Blockchain and AI Professional Job Oriented Certificate Program
(2 Months)
Enhance your career with AppliedTech’s 2-month program focused on Blockchain and AI technology. Developed with industry experts, this course offers a comprehensive overview of Blockchain and AI, covering smart contract development, decentralized applications, and core AI concepts. Through hands-on learning, you’ll gain practical skills to excel in these fast-growing fields. Whether you’re new to these technologies or looking to advance, our program prepares you to thrive in today’s competitive job market. Unlock your potential and lead in transformative industries with AppliedTech!
Enroll in AppliedTech 2 Months Blockchain and AI Professional Job Oriented Certificate Program
a Singapore based Technology Education Enterprise.Our AI-Powered Blockchain online course at AppliedTech is expertly crafted to meet the diverse needs of aspiring developers, whether you’re just starting out or looking to deepen your expertise. This course delves into a wide array of topics, from the core principles of Blockchain technology to the intricacies of smart contract development and decentralized applications. You will also explore the intersection of AI and Blockchain, learning how artificial intelligence enhances the functionality and efficiency of decentralized systems.
At AppliedTech, we believe in a holistic approach to education. Our program is designed to be engaging and flexible, featuring live interactive sessions that allow for real-time feedback and collaboration. You’ll receive mentorship from industry leaders, ensuring you gain insights that are not only theoretical but also applicable to current industry practices.
Enrolling in our Blockchain and AI Professional course is your opportunity to acquire in-demand skills that will set you apart in the job market. This course equips you with practical knowledge that can be immediately applied in real-world scenarios, preparing you for a successful career in two of the most dynamic fields today. Take the first step towards advancing your career by joining us at AppliedTech and becoming proficient in AI and Blockchain technologies!
An overview of what you will learn from this program.
Session 1:
(3 hours)
- Introduction to the course
- What is blockchain?
- History of blockchain
- Key principles of Blockchain (transparency, immutability, decentralization)
Session 2:
(3 hours)
- Introduction to AI
- History and evolution of AI
- Key principles of AI (ML, Neural Networks, Deep Learning)
Session 3:
(3 hours)
- AI implementation (prediction of a ML model using any easily available data)
- Stating that what if the data is not coming from a trusted source.
- Talk about a use case (medical science, finance)
Session 4:
(3 hours)
- How Blockchain and AI complement each other
- Stating a simple example of data being sourced from a Blockchain to a ML model.
Session 5:
(3 hours)
- How does Blockchain work
- Understanding blocks, chains and nodes
- Cryptography
- Consensus Algorithms
- Types of blockchain – public and private
Session 6:
(3 hours)
- Smart contracts
- Remix IDE
- Metamask
- Block explorer
Session 7:
(3 hours)
- Solidity fundamentals
Session 8:
(3 hours)
- Basic smart contract examples and hands on
Session 9:
(3 hours)
- ERC20 and ERC721 minting contracts
Session 10:
(3 hours)
- Supervised versus unsupervised learning.
- Introduction to common algorithms (regression, classification and clustering)
Session 11:
(3 hours)
- NLP and computer vision
- Building a simple AI model using python and tensorflow.
Session 12:
(3 hours)
- Python fundamentals
Session 13:
(3 hours)
- Fundamentals of Machine Learning using Python
Session 14:
(3 hours)
- Real world case studies of combined implementations of Blockchain and AI
Continued …
Session 15:
(3 hours)
- Real world case studies of combined implementations of Blockchain and AI
Session 16 (3 hours)
Session 17 (3 hours)
Session 18 (3 hours)
Session 19 (3 hours)
Session 20 (3 hours)
will include 3 complete end to end projects which combine Blockchain and AI.
Program Highlights
Live Projects
Engage with real-world Blockchain and AI projects, applying your coding skills to diverse datasets, all directly from your browser. Gain practical experience essential for today’s fast-evolving digital landscape.
Personalized Mentorship
Learn from top industry experts. Our A student-to-instructor ratio ensures close faculty interaction, providing the personalized support and guidance necessary for successful learning and professional growth.
Experienced Faculty Members
Learn from top industry experts. Our A student-to-instructor ratio ensures close faculty interaction, providing the personalized support and guidance necessary for successful learning and professional growth.
