PG Program in Data Science and Artificial Intelligence from Steinbeis
Get the skills required to be a top Data Scientist and gain deeper understanding of AI with PG Program in Data Science and AI Course from Steinbeis.
Online
11 MonthsQuick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based |
Course Overview
Data science is one of the most sought-after courses of the time and since its amalgamation with artificial intelligence, it has received even higher growth as well as demand from the industry. Due to it being new to the market and only catching the professional attention a little over 2 years ago, most of the candidates still don’t have the proper tools or techniques to master this skill set.
The PG Program in Data Science and Artificial Intelligence from Steinbeis does a good job of filling the gap between demand and supply by providing its students with all the necessary information about the domain. It gives the candidates useful insights into the sector through which they can make it as successful personnel in the field.
The course is widely renowned as is the university that is providing it; Steinbeis University. The materials provided in the course deal with any doubt that may arise in the candidates’ minds beforehand and maintains a smooth exchange of learning with the instructors.
The Highlights
- Steinbeis University certification
- 11-month long course
- ExcelR offered programme
- Dual certification
- Faculty of Steinbeis University
- SGIT alumnus status
- Online sessions
- Shareable certificate
- Self-paced learning
Programme Offerings
- Projects
- assignments
- videos
- blended learning
- E-learning
- Placement Support
- Instructor-led classroom
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | ExcelR SolutionsSteinbeis Global Institute, Tubingen |
Eligibility Criteria
Education
Candidates with a level of expertise in mathematical and analytical skills are eligible for the programme.
Certification Qualifying Details
Applicants will give an online exam after the end of the course in which scoring a minimum of 60% is necessary to ensure their certificate.
What you will learn
Applicants will have mastered a great number of skills and gained useful information on many topics by the end of the course.
- The candidates will start off by first mastering the skills of Agile.
- Structured query language will be an important part of the candidate’s syllabus which will help them greatly in storing and manipulating data.
- Applicants will learn the high-performance, object-oriented programming language Python that will work for analyzing machine algorithms and data.
- Artificial intelligence taught in the course will convey deep learning mechanisms of image and audio files, as well as textual data.
- Applicants will also be taught to store data and build as well as transform models using amazon web services.
- Internet of Things (IoT) gives insights into the IoT sensors which teaches the candidates to retrieve streaming data and put it onto Cloud.
Who it is for
Below mentioned are the group of people that will receive the most benefits from the course.
- Managers that wish to gain a strong grasp of data science to better optimise the information they get from clients are perfectly suited for the programme.
- The course is a tailored fit for team leaders who want to learn to organise and analyse their data in order to develop a better work plan strategy.
- Data science professionals are the ones this course was made for as it teaches them the shifts the industry is going through, with the introduction of AI, to not just learn how to grab a foothold in this scenario but how to make the most from the situation here as well.
The Syllabus
Introduction
- MS office Versions(similarities and differences)
- Interface(latest available version)
- Row and Columns
- Keyboard shortcuts for easy navigation
- Data Entry(Fill series),Find and Select
- Clear Options,Ctrl+Enter
- Formatting options(Font,Alignment,Clipboard(copy, paste special))
Referencing, Named Ranges,Uses,Arithemetic Functions
- Mathematical calculations with Cell referencing(Absolute,Relative,Mixed) Functions with Name Range
- Arithmetic functions(SUM,SUMIF,SUMIFS,COUNT,COUNTA,COUNTIFS,AVERAGE,AVERAGEIFS,MAX,MAXIFS,MIN,MINIFS)
Logical Functions
- Logical functions:IF,AND,OR,NESTED IFS,NOT,IFERROR Usage of Mathematical and Logical functions nested together
Referring Data From Different Tables: Various Types Of Lookup, Nested IF
- LOOKUP
- VLOOKUP
- NESTED VLOOKUP
- HLOOKUP
- INDEX
- INDEX WITH MATCH FUNCTION
- INDIRECT
- OFFSET
Advanced Functions
- Combination of Arithmatic
- Logical
- Lookup functions
- Data Validation(with Dependent drop down)
Date And Text Functions
