Data Science - Machine Learning and Deep Learning
Data science is an essential part of many industries today, given the massive amounts of data that are produced. Its popularity has grown over the years and companies have started implementing data science techniques to grow their business and increase customer satisfaction. Machine learning is the backbone of Data science.
Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Deep learning is an important element of data science which includes statistics and predictive modeling. It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data; deep learning makes this process faster and easier. Data scientists need to understand deep learning techniques and use them especially when dealing with massive data.
Data scientists are in high demand and well-paid because they work in both the business and IT sectors.
Duration : 4.5 Months
Ideal for : Freshers and professionals with 1-2 years of Industry experience who want to start their career in IT Sector as Data Analyst, Data Engineers or Data Scientist for various domains of Data science, Artificial Intelligence and Machine learning.
Aims at helping you master all the basic and advanced level skills that are crucial in the field of Data Science & Machine Learning
Designed for Working Professionals & Freshers
Course designed by Industry Experts as per Industry need
Job Oriented Programme – Kickstart your career in IT Sector
Various assignments on each important topic
Mock interviews and Mock tests preparation
Course Content
- What is Python? History of Python.
- Python Data Types
- What are Keywords, What are variables
- Introduction to Python
- Control Flow Statements
- Data Structures
- Functions
- Object oriented Programming
- Exception Handling and GUI
- Project Discussion
- What is SQL?
- Why we need SQL ?
- What is Database Management System (DBMS)?
- Types of DBMS
- Execution of SQL Query
- Difference between SQL & MYSQL
- Introduction to MYSQL
- Installation of MYSQL Server
- Download Sample Database
- Basic SQL Keywords
- Joins
- DML / DDL
- Introduction
- Distributions and Various Tests
- Inferential Statistics
- Case Studies
- History of Neural Networks
- Who invented Neural Network?
- What is the intuition of a Neural Network?
- What is a perceptron?
- Connecting Logistic Regression, Linear
- Regression with Perceptron
- Multi Layer Perceptron
- Training of a Perceptron
- MLP Backpropogation
- Notation
- Training a MLP:Chain Rule
- Training a MLP:Memoization
- Backpropogation
- Activation Functions
- Sigmoid
- Tanh
- RELU
- Vanishing gradient
- Deep Multilayer Perceptrons
- Dropout and Regularisation
- Batch Normalisation
- Batch SGD with Momentum
- Adam
- Softmax and Cross-Entropy
- How to train Deep MLP?
- Tensorflow and Keras Overview
- Install Tensorflow
- Softmax Classifier on MNIST data
- Code Walkthrough of MLP
- Hyperparameter Tuning in Keras
- Introduction to CNN
- Introduction to CNN(Convolution Neur
- What is Convolution?
- Convolution:Padding and Stride
- Convolution over RGB image
- Max Pooling
- CNN Training
- AlexNet
- VGGNet
- Residual Network
- Inception Network
- What is Transfer Learning?
- Code Walkthrough of CNN
- Introduction to RNN
- Why RNN (Recurrent Neural Network)?
- Training RNN
- Types of RNN
- Need of LSTM
- LSTM(Long Short Term Memory)
- Deep RNN
- Bidirectional RNN
- Code Walkthrough of RNN
- Introduction to NLP
- What is NLP (Natural Language Processing)?
- BOW (Bag of Words)
- Text Preprocessing:Stemming and Lemmatisation
- Stop Word Removal
- Tokenisation
- Unigram, Bigram, Ngrams
- TF-IDF
- Weighted TF-IDF
- Word2Vec(W2V)
- Code Walkthrough of NLP Techniques
- Deep Learning Project
- Business Problem
- Contraints
- Data Collection
- Formulate Business Problem to Deep Learning Problem
- Data Cleaning
- Data Preprocessing
- EDA(Exploratory Data Analysis)
- Feature Extraction
- Modelling
- Evaluating the Performance of the models
- Retrain if necessary
- Introduction to Power BI
- What is PowerBI? Why PowerBI?
- Power BI Desktop – Install
- Data Sources and Connections
- Connect to Data in Power BI Desktop
- How to use Query Editor in Power BI
- Why Data Visualization
- How to use Visual in Power BI
- Charts in Power BI (Scatter, Waterfall, Funnel)
- Slicers
- Data Analysis Expression
- What is DAX(Data Analysis Expression)?
- Data Types in DAX
- Calculation Types
- DAX Functions : Date and Time, Time Intelligence,
- Information, Logical, Mathematical, Statistical,
- Text, Aggregate Measures in DAX
- Table Relationships and DAX
- Custom Visualisation
- What Are Custom Visuals?
- Office Store
- Downloading Custom Visuals
- Importing Custom Visuals in Power BI Report KPI
- Visuals
- Data Binding in Power BI
- Power BI Embedded
- Power BI Embedded Conceptual Model
- Workspace Collection
- Adding Power BI content to a Workspace
- Application Authentication Tokens
- Parts of Power BI embedded - REST API
- Power BI Embedding without an Embed Token
- Power BI Embedding with an Embed Token
- PROJECT(Creating a Dashboard)