Enhancing Employability
Mentors at AppliedTech Academy prepare you for Industry 4.0, providing comprehensive support to develop skilled, employable talent that meets industry needs and aligns with emerging trends in technology and innovation.
Internship Certification
Participate in a 2-month certified internship, where you can immerse yourself in hands-on experience, develop practical skills, connect with experts, and be part of an exciting journey into the future of technology.
Career Counseling Services
Enhance your career with our 2-month Blockchain and AI Professional Job-Oriented Certificate Program, featuring career counseling to prepare you for opportunities in this dynamic, rapidly growing industry.
About the Blockchain and AI Professional Certification Program
Blockchain and AI Professional Certification Program: Your Path to Professional Excellence
AppliedTech’s Blockchain and AI Professional Certification Program is a dynamic two-month course designed to equip you with essential skills in two of the most transformative technologies today. You will gain a comprehensive understanding of Blockchain principles, including decentralized systems, smart contracts, and the development of decentralized applications (DApps). The program also explores the intersection of AI and Blockchain, demonstrating how machine learning can enhance decision-making and data analysis within Blockchain networks.
Led by industry experts, the course emphasizes hands-on learning through practical projects and real-world applications, ensuring you can apply your knowledge effectively. Additionally, you will receive personalized mentorship, career counseling, and support to help you prepare for job opportunities in high-demand roles such as Blockchain developer, AI solutions architect, and data scientist. Upon completion, you will earn a certification that validates your expertise and enhances your career prospects. Join AppliedTech today and take the next step toward a successful career in Blockchain and AI!
In-demand roles in Blockchain and AI.
Blockchain AI Developer: These professionals focus on creating decentralized applications (DApps) and smart contracts that integrate artificial intelligence (AI) algorithms. Their role involves designing and implementing AI-driven features within blockchain systems, utilizing capabilities like predictive analytics, natural language processing (NLP), and computer vision to enhance functionality.
AI Blockchain Architect: In this role, architects are responsible for designing and supervising the implementation of blockchain solutions that incorporate AI elements. They ensure smooth integration between blockchain and AI technologies while addressing key challenges related to data privacy, security, and scalability, ultimately creating robust systems that harness the power of both technologies.
Blockchain Data Scientist: Data scientists specializing in blockchain analyze data stored on distributed ledgers to extract meaningful insights and build AI models. They employ machine learning and other AI techniques to derive value from blockchain data, identifying trends, anomalies, and patterns that inform decision-making and drive business growth.
AI Blockchain Researcher: Researchers in this field investigate innovative methods to merge blockchain and AI technologies. They conduct experiments and studies aimed at advancing understanding in areas such as decentralized AI, federated learning on blockchain networks, and AI-enhanced consensus mechanisms, paving the way for future innovations.
Blockchain AI Solutions Architect: Solutions architects in this domain design comprehensive solutions that leverage both blockchain and AI to tackle complex business challenges. They collaborate with stakeholders to assess requirements, identify synergies, and create architectures that optimize performance, efficiency, and user experience.
AI Blockchain Security Specialist: These security experts concentrate on maintaining the integrity and confidentiality of AI models and data within blockchain networks. They develop cryptographic techniques and security protocols to safeguard AI algorithms, training datasets, and inference results from unauthorized access or manipulation, ensuring trust in the system.
Decentralized AI Governance Consultant: Consultants specializing in decentralized AI governance provide organizations with guidance on best practices for managing AI algorithms and data in blockchain environments. They help establish governance frameworks, compliance strategies, and accountability mechanisms to ensure the ethical and transparent deployment of AI on decentralized platforms, promoting responsible use of technology.
Why choose AppliedTech :
AppliedTech is one of the leading instructor-led training providers, boasting over 100,000 learners across 20+ countries. Our mission is to democratize education, as we believe everyone deserves access to quality learning. Our courses are delivered by subject matter experts from top multinational corporations, and our world-class pedagogy enables learners to quickly grasp complex topics. With 24/7 technical support and career services, we help individuals jump-start their careers in their dream companies.
The demand for blockchain and AI professionals continues to rise annually due to the existing skills gap and the increasing need for decentralized systems along with AI applications. According to the U.S. Bureau of Labor Statistics, jobs in the computer and information technology field are projected to grow by 11% from 2019 to 2029, significantly outpacing the average for all occupations. This growth is fueled by the ongoing expansion of technology, including the adoption of blockchain technology across various industries.