- Date Functions:DATE,DAY,MONTH,YEAR,YEARFRAC,DATEDIFF,EOMONTH;
- Text Functions:TEXT,UPPER,LOWER,PROPER,LEFT,RIGHT,SEARCH,FIND,MID,TTC, Flash Fill
Data Handling::Data Cleaning, Data Type Identification, Remove Duplicates, Formatting And Filtering
- Number Formatting(with shortcuts)
- CTRL+T(Converting into an Excel Table)
- Formatting Table
- Remove
- Duplicate
- SORT
- Advanced Sort
- FILTER
- Advanced Filter
Data Visualization: Conditional Formatting, Charts
- Conditional formatting(icon sets/Highlighted colour sets/Data bars/custom formatting)
- Charts:Bar,Column,Lines,Scatter,Combo,Gantt,Waterfall,pie
Data Summarization: Pivot Report And Charts
- Pivot Reports
- Insert,Interface,Crosstable Reports;Filter,Pivot Charts
- Slicers
- Add,Connect to multiple reports and charts Calculated field, Calculated item
Data Summarization: Dashboard Creation, Tips And Tricks
- Dashboard
- Types,Getting reports and charts together, Use of Slicers
- Design and placement
- Formatting of Tables,Charts,Sheets,Proper use of Colours and Shapes
Connecting To Data: Power Query, Pivot, Power Pivot Within Excel
- Power Query:
- Interface, Tabs; Connecting to data from other excel files, text files, other sources, Data Cleaning, Transforming, Loading Data into Excel Query
Connecting To Data: Power Query, Pivot, Power Pivot Within Excel
- Using Loaded queries
- Merge and Append
- Insert Power Pivot
- Similarities and Differences in Pivot and Power Pivot reporting
- Getting data from databases
- workbooks
- webpages
VBA And Macros
- View Tab
- Add Developer Tab
- Record Macro:Name
- Storage Record Macro to Format table(Absolute Ref)
- Format table of any size(Relative ref)
- Play macro by button,shape,as command(in new tab)
- Editing Macros
- VBA:Introduction to the basics of working with VBA for Excel: Subs, Ranges, Sheets. Comparing values and conditions, if statements and select cases. Repeat processes with For loops and Do While or Do Until Loops
- Communicate with the end-user with message boxes and take user input with input boxes, User Form
Power BI Introduction And Installation
- Understanding Power BI Background
- Installation of Power BI and check list for perfect installation Formatting and Setting prerequisits
- Understanding the difference between Power BI desktop & Power Query
The Power BI User Interface, Including Types Of Data Sources And Visualizations.
- Getting familiar with the interface BI Query & Desktop Understanding type of Visualisation Loading data from multiple sources Data type and the type of default chart on drag drop. Geo location Map integration
Sample Dashboard With Animation Visual
- Finanical sample data in Power BI
- Preparing sample dashboard as get started
- Map visual Types and usages in different variation Understanding scatter Plot chart with Play axis and the parameters
Power BI Artificial Intelligence Visual
- Understanding the use of AI in power Bi AI analysis in power bi using chart Q&A chat bot and the use in real life Hirarchy tree
Power BI Visualization
- Understanding Column Chart Understanding Line Chart Implementation of Conditional formating Implementation of Formating techniques
Power Query Editor
- Loading data from folder Understanding Power Query in detail Promote header
- Split to limiter
- Add columns
- append
- merge queries etc
Modelling With Power BI
- Loading multiple data from different format understanding modelling (How to create relationship) connection type
- Data cardinality
- Filter direction Making dashboard using new loaded data
Power Query Editor Filter Data
- Power Query Custom Column & Conditional Column Manage Parameter
- Introduction to Filter and types of filter Trend analysis
- Future forecast
Customize The Data In Power BI
- Understanding Tool tip with information Use and understanding of Drill Down Visual interaction and customisation of visual interaction
- Drill through function and usage Button triggers
- Bookmark and different use and implementation Navigation buttons
Dax Expressions
- Introduction to DAX Table Dax
- Calculated column
- DAX measure and difference Eg:- Calendar, Calendar auto, Summarize, Group by etc
- Calculated Column Related
- Lookup value
- switch
- Datedif
- Rankx
- Date functions
- Dax Measure and Quick Measure Remove filters
- Keep filters
- All
- Allselected
- Time Intelligence Functions
- Rolling average
- YoY
- Running total
Custom Visual
- Custom visual and understanding the use of custom Loading custom visual
- Pinning visual Loading to template for future use Publishinhg Power Bi
Power BI Service
- Introduction to app.powerbi.com Schedule refresh Data flow and use power bi from online Download data as live in power point and more
Introduction To Tableau
- What is Tableau ?
