How many projects are included in the ‘Full Stack Applied AI course’ module?

The program consists of seventeen industrially relevant projects that improve the domain knowledge of the learners.

What are the conditions to receive the course completion certificate?

The candidates will receive the course completion certificate for the ‘Full Stack Applied AI course’ training after submitting the project and assignments without plagiarism involved for evaluation.

Full Stack Applied AI Course by Analytixlabs: Fee, Duration, How to Apply

Full Stack Applied AI Course

Analytixlabs

Learn about the aspects of machine learning and the concepts associated with applied AI applications in the ‘Full Stack Applied AI course.

Online

820 Hours

₹ 109000

Important Dates

12 Jan, 2025

Course Commencement Date

particular	details
Medium of instructions English	Mode of learning Self study, Virtual Classroom +1 more	Mode of Delivery Video and Text Based	Frequency of Classes Weekdays, Weekends	Learning efforts 8-10 Hours Per Week

particular

details

                                    Medium of instructions
                                    English

Mode of learning Self study, Virtual Classroom

                                    Mode of Delivery
                                    Video and Text Based

                                    Frequency of Classes
                                    Weekdays, Weekends

                                    Learning efforts
                                    8-10 Hours Per Week
                                

Python for Data Science

Building Blocks

Introduction to Basic Statistics
Introduction to Analytics & Data Science
Introduction to Mathematical Foundations

Visualizing Geospatial data

Introduction to Folium
Maps with Markers
Choropleth Maps

Python essentials(core)

Overview of Python- Starting with Python
Why Python for data science?
Anaconda vs. python
Introduction to installation of Python
Introduction to Python IDE's(Jupyter,/Ipython)
Concept of Packages - Important packages
NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc
Installing & loading Packages & Name Spaces
Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
List and Dictionary Comprehensions
Variable & Value Labels – Date & Time Values
Basic Operations – Mathematical/string/date
Control flow & conditional statements
Debugging & Code profiling
Python Built-in Functions (Text, numeric, date, utility functions)
User defined functions – Lambda functions
Concept of apply functions
Python – Objects – OOPs concepts
How to create & call class and modules?

Operations with Numpy (Numerical python)

What is NumPy?
Overview of functions & methods in NumPy
Data structures in NumPy
Creating arrays and initializing
Reading arrays from files
Special initializing functions
Slicing and indexing
Reshaping arrays
Combining arrays
NumPy Maths

Overview of Pandas

What is pandas, its functions & methods
Pandas Data Structures (Series & Data Frames)
Creating Data Structures (Data import – reading into pandas)

Cleansing Data with Python

Understand the data
Sub Setting / Filtering / Slicing Data
- Using [] brackets
- Using indexing or referring with column names/rows
- Using functions
- Dropping rows & columns
Mutation of table (Adding/deleting columns)
Binning data (Binning numerical variables in to categorical variables)
Renaming columns or rows
Sorting (by data/values, index) -By one column or multiple columns - Ascending or Descending
Type conversions
Setting index
Handling duplicates /missing/Outliers
Creating dummies from categorical data (using get_dummies())
Applying functions to all the variables in a data frame (broadcasting)
Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc.)

Data Analysis using Python

Exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Uni-variate Analysis (Distribution of data & Graphical Analysis)
Bi-Variate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)

Data Visualization with Python

Introduction to Data Visualization
Introduction to Matplotlib
Basic Plotting with Matplotlib
Line Plots

Statistical Methods & Hypothesis testing

Descriptive vs. Inferential Statistics
What is probability distribution?
Important distributions (discrete & continuous distributions)
Deep dive of normal distributions and properties
Concept of sampling & types of sampling
Concept of standard error and central limit theorem
Concept of Hypothesis Testing
Statistical Methods - Z/t-tests (One sample, independent, paired), ANOVA, Correlation and Chi- square

