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Post Graduate Program In AI And Machine Learning
The Machine learning Certification course educates the candidates about machine learning topics like pre-processing techniques, statistics, mathematics.
Online
11 Months
₹ 149,999
Inclusive of GST
Quick facts
particular | details | ||
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Collaborators
IBM
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Medium of instructions
English
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Mode of learning
Self study, Virtual Classroom
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Mode of Delivery
Video and Text Based
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Course overview
The Machine learning Certification 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 Certification Training 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.
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
Machine Learning Certification fee details have been mentioned below.
Particulars | Course Fee |
Online Bootcamp | ₹ 1,49,999 |
certificate availability
certificate providing authority
Eligibility criteria
Skills
Machine Learning Certification Training 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 Certification 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
- 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
Who it is for
Machine Learning Certification Training 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
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.
The syllabus
Course Introduction
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
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 Certification Training 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 Certification Training 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
The Machine Learning Certificate has lifelong validity.
A certified Machine Learning professional can work as a Data Scientist or Machine Learning Engineer.
The Machine Learning Certification 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.
Machine Learning is an implementation of AI, which allows an electronic system to learn and improve simultaneously without explicit programming.
You can work as a Machine Learning Engineer in companies like Google, Microsoft, Amazon, Oracle, Accenture, and more after completing Machine Learning Certification Training Course by Simplilearn.
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.