- What is Data Visulaization ?
- Tableau Products
- Tableau Desktop Variations
- Tableau File Extensions
- Data Types
- Dimensions
- Measures
- Aggregation concept Tableau Desktop Installation Data Source Overview Live Vs Extract
Basic Charts & Formatting
- Overview of worksheet sections Shelves
- Bar Chart
- Stacked Bar Chart
- Discrete & Continuous Line Charts
- Symbol Map & Filled Map
- Text Table
- Highlight Table
- Formatting
- Remove grid lines
- hiding the axes
- onversion of numbers to thousands
- millions
- Shading
- Row divider
- Column divider Marks Card
Filters
- What are Filters?
- Types of Filters
- Extract
- Data Source
- Context
- Dimension
- Measure
- Quick Filters Order of operation of filters Cascading Apply to Worksheets
Calculations
- Need for calculations Types: Basic, LOD's, Table
- Examples of Basic Calculations
- Aggregate functions
- Logical functions
- String functions
- Tablea calculation functions
- numerical functions
- Date functions
Calculations Continued
- LOD's: Examples
- Table Calculations: Examples
Data Combining Techniques
- What is Data Combining Techniques ?
- Types
- Joins
- Relationships
- Blending & Union
Custom Charts
- Dual Axis
- Combined Axis
- Donut Chart
- Lollipop Chart
- KPI Cards (Simple)
- KPI Cards (With Shape)
Groups, Bins, Hierarchies, Sets, Parameters
- What are Groups ? Purpose
- What are Bins ? Purpose
- What are Hierarchies ? Purpose
- What are Sets ? Purpose
- What are Parameters ? Purpose and examples
Analytics & Dashboard
- Reference Lines
- Trend Line
- Overview of Dashboard
- Tiled Vs Floating
- All Objects overview
- Layout overview
- Dashboard creation with formatting
Dashboard Actions & Tableau Public
- Actions: Filter, Highlight, URL, Sheet, Parameter
- Set How to save the workbook to Tableau Public website ?
Introduction To Mysql
- Introduction to Databases
- Introduction to RDBMS
- Explain RDBMS through normalization
- Different types of RDBMS
- Software Installation(MySQL Workbench)
SQL Commands And Data Types
- Types of SQL Commands (DDL,DML,DQL,DCL,TCL) and their applications Data Types in SQL (Numeric, Char, Datetime)
DQL & Operators
- SELECT:LIMIT,DISTINCT,WHERE AND,OR,IN, NOT IN,BETWEEN, EXIST, ISNULL ,IS NOT NULL,Wild Cards, ORDER BY
Case When Then And Handling NULL Values
- Usage of Case When then to solve logical problems and handling NULL Values (IFNULL, COALESCE)
Group Operations & Aggregate Functions
- Group By, Having Clause. COUNT, SUM,AVG,MIN, MAX, COUNT String Functions, Date & Time Function
Constraints
- NOT NULL
- UNIQUE
- CHECK
- DEFAULT
- ENUM
- Primary key
- Foreign Key (Both at column level and table level)
Joins
- Inner
- Left
- Right
- Cross
- Self Joins
- Full outer join
DDL Commands
- DDL:
- Create
- Drop
- Alter
- Rename
- Truncate
- Modify
- Comment
DML & TCL Commands
- DML
- Insert
- Update
- Delete
- TCL
- Commit
- Rollback
- Savepoint and Data Partitioning
Indexes And Views
- Indexes (Different Type of Indexes) and Views in SQL
Stored Procedure
- Stored Procedures
- Procedure with IN Parameter
- Procedure with OUT parameter
- Procedure with INOUT parameter
Function, Constructs
- User Define Function
- Window Functions
- Rank