Python for Data Science-Visualization Tools

Basic Visualization Tools

Area Plots
Histograms
Bar Charts
Pie Charts
Box Plots
Scatter Plots
Bubble Plots

Advanced Visualization Tools

Waffle Charts
Word Clouds
Seaborn and Regression Plots

Predictive Modeling & Machine Learning-Predictive Modeling with Python

Introduction to Predictive Modeling

Concept of model in analytics and how it is used
Common terminology used in modeling process
Types of Business problems - Mapping of Algorithms
Different Phases of Predictive Modeling
Data Exploration for modeling
Exploring the data and identifying any problems with the data (Data Audit Report)
Identify missing/Outliers in the data
Visualize the data trends and patterns

Supervised Learning: Regression problems

Linear Regression
Non-linear Regression
K-Nearest Neighbor
Decision Trees
Ensemble Learning - Bagging, Random Forest, Adaboost, Gradient Boost, XGBoost
Support Vector Regressor

Supervised Learning: Classification problems

Logistic Regression
K-Nearest Neighbor
Naïve Bayes Classifier
Decision Trees
Ensemble Learning - Bagging, Random Forest, Adaboost, Gradient Boost, XGBoost
Support Vector Classifier

Predictive Modeling & Machine Learning-Machine Learning with Python

Introduction to Machine Learning

Applications of Machine Learning
Supervised vs Unsupervised Learning
Overall process of executing the ML project
Stages of ML Project
Concept of Over fitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
Concept of feature engineering
Regularization (LASSO, Elastic net and Ridge)
Types of Cross validation(Train & Test, K-Fold validation etc.)
Concept of optimization - Gradient descent algorithm
Cost & optimization functions
Python libraries suitable for Machine Learning

Unsupervised Learning

Principal Component Analysis
K-Means Clustering
Hierarchical Clustering
Density-Based Clustering

Recommender Systems

Content-based recommender systems
Collaborative Filtering

Time Series Forecasting

What is forecasting?
Applications of forecasting
Time Series Components and Decomposition
Types of Seasonality
Important terminology: lag, lead, Stationary, stationary tests, auto correlation & white noise, ACF & PACF plots, auto regression, differencing
Classification of Time Series Techniques (Uni-variate & Multivariate)
Time Series Modeling & Forecasting Techniques
Averages (Moving average, Weighted Moving Average)
ETS models (Holt Winter Methods)
Seasonal Decomposition
ARIMA/ARIMAX/SARIMA/SARIMAX
Regression
Evaluation of Forecasting Models

Text Mining Using NLP-Introduction to Text Mining

Text Mining - characteristics, trends

Text Processing using Base Python & Pandas, Regular Expressions

Text processing using string functions & methods
Understanding regular expressions
Identifying patterns in the text using regular expressions

Text Mining Using NLP

Text Processing with Modules like NLTK, SklearnGetting Started with NLTK Introduction to NLP & NLTK Introduction to NLTK Modules (corpus, tokenize, Stem, collocations, tag, classify, cluster, tbl, chunk, Parse, ccg, sem, inference, metrics, app, chat, too

Getting Started with NLTK
Introduction to NLP & NLTK
Introduction to NLTK Modules (corpus, tokenize, Stem, collocations, tag, classify, cluster, tbl, chunk, Parse, ccg, sem, inference, metrics, app, chat, toolbox etc)

Initial Data Processing and Simple Statistical Tools

Reading data from file folder/from text file, from the Internet & Web scrapping, Data Parsing
Cleaning and normalization of data
Sentence Tokenize and Word Tokenize, Removing insignificant words(“stop words”), Removing special symbols, removing bullet points and digits, changing letters to lowercase, stemming /lemmatization /chunking
Creating Term-Document matrix
Tagging text with parts of speech
Word Sense Disambiguation
Finding associations
Measurement of similarity between documents and terms
Visualization of term significance in the form of word clouds

