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Full Stack Applied AI Course
BY
Analytixlabs
Learn about the aspects of machine learning and the concepts associated with applied AI applications in the ‘Full Stack ...Read more
Online
820 Hours
₹ 109000
Quick Facts
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Medium of instructions
English
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Mode of learning
Self study, Virtual Classroom
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Mode of Delivery
Video and Text Based
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Frequency of Classes
Weekdays, Weekends
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Learning efforts
8-10 Hours Per Week
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Course overview
This program on ‘Full Stack Applied AI Course’ is developed and designed by the data science institute for the candidates to transform into python professionals with AI and ML skills. The program is scheduled for four hundred hours and the candidates will be required to attend a total number of fifty classes in which they are allowed to explore the features of deep learning, machine learning, and data science within the scope of applied artificial intelligence to meet the business needs.
The learners will get hands-on experience with the software tools such as Python and other data science aspects. The ‘Full Stacked Applied AI online course’ curriculum is application-based and includes data extraction, wrangling statistical frameworks, supervised and unsupervised machine learning strategies that are required for various applications of AI. The assignments, case studies, projects, and evaluation helps the students upskill themselves with experimental industrial insights and gain a valuable certification.
The highlights
- Online mode
- Live sessions
- Independent study option
- Demo session
- Placement support
- Job referrals
- Flexible fees
- Loan support
- Capstone project
- Testimonials
- Certification
Program offerings
- Demo session
- Fee installments
- Loan assistance
- Projects
- Assignments
- Practice exercises
- Evaluation
- Software tools
- Technical skill
- Job opportunities
- Case study
- Profile creation
- Interview preparation
- Course completion certificate
Course and certificate fees
Fees information
₹ 109,000
The ‘Full Stack Applied AI course’ is available to the candidates in three different versions for learning with specific course fee amounts. The students who can’t afford the one-time payment can pay the course fee in three installments. A loan facility is also available for the candidates.
Full Stack Applied AI course fee structure
| Particulars | Amount in INR |
Classroom & Bootcamp | Rs 109000 + taxes |
Fully Interactive Live Online | Rs 109000 + taxes |
Blended eLearning | Rs 85000 + taxes |
certificate availability
Yes
certificate providing authority
Analytixlabs
Who it is for
The candidates who wish to apply for the ‘Full Stack Applied AI course’ are required to have a background qualification in engineering, mathematics, statistics, and business management such as AI engineers, data analysts, business analysts, data scientists, analytics managers. The ‘Full Stack Applied for AI course’ benefits the students by providing them with skills needed for advanced analytics, AI, machine learning, and the applications of AI and ML.
Eligibility criteria
Certificate qualifying details
The students of the online ‘Full Stack Applied AI course’ can receive a course completion certificate after the submission and evaluation of the assignments and projects without plagiarism.
What you will learn
Machine learning
Data science knowledge
Knowledge of python
Knowledge of artificial intelligence
Knowledge of deep learning
Data wrangling
The ‘Full Stack Applied AI syllabus is framed for the students to understand the real-world applications of the concepts of artificial intelligence and machine learning with software tools and frameworks like Python, NumPy, TensorFlow, Keras, Pandas, SciKit-Learn, TextBlob, NLTK, and PyTorch. The students will learn about statistical modeling, data handling, and visualization along with the implementation of deep learning concepts in AI applications such as image processing, text and data processing, chat-bots, time series, recommendation systems, machine translation, and IoT.
The syllabus
Python for Data Science
Building Blocks
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
Artificial Neural Network
- Overview of Neural Networks
- Hidden layers, hidden units
- Illustrate & Training a Perceptron
- Important Parameters of Perceptron
- Limitations of A Single Layer Perceptron
- Illustrate Multi-Layer Perceptron
- Activation function, Optimizers, Loss Functions
- Understand Backpropagation – Using Example
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
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
Admission details
The candidates who are interested in applying for the ‘Full Stack Applied AI’ course should register online for the course on the website.
Step 1: Go to the course page on the official website of Analytixlab using the following link, https://www.analytixlabs.co.in/artificial-intelligence-certification-courses-online
Step 2: Choose among the preferred mode of learning and click on the respective ‘Enroll Now’ link
Step 3: Fill in the relevant information and complete the enrollment.
Filling the form
For the registration of ‘Full Stack Applied AI course’ training, the candidates must fill in their name, phone number, email address, course name, and city name in the form provided on the page.
How it helps
The ‘Full Stack Applied AI course’ certification enables the students to gain technical knowledge and aspires for industrially relevant jobs in their qualified domain. The candidates acquire skills for analytics on the cloud, for the techniques of predictive modeling using neural networks, processing of images with the help of strategies of deep learning, to understand the natural language processing and generation(Chatbots), implementation of deep learning modeling for the applications of AI, comprehending the procedures involved in data science, analyze the supervised and unsupervised learning models.
FAQs
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.
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