- Dense Rank
- Lead
- Lag Row_number
Union, Intersect, Sub-Query
- Union
- Union all
- Intersect
- Sub Queries
- Multiple Query
Exception Handling, Loops, Cursor
- Handling Exceptions in a query
- CONTINUE Handler
- EXIT handler
- Loops: Simple, Repeat, While Cursor
Triggers
- Triggers - Before | After DML Statement
Introduction To Python, Variables
- Python Introduction
- Programing Cycle of Python
- Python IDE
- Variables
- Data type
- Number
- string
- List
- Tuple
- Dictionary
Operators
- Operator
- Arthmatic
- Comparison
- Assignment
- Logical
- Bitwise opeartor
Conditional Statements And Loops
- Decision making If
- While loop
- for loop and nested loop
Number Conversion,Functions
- Mathametical functions
- Random function
- Trigonometric function
- Number type conversion - int(), long(). Float ()
Strings
- Strings
- Escape char
- String special Operator
- String formatting Operator
- Build in string methods
- center()
- count()
- decode()
- encode()
List And Tuples
- Python List
- Accessing values in list
- Delete list elements
- Indexing slicing & Matrices
- Built in Function
- cmp()
- len()
- min()
- max()
- Tuples
- Accessing values in Tuples
- Delete Tuples elements
- Indexing slicing & Matrices
- Built in tuples functions
- cmp()
- len ()
Dictionary
- Dictionary
- Accessing values from dictionary
- Deleting and updating elements in Dict.
- Properties of Dist.
- Built in Dist functions & Methods.
- Date & time
- Time Tuple
- calendor module
- time module
Function
- Function
- Define function
- Calling function
- pass by refernece as value
- Function arguments
- Anonymous functions
- return statements
- Scope of variables - local & global
Modules
- Import statemnts
- Locating modules
- current directory
- Pythonpath Dir() function
- global and location functions and reload () functions .
- Packages in Python
Files
- Files in Python
- Reading keyboard input
- input function Opening and closing files .
- Syntax and list of modes
- Files object attribute- open , close .
- Reading and writing files
- file Position
- Renaming
- deleting files
Directories And Exception Handling
- mkdir methid
- chdir () method
- getcwd method
- rm dir Exception handling
- List of exceptions
- Try and exception Try
- finally clause
- user defined exceptions
OOPS
- OOPS concepts
- class
- objects
- Inheritance
- Overriding methods like _init_
- Overloading operators
- Data hiding
Regular Expressions
- match function
- search function
- matching vs searching
- Regular exp modifiers
- patterns
Framework
- Introduction to Django framwork
- overview
- enviorment
- Apps life cycle
- creating views Application
Data Analysis Libraries
- Numpy
- Pandas
- Matplotlib
Datascience Project Lifecycle
- Demo:Introduction to Types of Analytics
- Project Life Cycle
- LMS walk through
Basic Stat
- Data Types
- Measure Of central tendency
- Measures of Dispersion
Basic Stat Contd..
- Graphical Techniques
- Skewness & Kurtosis
- Box Plot.
R And Basic Stat Contd..
- R
- R Studio
- Descriptive Stats in R
Python
- Python (Installation and basic commands)
- Libraries
- Jupyter note book
- Set up Github
- Descriptive Stats in Python
- Pandas and Matplotlib
Basic Stat Contd..
