Enquire this Course
Data Science Training Institute in Marathahalli
Why We are the Best Data Science Training Institute in Marathahalli
Training Institute Marathahalli is located in Bangalore, India and is happy to offer its expertise in Data
Science Program. Our tried and true system is based off over 10 years cumulative experience
shared between our trainers. We pride ourselves on setting up our clients for success in Data
Science and AI operations and are sure that you will leave our sessions more prepared than
you have ever been before to Machine Learning and Deep Learning courses.
Out Trainers have got more than 5 years' real time as well as teaching experience
Class Room & Online
We have multiple batches for classroom and online students.
We have got an experienced HR professionals to help students with job opportunitites
Data Science Training Institute in Marathahalli - Course Content
- Introduction to Data Science
- Data Science vs Business Analytics vs Big Data
- Classification of Business Analytics
- Data Science Project Workflow
- Various Roles in Data Science
- Application of Data Science in various industries
- Introduction to Data Science with Python
- Python Basics: Basic Syntax, Data Structures
- Data objects, Math, Comparison Operators, Condition Statements, loops, lists, tuples, dicts,
- Numpy Package
- Pandas Package
- Python Advanced: Data Mugging with Pandas
- Python Advanced: Visualization with Matplotlib
- Exploratory Data Analysis: Data Cleaning, Data Wrangling
- Exploratory Data Analysis: Case Study
- Introduction to Statistics
- Harnessing Data
- Exploratory Analysis
- Hypothesis & Computational Techniques
- Correlation & Regression
- Visual Analytics Basics
- Basic Charts, Plots
- Install SQL packages and Connecting to DB
- RDBMS (Relational Database Management) Basics
- Basics of SQL DB, Primary key, Foreign Key
- SELECT SQL command, WHERE Condition
- Retrieving Data with SELECT SQL command and WHERE Condition to Pandas Dataframe.
- SQL JOINs
- Left Join, Right Joins, Multiple Joins
- Machine Learning Introduction
- What is ML? ML vs AI. ML Workflow, Statistical Modelling of ML. Application of ML
- Machine Learning Algorithms
- Popular ML algorithms, Clustering, Classification and Regression, Supervised vs Unsupervised.
- Choice of ML
- Supervised Learning
- Simple and Multiple Linear Regression, KNN, and more
- Linear Regression and Logistic Regression
- Theory of Linear regression, hands on with use cases
- K-Nearest Neighbour (KNN)
- Decision Tree
- Naïve Bayes Classifier
- Unsupervised Learning: K-Means Clustering
- Advanced Machine Learning Concepts
- Tuning with Hyper parameters
- Random Forest – Ensemble
- Ensemble Theory, Random Forest Tuning
- Support Vector Machine (SVM)
- Simple and Multiple Linear Regression, KNN
- Natural Language Processing (NLP)
- Text Processing with Vectorization, Sentiment Analysis with Text Blob, Twitter Sentiment
- Naïve Bayes Classifier
- Naïve Bayes for Text Classification, New Articles Tagging
- Artificial Neural Network (ANN)
- Basic ANN network for Regression and Classification
- TensorFlow Overview
- Deep Learning Intro
- Named Entity Recognition(NER).
- Coreference Resolution.
- Semantic Network.
- Topic Modelling, TF-IDF, POS Tagging.
- Regex for NLP, Text Similarity.
- Context Understanding Using Bert.
- Handling Audio Data, Text to Audio, Audio to Text.
- What is a Time-Series?
- Trend, Seasonality, Cyclical and Random
- Auto Regressive Model (AR)
- Moving Average Model (MA)
- ARMA Model
- Stationarity of Time Series
- ARIMA Model – PredictionConcepts
- ARIMA Model Hands on with Python
- Basics of Application Program Interface (API)
- API basics, loosely Coupled Architecture
- Installing Flask
- Installation and configuring Flask and cross domain authentication
- End to End ML project with API Deployment
- Complete Project Flow with API Deployment and assessing through the website
- Introduction to Deep learning
- What is Deep Learning?
- Various Deep Learning models in practice and applications
- Convolutional Neural Network CNN Intro
- Deep Dive into Optimizers, Activation Functions, Loss Functions, Back-Propagation
- RNN, LSTM, BERT, Attention based Neural Networks
- Encoder & Decoder, Bi-Directional RNNs
- Transfer Learning, Different types of CNN Architectures
- Computer Vision Techniques – YOLO, FaceNet