Advanced Data Processing and Visualization

ectorization (Count, TF-IDF, Word Embedding's)
Sentiment analysis (vocabulary approach, based on Bayesian probability methods)
Name entity recognition (NER)
Methods of data visualization
- word length counts plot
- word frequency plots
- word clouds
- correlation plots
- letter frequency plot
- Heat map
Grouping texts using different methods
Language Models and n-grams -- Statistical Models of Unseen Data (Smoothing)

Text Mining-Predictive Modeling

Semantic similarity between texts
Text Segmentation
Topic Mining (LDA)
Text Classification (spam detection, sentiment analysis, Intent Analysis)

AI & Cloud Computing-Introduction to AI, Deep Learning & Cloud Computing

Introduction to Artificial Intelligence (AI)

Modern era of AI
Role of Machine learning & Deep Learning in AI
Hardware for AI (CPU vs. GPU vs. FPGA)
Software Frameworks for AI & Deep Learning
Key Industry applications of AI

Introduction to Deep Learning

What are the Limitations of Machine Learning?
What is Deep Learning?
Advantage of Deep Learning over Machine learning
Reasons to go for Deep Learning
Real-Life use cases of Deep Learning
Overview of important python packages for Deep Learning

Introduction to Cloud Computing

Introduction to Google Colab
What is Cloud Computing? Why it matters?
Traditional IT Infrastructure vs. Cloud Infrastructure
Cloud Companies (IBM, Microsoft Azure, GCP, AWS ) & their Cloud Services
Use Cases of Cloud computing
Overview of Cloud Segments: IaaS, PaaS, SaaS
Overview of Cloud Deployment Models
Implementation of ML/DL model in Cloud

AI & Cloud Computing-Computer Vision Application

Introduction to Computer Vision

OpenCV

Introduction to OpenCV
Core Functionalities
Image processing using OpenCV
Video processing using OpenCV
Feature Detection
Video Analysis

Computer Vision Applications

Concept of Transfer Learning
Popular Image net models
Object Classification
Object Detection
Object Tracking
Object Localization
Object Segmentation

Generative Adversarial Networks

AI & Cloud Computing-Neural Networks

Deep Learning with Keras

Define Keras
How to compose Models in Keras
Functional Composition
Predefined Neural Network Layers
What is Batch Normalization
Saving and Loading a model with Keras
Using Tensor Board with Keras
Use-Case Implementation with Keras
Intuitively building networks with Keras

Deep Learning with Tensorflow

Hello World with TensorFlow
Key concepts of Tensorflow
Implementing various types of models
Linear/Non-linear models

Convolutional Neural Networks (CNN)

CNN History
Understanding CNNs
CNN Application

Recurrent Neural Networks (RNN)

Intro to RNN Model
Long Short-Term memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model

AI & Cloud Computing-Unsupervised Learning, Auto Encoders, AcceleratingDL

Unsupervised Learning

Restricted Boltzmann Machine
Collaborative Filtering with RBM

Auto Encoders

Auto Encoders
Deep Belief Network

Hardware Accelerated Deep Learning
Distributed Deep Learning
Deep Learning in the Cloud

AI & Cloud Computing

Industrial & Functional Sessions (Domain Understanding)

Introduction to Data Sources for Various Industries
Introduction to Analytics Project Management
Marketing Analytics
Risk Analytics
Operation Analytics
Digital Analytics (Web Analytics)
Social Network Analytics
Banking & Financial Services, Insurance
Retail & E-Commerce
Pharma & Health Care
Telecom & Network

AI & Cloud Computing-Text Mining&Language Models With Deep Learning

Text Mining

NLP vs. NLU vs. NLG
Vectorization using Word Embedding's
Word2vec and Glove

Language Models

Transfer Learning in the Text Mining
Introduction to Popular Language Models
ULMFiT
Transformer
Google’s BERT
Transformer-XL
OpenAI’s GPT-2
ELMo
Flair
StanfordNLP