- Random Variable
- Probability
- Probility Distribution
- Normal Distribution
- SND
- Expected Value
- Sampling Funnel
- Sampling Variation
- Central Limit Theorem
- Confidence interval
- Assignments Session -1 (1 hr)
- Introduction to Hypothesis Testing
Hypothesis Testing
- Hypothesis Testing ( 2 proportion test, 2 t sample t test)
- Anova and Chisquare
EDA
- Exploratory data analysis-1(Data Cleaning, Imputation Techniques,Data analysis and Visualization(Scatter Diagram, Correlation Analysis,Tranformations )
Linear Regression
- Priciples of Regression
- Intro to Simple Linear Regression
- Multiple Linear Regression
Logistic Regression
- Logistic Regression
Model Deployment
- Python Model Deployment
Assignment
- Assignments Session-2(1 hr)
- Clustering introduction
- Hierarchical clustering
Data Mining : Unsupervised ML Algorithms
- Kmeans
- DBSCAN
DImensional Reduction Techniques
- PCA
- tSNE
Market Basket Analysis
- Association Rules
Recommendation System And Assignment
- Recomender System
- Assignments Session-3 (1 hr)
Supervised Machine Learning
- Supervised Machine Learning Concept(Regression Tasks/Classification Tasks)
Decision Tree
- Decision Tree(C5.0)
EDA-2
- EDA -2 ( Encoding Methods - OHE, Label Encoders,Outlier detection-Isolation Forest) and Calculating the Predictive Power Score (PPS)
Feautre Engineering
- Feature Engineering (Tree based methods, RFE,PCA)
Modal Validation Techniques
- Model Validation Methods (train-test,CV,Shuffle CV, and Accuracy methods)
Ensembled Techniques
- Bagging and Random Forest,Boosting
- XGBM,LGBM
Classifiers
- KNN,Support Vector Machines
Regularization Techniques
- Lasso and Ridge Regressions
Neural Network
- ANN
- Optimization Algorithm(Gradient descent)
- stochastic gradient descent(intro)
- Back Propagation method
Neural Network And Assignment
- Introduction to CNN
- Assignments Session-4 (1 hr)
Text Mining
- Introduction to Text Mining
- VSM
- Intro to word embeddings
- Word clouds and Doucument Similarity using cosine similarity
- Named Entity Recognition
Naive Bayes
- Text classification using Naïve Bayes
- Emotion Mining
TIme Series
- Introduction to Timeseries
- Level
- Trend and Sesonality
- strategy
- Scatter plot
- Lag plot
- ACF
- Principles of Visualization
- Naïve forecasts
Forecasting
- Forecastin Error and it metrics
- Model Based Approaches
- Model Based approach cont
- AR Model for errors
- Data driven approaches
- MA
- Exp Smoothing
- ARIMA
- Survival Analysis
Project Discussion
- End to End Data Science Project
Introduction To Machine Learning / Deep Learning / Artificial Intelligence (AI)
- Understanding Machine Learning Fundamentals
- Limitations of Machine Learning
- Deep Learning
- AI vs ML vs Generative AI
Python Programming
- Python revision
- Introduction to Tensorflow and Keras
Mathematics: Calculus And Vector Algebra
- Optimization
- Derivatives
- Function
- Scalar-Vector-Matrix
- Vector Operation
- Vector spaces
- Probability
Machine Learning:(Linear And Logistic Regression)
- Priciples of Regression and Classification Model
- Assumptions
- Model Evaluation Matrices
Machine Learning:(Segmentation)
- K-Means & Hierarichal Clustering
Machine Learning:(Bagging)
- Understanding Bagging Concepts
- Decision Tree
- Random Forest
Machine Learning:(Boosting)
- Understanding Boosting Concepts
- XGBM
Intro To Neural Network & Deep Learning
- Understanding Human Brain's functionality
- Gradient Descent Optimization
Neural Network Architecture
- Neural Network Overview
Backward Propagation
- Neural Network Processing
- Backward Propogation
Parameters And Hyperparameters
- Train
- Test & Validation Set
- Vanishing & Exploding Gradient
- Dropout
- Regularization
- Bias correction
- learning rate
- Tuning
- Softmax etc
Optimizers
- Adam
- Ada
- AdaBoost
- RMS Prop etc
Computer Vision
- What is Computer Vision?