Blockchain and AI technologies are being utilized for a wide range of applications, including finance, supply chain management, and healthcare, creating a growing need for professionals equipped with the knowledge and skills to design, implement, and maintain these systems. Recognizing this demand, AppliedTech has developed a unique program to elevate your blockchain and AI skills. Whether you aim to break into the field, seek career development opportunities, or wish to build valuable applications for your company, our offerings will teach you to employ the best industry practices and the latest technologies to create robust blockchain and AI-based systems.
Get Ahead with Blockchain and AI Professional Certificate!
Certificate of Completion for the 2 Months Program
Internship Certificate from Participating Companies
Letter of Recommendation
Program Eligibility Criteria and Prerequisites :
- No programming experience needed
- All tools used in this course are free for you to use.
- Internet, Laptop/PC
- We start from the very basics
Ready to be a Blockchain and AI Professional?
Enroll Today!
Course Syllabus In Detail :
- Session 1: Python Basics
- About Python
- Python Data Types
- Python Variables
- Python comments
- Python Keywords and Identifiers
- Python User Input
- Python Type conversion
- Python Literals
- Session 2: Python Operators + if-else + Loops
- Python Operators
- Python if-else
- Python While Loop
- Python for loop
- Break, continue, pass statement in loops
- Session 3: Python Strings
- String indexing
- String slicing
- Common String functions
- Assignments and Interview Questions
- Session 4: Python Lists
- Array vs List
- How lists are stored in a memory
- All Operations on List
- List Functions
- Session 5: Tuples + Set + Dictionary
- Tuple
- Operations on tuple
- Set functions
- Session 6: Dictionary
- Operations on dictionary
- Dictionary functions
- Assignments and Interview Questions
- Create functions.
- Arguments and parameters
- args and kwargs
- map(), filter(), reduce()
- Assignments and Interview Questions
- Session 7: OOP Part1
- What is OOP?
- What are classes and Objects?
- Methods vs Functions
- Magic/Dunder methods
- What is the true benefit of constructor?
- Concept of ‘self’
- __str__, __add__, __sub__ , __mul__ , __truediv__
- Session 8: OOP Part2
- Encapsulation
- Collection of objects
- Session 9: OOP Part3
- Class Relationship
- Inheritance and Inheritance class diagram
- Constructor example
- Types of Inheritance (Single, Multilevel, Hierarchical,Multiple )
- Code example and diamond problem
- Polymorphism
- Method Overriding and Method Overloading
- Session on Abstraction
- What is Abstraction?
- Abstract class
- 3 Interview Questions
- Session 10: File Handling + Serialization & Deserialization
- How File I/O is done
- Writing to a new text file
- append()
- Reading a file -> read() and readline()
- Seek and tell
- Working with Binary file
- Serialization and Deserialization
- JSON module -> dump() and load()
- Pickling
- Session 11: Exception Handling
- Syntax/Runtime Error with Examples
- Why we need to handle Exception?
- Exception Handling (Try-Except-Else-Finally)
- Handling Specific Error
- Raise Exception
- Create custom Exception
- Exception Logging
- Session 12: Decorators
- Decorators with Examples
- Session on Generator
- What is a generator?
- Why to use Generator?
- Yield vs Return
- 4 Interview Questions
- Session 13: Numpy Fundamentals
- Numpy Theory
- Numpy array
- Matrix in numpy
- Array operations
- Scalar and Vector operations
- Session 14: Advanced Numpy
- Numpy array vs Python List
- Broadcasting
- Mathematical operations in numpy
- Sigmoid in numpy
- Mean Squared Error in numpy
- Various functions like sort, append, concatenate, percentile, flip, Set functions, etc.
- Session 16: Pandas Series
- What is Pandas?
- Introduction to Pandas Series
- Series Methods
- Session 17: Pandas DataFrame
- Introduction Pandas DataFrame
- Creating DataFrame and read_csv()
- Selecting cols and rows from dataframe
- Filtering a Dataframe
- Adding new columns
- Session 18: Important DataFrame Methods
- Sort, index, reset_index, isnull, dropna, fillna, drop_duplicates, value_counts, apply etc.
- Session 19: GroupBy Object
- What is GroupBy?
