Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based |
Course Overview
Artificial Intelligence and Machine learning both are booming fields that creates multiple opportunities for skilled professionals. Technology has shown its optimistic approach to artificial intelligence, as machines are programmed in a way that they ought to think like humans. On the other hand, Machine learning is also a type of artificial intelligence that allows the software application to become and function more accurately. The International Certification In Artificial Intelligence & Machine Learning is an initiative by the IPL School of Data Science for the learners who wish to grasp comprehensive knowledge of these domains.
International Certification In Artificial Intelligence & Machine Learning training will be delivered by the experts through an online mode. The candidates will be exposed to practical learning along with theoretical knowledge for their overall development. This is a 5 months duration course that comprises 5 courses in it. The course is available on the IPL School of Data Science portal. The online learning portal is also providing multiple benefits for the students to make the learning easier and more interesting. The candidates will also receive International Certification In Artificial Intelligence & Machine Learning by the IPL School of Data Science at the end of the course.
The Highlights
- 5 months duration
- 6-8 hours of learning per week
- 5 embedded courses
- Weekly sessions by faculty
- Access to the laboratories
- Case studies and assignments
- One-to-one mentorship
- Access to career management cell
Programme Offerings
- 5 Months Duration
- Certification
- One-to-one mentorship
- weekly faculty sessions
- Career Assistance
- learning library with toolkits
- hiring talentathons
- CATAPULT data career labs
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | Institute of Product Leadership, Bangalore |
The IPL School of Data Science is allowing fees through credit card, debit card, and internet banking. Interested students can enroll in the course by clicking on the ‘Enroll Now’ button available on the homepage of the website.
Fee structure of International Certification In Artificial Intelligence & Machine Learning
Name of the course | Fee in USD |
International Certification In Artificial Intelligence & Machine Learning | $ 1299 |
Eligibility Criteria
Work Experience
- The candidate enrolling in International Certification In Artificial Intelligence & Machine Learning course should have at least 1 year of working experience in technical or business-related fields. Besides this, the candidates should have experience in programming languages.
Certification Qualifying Details
- International Certification In Artificial Intelligence & Machine Learning is awarded by the IPL School Data Science to all the learners who shall complete the course. Enrolled candidates should complete the projects and assignments on time. The certification will open gates of opportunities in the domain of artificial intelligence and machine learning.
What you will learn
International Certification In Artificial Intelligence & Machine Learning syllabus is vivid and designed to provide proficiency to the learners. The candidates shall learn tools like python, Keras, Jupyter, TensorFlow, SQL, Matplotib, and Kubernetes. The aim of the course is to generate minds that are skilled in the domain of artificial intelligence and machine learning. After the completion of the course, the candidate will gain expertise in the following concepts.
- Python for Machine Learning & Artificial Intelligence,
- Feature extraction,
- Prescriptive & Predictive Analytics,
- Feature Engineering,
- Building a Neural Network,
- Data Wrangling,
- Applying Machine Learning to different problems.
Who it is for
The International Certification In Artificial Intelligence & Machine Learning benefits the people who have one year of working experience in the technical or business-related domain. If the candidate has programming skills it would be easier for him/her to grasp the skills. This course shall help people working on the following designations.
- Software and IT Professionals,
- Domain Experts,
- Engineers,
- Project Managers,
- Business Analysts,
- Product Managers,
- Entrepreneurs,
- Consultants.
Admission Details
To enroll yourself in the International Certification In Artificial Intelligence & Machine Learning online course. Follow the steps mentioned below.
Step 1: Visit the portal of the IPL School of Data Science and search for the course, or directly click on the link to land on the page https://www.productleadership.com/programs/online-ai-ml-certification/.
Step 2: The screen with the detailed introduction of the course shall be displayed on the IPL School of Data Science portal.
Step 3: Click on the tab “Apply Now” and sign up for the course.
Step 4: Complete the enrollment process by paying the International Certification In Artificial Intelligence & Machine Learning fee.
Step 5: Start learning by attending International Certification In Artificial Intelligence & Machine Learning classes.
The Syllabus
Data Analytics Foundations
- Statistical Data Analysis in Excel
- What is statistics?