- Read in Images in Python
- Introduction to OpenCV
- Basic Image Conversions
- Image Resizing
- Image Transformations
- Contrast Stretching
CNN Architecture
- Introduction to CNN architecture
- Convolution
- Feature detector
- Padding
- Stride
- Activation function
- Pooling
- Training CNN
- Loss function
Transfer Learning In Computer Vision
- Object detection concepts
- Bounding box
- object detection models
- pre-trained models
- transfer learning
- segmentation concepts
Advanced CNN
- Advanced CNN models applications
- face detection
- RCNN
- fast RCNN
- faster RCNN
- mask RCNN
- YOLO
Basic NLP Concepts & Models
- Text pre-processing
- Binary weight
- BoW
- TF-IDF
- Spam detection / Sentiments Analysis
- Named Entity Recognition
Deep NLP (DNLP)
- Working with word vectors
- Word Embedding
- Word2Vec
- CBOW/Skip-gram
Forecasting Deep Learning -1
- Idea behind RNN architecture
- Vanishing/Exploding gradient problem
Forecasting Deep Learning -2
- LSTM
- GRU
- RNN vs LSTM vs GRU
- Training of RNN model
Seq2Seq Models
- Sequential data analysis
- seq2seq model
- encoder-decoder architecture
- application of seq2seq models
Advanced NLP Models
- Idea behind transformer
- architecture and analysis
- pre-trainer models
- Attention (Elmo, BERT , T5)
- Self-Attention
- Transformer Block
- Multi-Head Attention
- Encoder-Decoder Architecture
Generative AI
- Generative AI using LLM's
- Introduction to GPT
- VAE
- GANs
GANs And LLM
- DCGAN - intuition
- MNIST dataset
- Building the generator
- Building the discriminator
- Loss (error) calculation
- WGAN - intuition
Transfer Learning In NLP
- pretrained models (GPT, BERT ,BART, T5) models with applications
- Intro to Different types of Transformer encoder models- Basic BERT
- RoBERTa
- DistilBERT etc
Autoencoders
- Basics of autoencoders
- different types of autoencoders
- applications with examples of autoencoders
- variational autoencoders
AI Applications
- Model evaluation
- ChatGPT
- Bing
- Bard
Speech Analytics
- Introduction
- Automated Speech Recognition
- text to speech conversion
- voice assistant devices
Reinforcement Learning-1
- Intro to RL
- Q learning concept examples
- Q learning applications
- exploration
- exploitation
Reinforcement Learning-2
- policy gradient concepts
- Actor-critic methods
- Proximal policy Optimization (PPO)
- Work with deep RL libraries
Introduction To Generative AI
- Overview of Generative AI
- What is generative AI?
- What's latest in GenAI?
- Generative AI vs Traditional AI
- Common Techniques of Generative AI
- Evolution of LLM
- Gans and LLM?
- Use of GENERATIVE AI
- Challenges in Generative AI
- Understanding AI: Basics and Use Cases
- Differentiating ML,DL, and AI
- Domains of AI-ML
- Use of AI-ML
- Conclusion
Intro To Python
- Why Python
- Getting Started
- First program
- Keywords and Identifiers
- Python Variables,
- Constants and Literals
- Python Data Types
- Python Input
- Output and Import
- if...else Statement
- Python Loop
- Python Functions
- Python Modules vs Package vs Libraries vs Frameworks
Machine Learning
- Machine Learning Basics and Algorithms
Basics On NLP
- What is NLP
- History of NLP
- NLP End to end workflow
- Stopwords
- Tokenization
- Stemming
- Lemmatization
- POS tagging
- TFIDF
- One hot encoding
- Bag of words
- Unigram
- Bigram
- ngram
- Word
- embeddings
- Skip Gram
- Word2vec model
NLP Models
- RNN
- LSTM Models & GRU Models
- Evolution in NLP
Advance NLP: Foundations Of Generative Models
- Foundations of Generative Models & LLM
- Encoder Models
- Decoder Models
- Encoder-decoder architecture
- Attention mechanism
- reseach paper walkthrough
Transformers
- Transformers Models
- Introduction
- Natural Language Processing Transformers
- what can they do?
- How do Transformers work?
