Full Stack Data Science with 1 year Internship

COURSE DESCRIPTION

This is a Data Science training program with all dimensions of industry requirements to full fill their need. We will cover all industrial needs along with Artificial Intelligence, Machine Learning, Deep Learning , NLP, Computer Vision . We will cover all  visualization tools , Clouds Platform, Databases ,Big Data exposures along with required programming languages. We will cover live industrial projects and  will provide Internship of at least one year

CERTIFICATION

This is the age of Full Stack Trainings to full fill all industrial requirements at one place. Industries are searching all those peoples who have all skills in one person. This is the reason to recommend to do Full Stack Program. You will get the one year Internship certificate along with training completion certificate by Dataintelliage private limited.

COURSE OVERVIEW

• Data Introduction
• Data Science Introduction
• Statistics Basic
• Statistics Advance
• Database Introduction
• Database Advance
  •  SQL
  • ORACLE
  • No SQL Cassandra
  • SQLite
  • MongoDB
  • MySQL
• Core python
  • Introduction  to Python.
  • Anaconda
  • Identifiers
  • Constants, Variable and Datatypes
  • Operators in Python
  • IF-ELIF-ELSE-ELIF
  • LOOP
  • Nested LOOP
  • Break, Continue
  • List
  • Tuple
  • String
  • Array
  • Method
  • Package Installation
  • Python NUMPY
• Advance Python
  • OOPS Concept
  • Class
  • Instance
  • Inheritance
  • Polymorphism
  • Encapsulation
  • Abstraction
  • Decorator
  • Class methods and static methods
  •  getters, setters, and deletes
  • Package
  • Interface
  • Exception Handling
  • Thread
  • File Handling
  • Tinker (GUI Framework)
  • Kivy
  • Database( MYSQL ,  NoSQL – Cassandra, SQLite, MongoDB )
  • Django
  • Pandas (Basic)
  • Pandas (Advance)
  • Flask
  • Dask
  • Matplotlib
  • Seaborn
  • Plotly
  • Cufflinks
  • Bokeh
  • Project
• Graphs
• Data analytics
  • Tableau
  • Power BI
  • R
  • Weka
  • Excel
• Machine learning
  • Introduction to Machine Learning
  • Numpy
  • Pandas
  • Matplotlib
  • Sklearn
  • EDA
  • Probability
  • Seaborn
  • Feature Engineering
  • Underfitting Overfitting
  • ALGORITHMS
    • Linear Regression
    • Ridge and Lasso Regression
    • Multiple Linear Regression
    • Multi Collinearity
    • Logistic Regression
    • Decision Tree
    • SVM
    • Naive Bayes
    • kNN
    • Clustering
      • K – means Clustering
      • Mean Shift Clustering
      • DBSCAN
      • Agglomerative Hierarchical Clustering
      • EM Clustering
    • K-Means
    • Random Forest
    • Dimensionality Reduction Algorithms
    • Gradient Boosting algorithms
      • GBM
      • XGBoost
      • LightGBM
      • CatBoost
  • Pruning Algorithm
  • Model Building
  • Model Deployment on Google Colab, AWS, Azure.
  • 20 End to End Project
• Cloud
  • AWS
  • AZURE
  • GCP
  • ORACLE CLOUD
• Deep learning
  • Introduction to Deep Learning
  • Introduction to Neural Network
  • Multilayer Neural Network
  • Gradient Descent and Back Propagation
  • Activation Function
  • Weight initialization Techniques
  • Stochastic Gradient Descent
  • Global Minima and Local Minima
  • Keras
  • Pytorch
  • Flask
  • Optimizers
  • Keras Tuner
  • Hyper Parameter Tuning
  • CNN
  • Data Augmentation (with Keras)
  • RNN
  • Back propagation in RNN
  • Neural Designer
  • LSTM
  • LSTM RNN
  • NLP with Deep Learning
  • ANN training on Google Colab GUP
  • Weka
• Big data
  • • Introduction to Big Data
    • Introduction to Hadoop
    •  Relational Databases vs NOSQL Databases Structure
    • Big Data Applications
    • Cluster Architecture
    • Grid computing
    • Cloud Computing
    • Introduction to Hadoop
    • HDFS
    • Map Reduce
    • Kafka
    • Airflow
    • HBase
    • Hive
    • SQOOP
    • SPARK
    • DATABRICKS
    • Oozie
    • Pig
    • YARN
    • Big Data and Cloud
    • ETL
    • Project
• PySpark
• Computer vision
  • Introduction to Computer Vision
  • Prerequisite Revision
  • OpenCV
  • Pytorch
  • Tensorflow
  • Neural Network
  • Text Processing
  • OCR
  • Computer Imaging System
  • Binary Image Analysis
  • Image Processing
  • Edge Detection
  • Motion and Flow
  • Single view geometry
  • Multiple view geometry
  • Image Detection
  • Project
• Natural language processing
  • Introduction to NLP and Deep Learning
  • Tokenization,
  • Stop words
  • Stemming and lemmatization
  • Named entity recognition
  • Word vector
  • NLTK
  • BERT
  •  Google Dialogflow (Chatbot project)
  • Azure Luis (Chatbot project)
  • Amazon Lex (Chatbot project)
  • RASA NLU (Chatbot project)
• Data structure and algorithm
• Projects
• Mock interview
• Interview preparation
• Resume building