- Why is statistics relevant to Data Science, Machine Learning and Deep Learning
- Describe Quantitative Data and methods/graphs
- Quantitative description measures
- What is Probability
- Conditional Probability
- How to draw a sample
- Population and Sampling Distributions
- Chi Square tests and Analysis of Variance
- Data Analysis using SQL
- Introduction to DBMS
- ER diagram
- Schema design
- Key constraints & basics of normalization
- Joins
- Subqueries involving joins & aggregations
- Sorting
- Independent subqueries
- Correlated subqueries
- Analytic functions
- Set operations
- Grouping and filtering
- SQL Aggregate & Rank Functions
- SQL Analytics Functions
Python for Artificial Intelligence & Machine Learning
- Introduction to AI & ML
- Introduction to Artificial Intelligence (AI), Data Science (DS), Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP)
- Basics of Data Science: Feature extraction, Feature Engineering, Data Wrangling, Outliers
- Python Programming
- Python Structure, Variables,
- Conditionals, loops
- Functions
- list, dict, tuple, set, bytearray
- Exceptions handling and raising
- Python functions, packages and routines
- Numpy
- Pandas
Machine Learning Foundations
- Introduction to Machine Learning
- What is Machine Learning
- How does it work?
- Supervised Machine Learning
- Unsupervised Machine Learning
- Different Machine Learning algorithms.
- Regression and Classification algorithms
- Use cases of different types of Machine Learning algorithms
- Machine Learning Practice Workouts
- Regression
- What is regression,
- Regression algorithms:
- Simple Linear Regression Statistical method using OLS
- Programming with Simple Linear Regression Statistical method and Statslib
- Regression Algorithm Gradient Descent method (incl derivation of Gradients)
- Programming with Gradient Descent method, Stochastic Gradient Descent, sklearn
- Overfitting/Bias
- Non-Linear Regression
- Programming Assignment on Linear Regression using Gradient Descent Method*
- Logistic Regression
- What is Logistic Regression? Is it regression or classification?
- Learning in Logistic Regression
- Gradient Descent method
- Programming with Statistical Method using Statslib
- Programming with Gradient Method using Native Python
- K Nearest Neighbors
- Reconnecting to Math foundations
- Different Similarity measures (include Cosine Theta)
- KNN Algorithm
- Intro to Sklearn Framework
- Programming and building a KNN model with Sklearn Framework
- Programming and building a KNN model with Native Python
Advanced Artificial Intelligence
- Decision Tree
- What is Decision Tree
- How is it constructed?
- Programming and building a Decision Tree with Sklearn Framework
- Overfitting Problems in Decision Tree
- What is Bagging, Random Forest,
- Hyperparameters in Decision Tree, Random Forest
- Hyperparameter tuning: Programming and building an optimal Decision Tree using Grid Search, Decision Tree, and Random Forest
- Advanced Decision Tree
- Adaboost,
- Gradient Boost,
- XG Boost
- Hyperparameters in Adaboost, Gradient Boosting, XGBoosting
- Hyperparameters tuning: Programming and building an optimal model using Grid Search
- Unsupervised Learning
- Review of Unsupervised Learning
- Clustering
- K Means Clustering
- Apply Machine Learning in different Problems
- How do you solve an ML problem?
- Use different models and choose the best or best combination
- Waterfall or AGILE?
- A sample problem and code
Deep Learning
- Deep Learning Foundations
- Problems solved with deep learning
- What is deep learning
- Intro to Neural Network
- Forward propagation in Neural Network
- How does a Neural Network Learn?
- Review of Gradient Descent used in Lin and Log Regression
- Back Propagation in Neural Network
- Hyperparameters in Neural Network
- Hyperparameter Tuning
- Overfitting in Neural Network
- How to handle Overfitting in Neural Network
- Building a Neural Network
- Introduction to Tensorflow
- Introduction to keras
- Build a simple NN using Keras
- Solve a problem using Keras
- Hyper-parameter tuning using keras
- Choosing right Neural Networks
- Problems of Vanishing Gradient
- Handling Overfitting using Dropouts
- How to handle Vanishing Gradient – Batch Normalization, Skip Connections
- Solve a practical problem using Neural Network and Keras
- Issues with Neural Network