# Advance Machine Learning Course

> Master machine learning in 12 months with Next G Classes' comprehensive program covering fundamentals, advanced algorithms, deep learning, NLP, and real-world applications

- Institute: Next-G Classes, Plot No 4, Kh. No. 59/13, First Floor, Pal Colony, Rithala, Near Rithala Metro Station, Delhi - 110085
- Web page: https://nextgclasses.com/advance-machine-learning-course
- Duration: Regular 12 Months · Fastrack 6 Months · Crash 4 Months
- Mode: Classroom and Live Online · Certificate · Live projects · Placement assistance
- Enquire: +91-7503301775 · nextgclasses@gmail.com

Embark on a transformative journey into the realm of artificial intelligence with the Advance Machine Learning Course at[**Next G Classes**](https://nextgclasses.com/). 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.

## Syllabus

**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