowledge Check
Simplifying Cancer Treatment

Supervised Learning: Classification

Learning Objectives
Overview of Classification
Classification: A Supervised Learning Algorithm
Use Cases of Classification
Classification Algorithms
Decision Tree Classifier
Decision Tree Examples
Decision Tree Formation
Choosing the Classifier
Overfitting of Decision Trees
Random Forest Classifier- Bagging and Bootstrapping
Decision Tree and Random Forest Classifier
Performance Measures: Confusion Matrix
Performance Measures: Cost Matrix
Demo: Horse Survival
Practice: Loan Risk Analysis
Naive Bayes Classifier
Steps to Calculate Posterior Probability: Part A
Steps to Calculate Posterior Probability: Part B
Support Vector Machines: Linear Separability
Support Vector Machines: Classification Margin
Linear SVM: Mathematical Representation
Non-linear SVMs
The Kernel Trick
Demo: Voice Classification
Practice: College Classification
Key Takeaways
Knowledge Check
Classify Kinematic Data

Unsupervised Learning

Learning Objectives
Overview
Example and Applications of Unsupervised Learning
Clustering
Hierarchical Clustering
Hierarchical Clustering Example
Demo: Clustering Animals
Practice: Customer Segmentation
K-means Clustering
Optimal Number of Clusters
Demo: Cluster Based Incentivization
Practice: Image Segmentation
Key Takeaways
Knowledge Check
Clustering Image Data

Time Series Modelling

Learning Objectives
Overview of Time Series Modeling
Time Series Pattern Types: Part A
Time Series Pattern Types: Part B
White Noise
Stationarity
Removal of Non-Stationarity
Demo: Air Passengers - A
Practice: Beer Production - A
Time Series Models: Part A
Time Series Models: Part B
Time Series Models: Part C
Steps in Time Series Forecasting
Demo: Air Passengers - B
Practice: Beer Production - B
Key Takeaways
Knowledge Check
IMF Commodity Price Forecast

Ensemble Learning

Ensemble Learning
Overview
Ensemble Learning Methods: Part A
Ensemble Learning Methods: Part B
Working of AdaBoost
AdaBoost Algorithm and Flowchart
Gradient Boosting
XGBoost
XGBoost Parameters: Part A
XGBoost Parameters: Part B
Demo: Pima Indians Diabetes
Practice: Linearly Separable Species
Model Selection
Common Splitting Strategies
Demo: Cross Validation
Practice: Model Selection
Key Takeaways
Knowledge Check
Tuning Classifier Model with XGBoost

Recommender Systems

Learning Objectives
Introduction
Purposes of Recommender Systems
Paradigms of Recommender Systems
Collaborative Filtering: Part A
Collaborative Filtering: Part B
Association Rule Mining
Association Rule Mining: Market Basket Analysis
Association Rule Generation: Apriori Algorithm
Apriori Algorithm Example: Part A
Apriori Algorithm Example: Part B
Apriori Algorithm: Rule Selection
Demo: User-Movie Recommendation Model
Practice: Movie-Movie recommendation
Key Takeaways
Knowledge Check
Book Rental Recommendation

Text Mining

Learning Objectives
Overview of Text Mining
Significance of Text Mining
Applications of Text Mining
Natural Language Toolkit Library
Text Extraction and Preprocessing: Tokenization
Text Extraction and Preprocessing: N-grams
Text Extraction and Preprocessing: Stop Word Removal
Text Extraction and Preprocessing: Stemming
Text Extraction and Preprocessing: Lemmatization
Text Extraction and Preprocessing: POS Tagging
Text Extraction and Preprocessing: Named Entity Recognition
NLP Process Workflow
Demo: Processing Brown Corpus
Wiki Corpus
Structuring Sentences: Syntax
Rendering Syntax Trees
Structuring Sentences: Chunking and Chunk Parsing
NP and VP Chunk and Parser
Structuring Sentences: Chinking
Context-Free Grammar (CFG)
Demo: Structuring Sentences
Practice: Airline Sentiment
Key Takeaways
Knowledge Check
FIFA World Cup

Project Highlights

Project Highlights
Uber Fare Prediction
Amazon - Employee Access

Admission details

Filling the form

Here are the Machine Learning Certification Course classes admission details:

Step 1 - Visit the official https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course

Step 2 - Click on Enroll now button. You will redirect to a new page

Step 3 - Apply a coupon if you have or directly click on the Proceed button.

Step 5 - Fill in the details such as name, email, and contact number and proceed

Step 6- Pay the fee and save the receipt for the future.

Evaluation process

Candidates must attend at least one complete batch of Machine Learning training if they’ve opted for the online classroom model and complete at least 85% of the course if they’ve opted for self-learning model to get the certificate

The candidates must also submit at least one completed project to avail the Machine Learning Certificate.

How it helps

There has been a visible shift towards automation and predictive analytics in these past few years. The Machine learning market has clocked in a growth rate of a whopping 44%. With the numbers likely to rise in the foreseeable future, it is a great time for professionals and students to learn the nuances of Machine learning.

Machine Learning using Python Course benefits also include industry-level projects that can help you get a clear view of what is demanded by the industry and the many areas one can apply Machine Learning in. Moreover, candidates, upon successful completion of the Machine Learning using Python Course by Simplilearn, will be able to apply for job profiles like Machine learning engineer and Data scientist.

Instructors

Mr Venkata N Inukollu
Assistant Professor
Purdue University, W...

Other Masters, Ph.D

FAQs

How long is the Machine Learning Certification online course valid for?

The Machine Learning Certificate has lifelong validity.

What are the job profiles for certified Machine Learning professionals?

A certified Machine Learning professional can work as a Data Scientist or Machine Learning Engineer.

How can I become a certified Machine learning Engineer?

The Machine Learning using Python course will provide the candidates with an overview of various machine learning techniques and methodologies and the certification will establish the candidates as Machine Learning Engineers.

What is Machine Learning?

Machine Learning is an implementation of AI, which allows an electronic system to learn and improve simultaneously without explicit programming.

Which companies hire Machine Learning experts?

You can work as a Machine Learning Engineer in companies like Google, Microsoft, Amazon, Oracle, Accenture, and more after completing Machine Learning using Python Course by Simplilearn.

What are the prerequisites for the Machine Learning Certification Course?

This course will require the candidates to have a basic understanding of college level statistics and mathematics. It also recommended that the candidates should have some idea about the basics of Python programming before starting the course.

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Post Graduate Program In AI And Machine Learning

Online

11 Months

₹ 149999