About Course

Machine Learning (ML) Course Training in Tirunelveli

Choosing a career in Machine Learning is a smart move for the future. Jaz Academy offers one of the Best Machine Learning courses in Tirunelveli. Unlock your potential with top-notch course In Our Jaz Academy. Get Certified from Best Machine Learning Training Institute Jaz Academy known for Quality Training.

Course Features:

40+ Hours of Instructor-Led Training
5+ Real-World Industry Projects
25+ Hours of Hands-on Coding and Exercises
70+ Hours of Applied Learning
Lifetime Access to Course Materials
Flexible Training Options (Weekdays/Weekends)
Placement Assistance with Resume Building
Capstone Project on Advanced ML Models
Certification of Completion
Tools Covered: Python, TensorFlow, Scikit-Learn, Keras
Access to Cloud Platforms for Model Deployment
24/7 Support for Learning Queries
Doubt-Clearing Sessions with Mentors

Key Skills

Programming Skills
Mathematical and Statistical Knowledge
Data Analysis and Preprocessing
Machine Learning Algorithms
Deep Learning Basics
Model Deployment and Optimization

Advantages in Jaz Academy :

Professional Trainer from IT Industry.
Dedicated Support Team for Training and Development.
Practical Training Program includes Hand On Project Session.

Course Content

Introduction to Machine Learning

What is Machine Learning (ML)?
Types of ML:
Applications of ML in different industries.
Basic ML workflow: Data Collection → Preprocessing → Model Building → Evaluation.

Python for Machine Learning

Data Preprocessing

Supervised Learning

Unsupervised Learning

Neural Networks and Deep Learning

Natural Language Processing (NLP)

Advanced Topics

Tools and Platforms

Capstone Project

Student Ratings & Reviews

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About Course

Machine Learning (ML) Course Training in Tirunelveli

Course Features:

Key Skills

Advantages in Jaz Academy :

Course Content

Introduction to Machine Learning

What is Machine Learning (ML)?

Types of ML:

Applications of ML in different industries.

Basic ML workflow: Data Collection → Preprocessing → Model Building → Evaluation.

Python for Machine Learning

Python Basics for ML:

Introduction to Jupyter Notebooks.

Exploratory Data Analysis (EDA) with Pandas and Visualization Tools.

Data Preprocessing

Data Cleaning:

Feature Scaling

Encoding Categorical Data

Splitting Data: Train-Test Split and Cross-Validation.

Supervised Learning

Linear Regression.

Logistic Regression.

Decision Trees and Random Forests.

Support Vector Machines (SVM).

Evaluation Metrics: Accuracy, Precision, Recall, F1 Score, ROC-AUC

Unsupervised Learning

Clustering Algorithm

Dimensionality Reduction:

Association Rule Learning (e.g., Apriori, Eclat).

Neural Networks and Deep Learning

Introduction to Neural Networks.

Building Neural Networks with TensorFlow/Keras.

Basics of Convolutional Neural Networks (CNNs).

Recurrent Neural Networks (RNNs) and LSTMs.

Natural Language Processing (NLP)

Text Preprocessing

Sentiment Analysis.

Text Classification using Bag of Words and TF-IDF.

Word Embeddings (e.g., Word2Vec, GloVe).

Advanced Topics

Reinforcement Learning Basics.

Transfer Learning.

Time Series Forecasting.

Generative Models (e.g., GANs).

Tools and Platforms

Introduction to Cloud ML Platforms:

ML Ops and Deployment:

Capstone Project

Real-world ML project:

Student Ratings & Reviews