Description

What is Machine Learning?

Machine Learning is the field of study that empowers machines to learn without explicit programming. ML is one of the most innovative developments one’s ever seen.

ML Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using Python and R. Data Science Training encompasses a conceptual understanding of Statistics, Text Mining and an introduction to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR.

Course Outcome: –

On completion of this course, the students will be able to

Understand the basic concepts of ML.

Understand how to use different python libraries in ML.

Work with different Regression and Classification Algorithms.

Perform syntax and semantics in ML with Python.

Ability to design and analyse various ML algorithms.

Apply different concepts of ML in other application areas.

Session 1 Introduction:–

What is Machine Learning?

Applications of Machine Learning

ML Types of Learning- Supervised vs Unsupervised Learning

Python libraries suitable for Machine Learning

Session 2 Data Pre-processing:–

Missing Value

Categorical Data

Splitting Dataset

Feature Scaling

Session 3 Regression:–

Introduction to Regression

Types of Regression

Simple Linear Regression

Multiple Linear Regression

Polynomial Regression

Decision Tree Regression

Random Forest Regression

Session 4 Classification:–

Basic Concept of Classification

Classification vs Regression

Logistic Regression

K-NN

SVM

Kernel SVM

Naive Bayes

Decision Tree Classification

Random Forest Classification

Session 5 Clustering: –

K-Means

Hierarchical Clustering

Session 6 Natural Language Processing: –

NLTK installation

Tokenization

Case Study: Document Classification

Session 7 Deep Learning:–

Artificial Neural Networks

Convolutional Neural Networks

Session 8 Dimensionality Reduction:–

Introduction to Dimensionality Reduction

Principal Component Analysis (PCA)

LDA

Kernel PCA

Prerequisites: –

1.Basic of any programming knowledge but Python is preferred.

2.The student should have good logical and reasoning skill.

Duration & Timings :

Duration – 30 Hours.

Training Type: Online Live Interactive Session.

Faculty: Experienced.

Weekday Session – Mon – Thu 8:30 PM – 10:30 PM (EST)– 4 Weeks. December 9, 2024.

Weekend Session – Sat & Sun 9:30 AM to 12:30 PM (EST) – 5 Weeks. January 4, 2025.

USA: +1 734 418 2465 ( Call Only )

WhatsApp

Email : info@learntek.org

MACHINE LEARNING REVIEWS

Nitin, Thank you for all your help in teaching and training our brains with Machine Learning. Your teaching aid and methodology used was excellent such as derivations and definitions first and implementation next, navigated students like me not loosing focus and developed interest and created thirst for continued learning. Your deep knowledge in deep learning, you are like a machine in Machine Learning and naturally talented in Natural Language Processing are the observations and evidences from me during the training.

Anand

Nitin is an excellent speaker and he has deep knowledge on Machine Learning and other subsidiaries.

Nagesh

I have successfully completed the Machine Learning using Python course. Nitin (Course Instructor) has sound knowledge and has provided structured and detailed content. He has explained the difficult concepts in a very simplified way with suitable case studies, and examples.I highly recommend this course to others.Thank you – Nitin and Anand .

Sanjeev