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Career Path - Artificial Intelligence and Machine Learning Engineer

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M G Road 1, Bangalore

Course ID: 369807

M G Road 1, Bangalore

Students Interested 0 (Seats Left 0)

₹ 5,500

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About the Course

Career Path - Artificial Intelligence and Machine Learning Engineer – Course Syllabus

 

Machine Learning Basics –Course Syllabus

 

 

Introduction:

Getting Started with Machine Learning
An Introduction to Machine Learning
What is Machine Learning?
Introduction to Data in Machine Learning
Demystifying Machine Learning
ML – Applications
Best Python libraries for Machine Learning
Artificial Intelligence | An Introduction
Machine Learning and Artificial Intelligence
Difference between Machine learning and Artificial Intelligence
Agents in Artificial Intelligence
10 Basic Machine Learning Interview Questions

 

Data and It’s Processing:

Introduction to Data in Machine Learning
Understanding Data Processing
Python | Create Test DataSets using Sklearn
Python | Generate test datasets for Machine learning
Python | Data Preprocessing in Python
Data Cleansing
Feature Scaling – Part 1
Feature Scaling – Part 2
Python | Label Encoding of datasets
Python | One Hot Encoding of datasets
Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python

 

Supervised learning :

Getting started with Classification
Basic Concept of Classification
Types of Regression Techniques
Classification vs Regression
ML | Types of Learning – Supervised Learning
Multiclass classification using scikit-learn

 

 

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

 

 

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

 

Python | Implementation of Polynomial Regression

 

Softmax Regressionusing TensorFlow

 

Logistic Regression:

Understanding Logistic Regression
Why Logistic Regression in Classification?
Logistic Regression using Python
Cost function in Logistic Regression
Logistic Regression using Tensorflow

 

Naive BayesClassifiers

 

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

 

Decision Tree:

Decision Tree
Decision Tree Regression using sklearn
Decision Tree Introduction with example
Decision tree implementation using Python
Decision Tree in Software Engineering

 

Random Forest:

Random Forest Regression in Python
Ensemble Classifier
Voting Classifier using Sklearn
Bagging classifier

 

 

 

Machine Learning using Python– Course Syllabus

 

1.           Introduction to Machine Learning

What is a Machine Learning?
Need for Machine Learning
Why & When to Make Machines Learn?
Challenges in Machines Learning
Application of Machine Learning

 

2.           Types of Machine Learning

·                     Supervised

·                     Unsupervised

·                     Reinforcement

 

3.           Components of Python ML Ecco system

Using Pre-packaged Python Distribution: Anaconda
Jupyter Notebook
NumPy
Pandas

 

4.           Regression Analysis (I)

Regression Analysis
Linear Regression
Examples on Linear Regression
scikit-learn library to implement simple linear regression

 

5.           Regression Analysis (II)

Multiple Linear Regression
Examples on Multiple Linear Regression
Polynomial Regression
Examples on Polynomial Regression

 

6.           Classification (I)

What is Classification
Classification Terminologies in Machine Learning
Types of Learner in Classification
Logistic Regression
Example on Logistic Regression

 

7.           Classification (II)

What is KNN?
How does the KNN algorithm work?
How do you decide the number of neighbours in KNN?
Implementation of KNN classifier
What is a Decision Tree?
Implementation of Decision Tree

 

8.           Clustering (I)

What is Clustering?
Applications of Clustering
Clustering Algorithms
K-Means Clustering
How does K-Means Clustering work?

 

 

9.           Clustering (II)

Hierarchical Clustering
Agglomerative Hierarchical clustering and how does it work
Woking of Dendrogram in Hierarchical clustering
Implementation of Agglomerative Hierarchical Clustering

 

10.        Association Rule Learning

Association Rule Learning
Apriori algorithm
Working of Apriori algorithm
Implementation of Apriori algorithm

 

11.        Recommender Systems

Introduction to Recommender Systems
Content-based Filtering
How Content-based Filtering work
Collaborative Filtering
Implementation of Movie Recommender System

 

 

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