- Load models and Use locally
Use Cases: Chatbot Creation
- Real-world applications and case studies of LLMs Chatbot Creation
Finetuning LLM Using Transformers For Different Tasks
- Fine-tuning a model process
- LSTM vs GRU
- Fine tuning Transformers model
Evolution Of GenAI
- History Of Generative AI
- Generative AI (GenAI): The evolution of creativity through technology
GenAI Models And Basic: GPT Models With Prompt Engineering
- Chatgpt- GPT-2, GPT-3, GPT-3.5
- LLAMA-2
- OpenChat Model
- Prompt Engineering
- Hallucination in llm
Model Evaluation
- Model evaluation
- Entropy
- Cross Entropy
- Perplexity
- Parameter efficient fine-tuning (PEFT)
- LoRA
- Soft prompts
- ARC
- Hellaswag
- MMLU
- TruthfulQA
Finetuning LLM LLAMA-2
- Simple finetuning of LLM: LLAMA-2
Model Architectures In LLM
- What is GGUF
- What is GGML
- Use of Open Source LLM on Local Machine
- How to use pretrain LLM models using Ctransfromers
- LM Studio
Generative Adversarial Networks (GANs)
- Generative Adversarial Networks (GANs)
- Image Generation
- Pix2pix Gan
- Dall-e and prompts in GenAI Images
Reinforcement Learning
- Reinforcement Learning
- Techniques of RL
- OPENAI Gym
- Reinforcement Learning
- LLM Applications
Deployment Strategies, Hardware Requirements APIs, Dockers Etc
- Deployment Strategies
- Hardware Requirements
- APIs
- Dockers etc
Langchain
- Langchain: A Framework for LLMs
Model Serving
- Demo with FastAPI and Streamlit
LLM Operations, Scalability, Best Practices, Ethical Issues
- LLM Operations
- Scalability
- Best Practices
- Responsible AI
- Google's Approach
- Ethical Issues
Understanding Of AI, Intro To NLP
- The nature of AI
- Comparison of Descriptive AI and Generative AI
- Core concepts of NLP
- Basics of language understanding
Intro To GPT And ChatGPT, Fundamentals Of Prompt Engineering
- What is GPT
- Its evalution and generational changes
- What is Prompt engineering
- its importance,tpes of prompts
Content Generation With Prompts
- Stratagies for generating text
- music and video using prompt
Applications Of Prompt Engineering
- Question-Answering systems
- Conversational AI
- Sentiment Analysis
Code Generation
- GitHub Copilot Exploration
Image & Video Creation
- Exploring the capabilities and limitations of DALL-E 2 and GPT-4
Project
- Demonstrate mastery of course content with a comprehensive projects
Introduction To ChatGPT
- History and Development of ChatGPT
- Examples of ChatGPT use in various industries
Generative AI
- Key concepts and principles of Generative AI
- Examples of Generative AI models including ChatGPT
- LamDA and Others
ChatGPT In Everyday Life
- ChatGPT applications in everyday life such as writing
- translation and creativity
- Explore ChatGPT potential for Entertainment and Education
Coding Using ChatGPT, Customer Service
- Practical coding tasks and automation using ChatGPT
- Implementing ChatGPT as Virtual assistant or Chatbot for customer service
ChatGPT In Business
- Utilizing ChatGPT for Content creation
- Social media engagement and personalized marketing campaigns
ChatGPT Architectures
- Overview of GPT
- ChatGPT Capabilities
- GPT Architecture
- GPT-3 vs GPT-4
- Advancements in GPT-4
Training And Optimization
- Accessing Open AI API-API keys and Rate Limits
- Conversion flows-GPT in Chatbots Testing and Iteration
- Dataset preparation
- Fine-tuning Techniques
- Model Performance Monitoring
Prompt Engineering With ChatGPT
- Writing Clear,Concise and Focused prompts to maximize ChatGPT's Performance
Introduction To Azure And MS Entra ID
- Introduction to Cloud Computing
- Azure
- Configure Azure account
Administer Identity
- Microsoft Entra ID, Configure User and Group Accounts
Administer Governance
- Configure Subscriptions
- Configure Role Based Access Control
Compliance And Administer Resource
- Configure Azure Policy
- Configure Azure Resources with Tools
Administer Virtual Networking
- Introduction to Virtual Networking
- Configure Virtual Networks, subnets
- Configure Network Groups
- Application security group
- Configure
- user defined routes
- Configure Network Watcher
- Azure Firewall
Administer Intersite Connectivity
- Introduction to Vnet connectivity
- Configure VNet Peering
Administer Network Traffic
- Configure Azure Load Balancer,Configure Azure Application Gateway
Cloud & AWS
- What is cloud ?
