Machine Learning

Training Mode Regular Fastrack Crash
Classroom | Online 12 Months

(M,W,F or T,T,S Class)

(3 Class in a week)

6 Months

(Monday to Friday Class)

(5 Class in a week)

4 Months

(Monday to Friday Class)

(5 Class in a week 1:30 hour duration)

Embark on a transformative journey into the realm of artificial intelligence with the Advance Machine Learning Course at Next G Classes. This comprehensive 12-month program is meticulously designed to equip students with the profound knowledge and practical skills required to excel in the dynamic field of machine learning.

Introduction :
1. Getting Started with Machine Learning
2. An Introduction to Machine Learning
3. What is Machine Learning ?
4. Introduction to Data in Machine Learning
5. Demystifying Machine Learning
6. ML – Applications
7. Best Python libraries for Machine Learning
8. Artificial Intelligence | An Introduction
9. Machine Learning and Artificial Intelligence
10. Difference between Machine learning and Artificial Intelligence
11. Agents in Artificial Intelligence
12. 10 Basic Machine Learning Interview Questions


Data and It’s Processing:
1. Introduction to Data in Machine Learning
2. Understanding Data Processing
3. Python | Create Test DataSets using Sklearn
4. Python | Generate test datasets for Machine learning
5. Python | Data Preprocessing in Python
6. Data Cleansing
7. Feature Scaling – Part 1
8. Feature Scaling – Part 2
9. Python | Label Encoding of datasets
10. Python | One Hot Encoding of datasets
11. Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python


Supervised learning :

1. Getting started with Classification
2. Basic Concept of Classification
3. Types of Regression Techniques
4. Classification vs Regression
5. ML | Types of Learning – Supervised Learning
6. Multiclass classification using scikit-learn
7. Gradient Descent :
• Gradient Descent algorithm and its variants
• Stochastic Gradient Descent (SGD)
• Mini-Batch Gradient Descent with Python
• Optimization techniques for Gradient Descent
• Introduction to Momentum-based Gradient Optimizer

8. Linear Regression :
• Introduction to Linear Regression
• Gradient Descent in Linear Regression
• Mathematical explanation for Linear Regression working
• Normal Equation in Linear Regression
• Linear Regression (Python Implementation)
• Simple Linear-Regression using R
• Univariate Linear Regression in Python
• Multiple Linear Regression using Python
• Multiple Linear Regression using R
• Locally weighted Linear Regression
• Python | Linear Regression using sklearn
• Linear Regression Using Tensorflow
• A Practical approach to Simple Linear Regression using R
• Linear Regression using PyTorch
• Pyspark | Linear regression using Apache MLlib
• ML | Boston Housing Kaggle Challenge with Linear Regression

9. Python | Implementation of Polynomial Regression

10. Softmax Regression using TensorFlow

11. Logistic Regression :
• Understanding Logistic Regression
• Why Logistic Regression in Classification ?
• Logistic Regression using Python
• Cost function in Logistic Regression
• Logistic Regression using Tensorflow

12. Naive Bayes Classifiers

13. Support Vector:
• Support Vector Machines(SVMs) in Python
• SVM Hyperparameter Tuning using GridSearchCV
• Support Vector Machines(SVMs) in R
• Using SVM to perform classification on a non-linear dataset

14. Decision Tree:
• Decision Tree
• Decision Tree Regression using sklearn
• Decision Tree Introduction with example
• Decision tree implementation using Python
• Decision Tree in Software Engineering

15. Random Forest:
• Random Forest Regression in Python
• Ensemble Classifier
• Voting Classifier using Sklearn
• Bagging classifier

Contact Us

Course Feedback

View More Testimonials

Student Projects

View More Projects


Enjoy a free demo session in both classroom and online with live instructor with us before join any course. This helps you to understand our instructor and the atmosphere of our institute. To attend a demo session just give us call or fill enquiry from or email us on: [email protected].
We at Next-G Classes enables you to pay your course fees in Installments through a simple and Hassle free process. You can discuss your installments at the time of registration and pursue your dreams. Our installments process is totally interest free, we don’t charge any extra charge for same.
We always believe on quality training that’s why we have put limited batch size. Because we often feel that some students prefer small batch size. Our limited batch size provides personal attention, better results, enhance learning, focus on learning and many more also.
Our Instructors are highly professional. All our instructors are passionate about delivering student achievement and learning outcomes. Next-G classes is one of few institutes in all across country that’s aim is to provide high quality learning experience.
One year free class retake facility provides an opportunity to retake class at No Cost as per your convenience. Because at our institute our aim is to enhance the concepts of every student’s, after provide in-depth knowledge of every software’s, languages etc.
One year free class retake facility provides an opportunity to retake class at No Cost as per your convenience. Because at our institute our aim is to enhance the concepts of every student’s, after provide in-depth knowledge of every software’s, languages etc.

Master IT Courses

Other Courses

Trusted by our Students

More than 1000 students we have trained in last 8 years placed successfully in various Industry.

    WDI Student review

Request For Demo