Data Science Training in BTM Layout, Bangalore

Best Data Science Training in BTM Layout with Placement Assistance


Data Science Training

TecMax is one of the leading Data Science Training Institute in Bangalore. Certified experts at TecMax are real-time consultants at multinational companies and have more than 5+ years of experience in Data Science Training. Our Trainers have conducted more than 200 classes and have extensive experience in teaching Data Science in most simple manner for the benefit of sudents.

We have advanced lab facilities for students to practice Data Science course and get hands-on experience in every topics that are covered under Data Science Training. In the presence of Data Science Trainer, students can execute all the techniques that has been explained by the instructor. Course Material for Data Science is specifically designed to cover all the advanced topics and each of the module will have both theory and practical classes. Data Science Batch Timings at TecMax are flexible and students can choose to join the batch as per their requirements. We have a batch starting every week for Data Science for regular students. Weekend batches and fast track batches for Data Science training can be arranged based on the requirement. 

All our students will get placement assistance in Data Science after successfully completing the Data Science training from our institute. We are committed to provide high-quality training and provide assistance to get you the right job.


Data Science Training Course Content

 

Data Science Essentials

Introduction to Data Science

Overview of Data Science and Machine Learning

Supervised vs. Unsupervised Learning

Working with the Jupyter notebook

The Numpy library for array manipulation

Working with real-world data

The Pandas library for data manipulation

Data cleaning and pre-processing

Data visualisation with Matplotlib and Seaborn

Principal Component Analysis (PCA)

What is PCA and why you need it

Applying PCA in Python with SKLearn

Unsupervised learning and supervised learning

Unsupervised learning

The scikit-learn library for Machine Learning and scikit-learn pipelines

k-means clustering

Hierarchical cluster analysis

Density-based clustering (DBScan)

Supervised Learning

The k-Nearest Neighbour algorithm

Overfitting, underfitting, bias-variance tradeoff

Cross-Validation and hyperparameter tuning

Machine Learning

Random Forests

Decision Trees

Ensemble models and Random Forests

Logistic Regression

Logistic Regression

Regularisation: Ridge and Lasso

Support Vector Classifiers

Linear Support Vector Classifiers (SVC)

The kernel-trick and non-linear SVCs

Random Forests, Logistic Regression, Support Vector Machines (SVMs)

Introduction to Machine Learning

Overview of Machine Learning

Supervised vs. Unsupervised Learning

Industrial Applications

Random Forests

Decision Trees

Ensemble models and Random Forests

Overfitting, validation and the bias-variance trade-off

Hyperparameter tuning, grid search and model selection

Support Vector Classifiers

 

Linear SVCs

The kernel trick and non-linear

Neural networks and deep learning

Neural Networks

Overview of modern applications of Neural Networks

The Perceptron

Structure of general neural networks

Training of Neural Networks

Deep Learning

Motivation and architecture

Real-world examples

Convolutional Neural Networks

Impact and limitations of Deep Learning



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