Language Models Application

Machine Translation
Text Classification
Text Segmentation
Sentiment Analysis

Build Your Own Chatbot

Introduction to Chatbots
What are chatbots?
Chatbots are trending
How chatbots work
Working with Intents
Understanding Intents
Working with Entities
Understanding Entities
Create Entities
Import and Export Entities
Defining the Dialog
Putting it all together
Building user-friendly chatbots
Implement the Dialog
Define Domain-Specific Intents
Deploying to a WordPress site
Add a preview and retrieve your credentials
Deploy your Chatbot
Watson Assistant in the Private Cloud

Placement Readiness Program

CV preparation and expert sessions
Profile creation on Kaggle and GitHub
Mock interviews
Personal interviews focusing on effective communication and behavioral fit
Technical interviews on applications of data science concepts and techniques
Placement days - Actual & Practice Runs
Technical tests - MCQs and Hiring Projects/ Case Studies
Case Study Presentations
Interviews with Industry Experts
General Analytics and Problem Solving
Profile optimization on job portals (like LinkedIn, Naukri, Indeed, IIMJobs, etc.)
Continual feedback sessions pre and post interviews

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Full Stack Applied AI Course

Online

820 Hours

₹ 109000

Quick Facts

Course overview

The highlights

Program offerings

Course and certificate fees

Fees information

certificate availability

certificate providing authority

Who it is for

Eligibility criteria

What you will learn

The syllabus

Python for Data Science

Building Blocks

Visualizing Geospatial data

Python essentials(core)

Operations with Numpy (Numerical python)

Overview of Pandas

Cleansing Data with Python

Data Analysis using Python

Data Visualization with Python

Statistical Methods & Hypothesis testing

Python for Data Science-Visualization Tools

Basic Visualization Tools

Advanced Visualization Tools

Predictive Modeling & Machine Learning-Predictive Modeling with Python

Introduction to Predictive Modeling

Supervised Learning: Regression problems

Supervised Learning: Classification problems

Predictive Modeling & Machine Learning-Machine Learning with Python

Introduction to Machine Learning

Unsupervised Learning

Recommender Systems

Time Series Forecasting

Text Mining Using NLP-Introduction to Text Mining

Text Mining - characteristics, trends

Text Processing using Base Python & Pandas, Regular Expressions

Text Mining Using NLP

Text Processing with Modules like NLTK, SklearnGetting Started with NLTK Introduction to NLP & NLTK Introduction to NLTK Modules (corpus, tokenize, Stem, collocations, tag, classify, cluster, tbl, chunk, Parse, ccg, sem, inference, metrics, app, chat, too

Initial Data Processing and Simple Statistical Tools

Advanced Data Processing and Visualization

Text Mining-Predictive Modeling

AI & Cloud Computing-Introduction to AI, Deep Learning & Cloud Computing

Introduction to Artificial Intelligence (AI)

Introduction to Deep Learning

Introduction to Cloud Computing

AI & Cloud Computing-Computer Vision Application

Introduction to Computer Vision

OpenCV

Computer Vision Applications

Generative Adversarial Networks

AI & Cloud Computing-Neural Networks

Artificial Neural Network

Deep Learning with Keras

Deep Learning with Tensorflow

Convolutional Neural Networks (CNN)

Recurrent Neural Networks (RNN)

AI & Cloud Computing-Unsupervised Learning, Auto Encoders, AcceleratingDL

Unsupervised Learning

Auto Encoders

AI & Cloud Computing

Industrial & Functional Sessions (Domain Understanding)

AI & Cloud Computing-Text Mining&Language Models With Deep Learning

Text Mining

Language Models

Language Models Application

Build Your Own Chatbot

Placement Readiness Program

Placement Readiness Program

Admission details

Filling the form

How it helps

FAQs

How many projects are included in the ‘Full Stack Applied AI course’ module?

What are the conditions to receive the course completion certificate?