Post Graduate Program In AI And Machine Learning:- For candidates interested in machine learning, there is a 12-part course called the Machine learning Certification course that aims to make them industry-ready right away. The course offers applicants interactive learning, and they have access to a Jupyter notebook that they can use to practise the material straight after a lesson to help them remember it.
Additionally, candidates are free to set their own pace for learning. A detailed introduction of dealing with real-time data, creating algorithms utilising supervised and unsupervised learning, time series modelling, regression, and classification are all included in the Simplilearn machine learning certification training course. Additionally, there will be more than 25 practical tasks in the course for the candidates. There will be 44 hours of instructor-led instruction in the online classrooms. Businesses can also choose corporate training to educate their staff members.
Post Graduate Program In AI And Machine Learning
Additionally, applicants will learn how to apply the principles learned in a seamless manner through a variety of practise projects and industry projects that address both the theoretical and practical aspects of machine learning.
Students Post Graduate Program In AI And Machine Learning may also work on supplied industrial projects, which require them to develop large-scale machine learning models for organisations like Amazon, Uber, and IDB, in addition to the lab sessions.
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Post Graduate Program In AI And Machine Learning Details
| Article Name | Post Graduate Program In AI And Machine Learning BY Simplilearn , Purdue University |
| Year | 2024 |
| Category | Courses |
| Official site | https://www.simplilearn.com |
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The highlights
- 44 hours of instructor-led training
- Four industry-based course-end projects
- 14 hours of Online self-paced learning
- 58 hours of blended learning
- 100% money back guarantee
Program offerings
- Self-paced learning
- Blended learning
- Industry-based projects
- Hands-on learning
- Interactive classes
- Jupyter notebooks integrated labs
Course and certificate fees
Fees Information
₹ 149,999
(Inclusive of GST)
Details on the Machine Learning Certification cost are provided below.
| Particulars | Course Fee |
| Online Bootcamp | ₹ 1,49,999 |
Certificate Availability
Yes
Certificate Providing Authority
Simplilearn
Eligibility criteria
Skills
Candidates for Simplilearn’s Machine Learning Certification Training Course must possess a fundamental knowledge of college-level mathematics and statistics. Additionally, it is advised that before beginning the course, candidates have a basic understanding of Python programming. Candidates can first finish the Python for Data Science, Statistics necessary for data science, and Math refresher courses to gain this understanding.
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Certification Qualifying Detail
After attending an online classroom, you must complete a full batch of machine learning training and submit one finished project in order to receive the Simplilearn Machine Learning Certification Course.
Complete 85% of the course in offline classes and turn in at least one project.
What you will learn
- Discover decision trees, Naive Bayes, Kernel SVM, random forest classification, and supervised and unsupervised learning methods.
- To lay the groundwork for more complex algorithms and models, use modelling approaches, linear regression, and logistic regression.
- To accurately organise seemingly random, unlabeled data into something comprehensible, use the clustering method.
- Up until the data is fully prepared for the model, become familiar with pre-processing techniques such as data import, data wrangling, data modification, and many more procedures.
- Study Natural Language Processing (NLP), which focuses on the use of natural language in interactions between people and computers.
- Having expertise in simplifying the computing load, perfecting feature engineering, and quickly understanding the data.
- Learn about deep neural networks and deep neural learning by diving into the field.
Who it is for
The best candidates for Simplilearn’s Machine Learning Certification Training Course are professionals who want to expand their horizons by working in the field of machine learning. Some typical profiles are:
- Analytics Manager
- Data Scientist
- Data Analyst
- Web Developers
- IT professionals
- Business Analysts
Admission details
The following information about admissions to the Post Graduate Program In AI And Machine Learning Course classes:
- First of all, Visit the official https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course
- To enrol now, click the button. You’ll be taken to a different page.
- If you have a coupon, enter it here, otherwise just click the Proceed button.
- Please provide your name, email address, and phone number before continuing.
- Pay the charge and keep the receipt for your records.
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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
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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
Evaluation process
To receive the certificate, candidates must complete at least 85% of the course if they chose the self-learning model and must attend at least one complete batch of machine learning training if they chose the online classroom format.
To be eligible for the Machine Learning Certificate, candidates must also submit at least one finished project.
How It Helps
Over the past few years, there has been a discernible shift towards automation and predictive analytics. A staggering 44% growth rate has been recorded for the machine learning market. It is an excellent moment for professionals and students to master the complexities of machine learning because the numbers are expected to increase in the near future.
The advantages of a Machine Learning Certification Training Course also include industry-level projects that can give you a clear understanding of the demands of the sector and the various fields in which Machine Learning can be used. Additionally, candidates will be eligible to apply for job profiles like Machine learning engineer and Data scientist after successfully completing the Simplilearn’s Machine Learning Certification Training Course.
Post Graduate Program In AI And Machine Learning FAQ’S
Which degree is best for AI and ML?
A computer science degree is a common choice since AI is a subdiscipline of computer science. But a data science degree, which also comes with AI skills, may be equally useful because it’s one of the most in-demand.
Can we do Masters in AI and ML?
With a curriculum centered around the field’s fundamental mathematics and concepts, Drexel CCI’s Master’s in Artificial Intelligence and Machine Learning program provides students with the skills they need to thrive in a wide range of artificial intelligence career paths.
What is PG diploma in Machine Learning and AI?
The curriculum of the program covers a wide range of topics, including machine learning algorithms, deep learning, natural language processing, computer vision, data mining, and predictive analytics.
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