- Services offered by different cloud providers
- What is AWS ?
AWS Cloud Services
- S3 Service
- EC2 Instances
- Databases
- IAM - Identity Access Management
- Billing Details
- Machine Learning
S3 Service
- What is AWS Storage ?
- Storage architecture of S3 buckets
- Concepts of Data Encryption
EC2 Instances
- What is Region ?
- What is availability Zones ?
- Pre EC2 and EC2 instance Types
- Procedure for launching AWS EC2 Instance
AWS Databases
- Types Of Databases available in AWS
- Introduction to Amazon RDS
- Read Replicas in Amazon RDS
- What is No SQL Database ?
IAM - Identity Access Management
- Why access management
- What is ARN Service (Amazon Resource Name), IAM features
- What is IAM policies
- IAM permision
- IAM roles
- identity federation
Amazon Virtual Private Cloud
- Introduction to Amazon VPC
- Amazon VPC Components
- IP Addresses
- Elastic Network Interface
Intro To Big Data Technologies
- What is big data
- characteristics of big data
- technologies in big data etc.
Intro To Spark Environment
- what is spark environment
- spark documentation
- installation of spark
- spark concepts
Integration Of Spark Platform
- Integration with different languages like python
- r
- scala, etc.
- Introducing pyspark environment
- pyspark basics and functions
Pyspark Concepts
- Pyspark RDD structures
- dataframe modules
- sql modules
- examples
- exercise problems
- working on datasets
Pyspark ML Concepts
- Pyspak ML libraries
- Regression models
- linear and logistic regression and clustering basics
- tree based models
- ensemble concepts
Pyspark ML Applications
- Pyspark ML applications
- with excercises
- visualizations
Databricks Environment
- What is databricks
- account creation
- cluster creation
- working on pyspark applications in databricks with r
- python and scala
Intro To AWS Cloud
- What is aws cloud
- account creation
- understanding basic aws enevironment and knowledge
Hadoop Environment
- What is hadoop
- hadoop architecture
- creating hadoop environment on AWS cloud
- install java
- install hadoop and related concepts
Hadoop Applications
- Running applications like map reduce on data
- getting insights
- doing analysis
- word count problems etc.
Steinbeis University, Berlin Frequently Asked Questions (FAQ's)
1: What tools will the candidates learn in the course?
The course will teach the candidates a number of great tools to progress in the industry including Amazon web services, Tableau, Agile, IoT, RDBMS, and more.
2: What are the criteria to qualify for the certificate?
For candidates to successfully qualify for the certificate, they need to first pass an examination with a minimum score of 60% at the end of the course.
3: How will the classes be conducted?
The mode of learning will be online which means there will be live sessions from the instructors of the classroom. However, it will be recorded to offer the candidates self-paced learning.
4: How will the candidates prepare for the exam?
The candidates will receive training for exam preparation. They will also have the option of participating in 2 online mock tests before the actual exam.
5: What will happen if candidates fail the exam?
In case a candidate fails to pass the exam or does not score the minimum marks for certification qualification, he/she will have the option of giving the exam once again.
6: What certificates will the candidates gain from the course?
The candidates will attain a course completion certificate from ExcelR, an Internship certificate from the AI variant, PG program certificate from Steinbeis University.
7: Which companies come to ExcelR for candidate placement?
ExcelR has the privilege of calling some of the world’s best companies their clients such as Mercedes-Benz, Metro, Dell, IBM, HP Enterprise, Amazon, Ericsson, Oracle and many more.
8: What is ExcelR?
ExcelR is an organization that provides training in high education programs to both students and professionals by collaborating with great educational institutions. It has awarded more than 30 franchises to entrepreneurs across the world.