Courses
Level: 1
Age group: All
This course Includes
16 live tutoring sessions
90 topics
Access on mobile
Certificate of completion
Understanding of the concept of Machine learning
Proficiency in Regression algorithm and Metrics
Ability to work on Supervised Learning with Real Life Scenario
Skill in Time Series Modelling, Ensemble Learning and Recommender Systems
Mastery in types of Machine Learning
90 topics
Introduction to Machine Learning
Explanation of machine Learning
Relationship between Artificial Intelligence, Machine Learning, and Data Science
Definition and Features of Machine Learning
Machine Learning Approaches
Supervised Learning
Supervised Learning
Supervised Learning: Real Life Scenario
Understanding the Algorithm
Supervised Learning Flow
Types of Supervised Learning
Types of Classification Algorithms
Regression
Types of Regression Algorithms
Regression Use Case
Accuracy Metrics
Cost Function
Evaluating Coefficients
Demo: Linear Regression
Challenges in Prediction
Types of Regression Algorithms: Part II
Example as Bigmart
Logistic Regression
Sigmoid Probability
Accuracy Matrix
Demo: Survival of Titanic Passengers
Classification
Overview of Classification
Classification: A Supervised Learning Algorithm
Use Cases
Classification Algorithms
Performance Measures: Confusion Matrix
Performance Measures: Cost Matrix
Naive Bayes Classifier
Steps to Calculate Posterior Probability
Support Vector Machines: Linear Separability
Support Vector Machines: Classification Margin
Linear SVM: Mathematical Representation
Non linear SVMs
The Kernel Trick
Decision Trees and Random Forest
Decision Tree: Classifier
Decision Tree: Examples
Decision Tree: Formation
Choosing the Classifier
Overfitting of Decision Trees
Random Forest Classifier Bagging and Bootstrapping
Example of Linear Regression
Unsupervised Learning
Overview of Unsupervised Learning
Example and Applications of Unsupervised Learning
Clustering
Hierarchical Clustering
Hierarchical Clustering: Example
K-means Clustering
Optimal Number of Clusters
Examples of Cluster Based Incentivization
Time Series Modeling
Overview of Time Series Modeling
Time Series Pattern Types
White Noise
Stationarity
Removal of Non Stationarity
Demo: Air Passengers I
Time Series Models
Steps in Time Series Forecasting
Demo: Air Passengers II
Ensemble Learning
Overview of Ensemble Learning
Ensemble Learning Methods: Part I and Part II
Working of AdaBoost
AdaBoost Algorithm and Flowchart
Gradient Boosting
XGBoost
XGBoost Parameters: Part I and Part II
Demo: Pima Indians Diabetes
Model Selection
Common Splitting Strategies
Demo: Cross Validation
Recommender Systems
Introduction to Recommender Systems
Purposes of Recommender Systems
Paradigms of Recommender Systems
Collaborative Filtering: Part I and II
Association Rule: Generation Apriori Algorithm
Apriori Algorithm Example: Part I and II
Apriori Algorithm: Rule Selection
Demo: User Movie Recommendation Model
Association Rule: Mining Market Basket Analysis
Association Rule: Mining
Here's why more and more people are joining EnthuZiastic
Customize your lessons to meet your individual goals.
Top rated teachers to guide you through the learning process.
Attend classes anytime, anywhere. Make your own schedule.
Gain experience in organizing live events such as 'Show What You Know,' competitions, and cultural celebrations to develop self-assurance and confidence.
A compassionate support team to listen to your needs.
You will get a certificate for the completion of the course.
Enroll for the course of your liking by selecting 1:1 or group classes. Choose the type of instructor you want to learn with.
Download Enthu app and schedule classes for the day and time that works best for you. You own your learning schedule.
Join classes on Zoom and start learning with lessons customized for you. Make most of our student success program.
knowledge of Mathematics and programming language will be helpful
Basic knowledge of Statistics and modeling
Conceptual knowledge of Data Science
A PC or Laptop with internet connection
Python software should be installed
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Who can join this course?
All enthuziast who are analytics managers, information architects, developers, or graduates willing to become the ML engineer can join this course.
Why join this course?
If you are looking for a course that provides you in-depth overview of machine learning topics including the use of Python in machine learning and drawing predictions from the data, this course is the right place to start with.
What can I expect after this course?
By the end of this course, you will get proficiency in working with real-time data. You will be ready to start your career as the Machine Learning Engineer.
Are these classes Live?
Yes, all our classes are conducted online. These classes are no different than taking up face-to-face, physical chess training. Taking up online lessons help you save significant time, money, and energy.
How will I be able to join classes?
You will need to download Enthu app. It will have all your classes and all your schedule in one place. You can join classes directly from the app (both mobile and desktop app are available). You will get regular reminder for classes on the app so that you never forget a class. App can also be used to share feedback for each session.
How many students will there be in a class?
We have 1:1 as well as n:1 classes. You can make your choice when enrolling in the course. For group classes, we take only small batches not exceeding 5 students at a time.
What if I miss a class?
In the case of 1:1 class, you can easily reschedule your class with the help of support team or through the app. In the case of group classes, class recording will be available to you in the Enthu app. You can watch them and get any doubts you have answered in the next class.
Do you follow a curriculum?
Yes, our curriculum has been designed by our teachers with more than a decade of experience in the respective subject. It is a tried and tested curriculum to help you uncover your best.
What are the privacy steps followed by you for these classes?
We take utmost care to ensure privacy during our online live sessions. Only verified students can join the sessions. This makes our sessions completely safe and secure. We do not share this personal information with any third parties.
Whom to reach out if I need any help during session or otherwise?
You can reach out to support team on Enthu App or email at support@enthu.com
What is your cancellation policy?
You can cancel your enrollment by emailing at support@enthu.com within 5 working days of your registration.
What is your refund policy?
We offer 30 Day Money Back Guarantee. You can ask for refund by emailing at support@enthu.com.
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