- Applying builtin aggregation fuctions on groupby objects
- Session 20: Merging, Joining, Concatenating
- Pandas concat method
- Merge and join methods
- Practical implementations
- Session 21: MultiIndex Series and DataFrames
- Session on Pandas Case Study
- Session 23: Plotting Using Matplotlib
- Get started with Matplotlib
- Plotting simple functions, labels, legends, multiple plots
- About scatter plots
- Bar chart
- Histogram
- Pie chart
- Changing styles of plots
- Session 25: Plotting Using Seaborn
- Why seaborn?
- Categorical Plots
- Stripplot
- Swarmplot
- Categorical Distribution Plots
- Boxplot
- Violinplot
- Barplot
- Session on Data Cleaning and Data Preprocessing Case Study
- Quality issues
- Tidiness issues
- Data Cleaning
- Session 29: Exploratory Data Analysis (EDA)
- Introduction to EDA
- Why EDA?
- Steps for EDA
- Univariate, Bivariate Analysis
- Feature Engineering
- Data Preprocessing steps.
- Session 30: Database Fundamentals
- Introduction to Data and Database
- CRUD operations
- Types of Database
- MySQL workbench
- DDL ,DML ,DQL ,DCL Commands
- Selecting & Retrieving Data with SQL
- Filtering, Sorting, and Calculating Data with SQL
- Sub Queries and Joins in SQL
- Session 38: Descriptive Statistics Part 1
- What is Statistics?
- Types of Statistics
- Population vs Sample
- Types of Data
- Measures of central tendency
- Measure of Dispersion
- Quantiles and Percentiles
- Five Number Summary
- Boxplots
- Scatterplots
- Covariance
- Correlation
- Probability Distribution Functions (PDF, CDF, PMF)
- Random Variables
- Probability Distributions
- Probability Distribution Functions and its types
- Probability Mass Function (PMF)
- Cumulative Distribution Function (CDF) of PMF
- Probability Density Function (PDF)
- Density Estimation
- Parametric and Non-parametric Density Estimation
- Kernel Density Estimate (KDE)
- Cumulative Distribution Function (CDF) of PDF.
- Session 41: Normal Distribution
- How to use PDF in Data Science?
- 2D density plots
- Normal Distribution (importance, equation, parameter, intuition)
- Standard Normal Variate (importance, z-table, empirical rule)
- Skewness
- Use of Normal Distribution in Data Science
- Session 42: Non-Gaussian Probability Distributions
- Kurtosis and Types
- Transformation
- Mathematical Transformation
- Log Transform
- Reciprocal Transform / Square or sqrt Transform
- Power Transformer
- Box-Cox Transform
- Session 43: Central Limit Theorem
- Bernouli Distribution
- Binomial Distribution
- Intuition of Central Limit Theorem (CLT)
- CLT in code
- Session 44: Confidence Intervals
- Confidence Interval
- Ways to calculate CI
- Applications of CI
- Confidence Intervals in code
- Confidence Interval
- Session 45: Hypothesis Testing (Part 1)
- Key idea of hypothesis testing
- Null and alternate hypothesis
- Steps in Hypothesis testing
- Performing z-test
- Rejection region and Significance level
- Type-1 error and Type-2 Error
- One tailed vs. two tailed test
- Applications of Hypothesis Testing
- Hypothesis Testing in Machine Learning
- Session 46: Hypothesis Testing (Part 2) | p-value and t-tests
- What is p-value?
- Interpreting p-value
- T-test
- Types of t-test
- Single sample t-Test
- Independent 2-sample t-Test
- Paired 2 sample t-Test
- Code examples of all of the above
- Session on Chi-square test
- Chi-square test
- Goodness of fit test (Steps, Assumptions, Examples)
- Test for Independence (Steps, Assumptions, Examples)
- Applications in machine learning
- Session on ANOVA
- F-distribution
- One/Two-way ANOVA
- Session on Tensors | Linear Algebra part 1(a)
- What are tensors?
- 0D, 1D and 2D Tensors
- Nd tensors
- Example of 1D, 2D, 3D, 4D, 5D tensors
- Session on Vectors | Linear Algebra part 1(b)
- What is Linear Algebra?
- What are Vectors?
- Vector example in ML
- Row and Column vector
- Distance from Origin
- Euclidean Distance
- Scalar Addition/Subtraction (Shifting)
- Vector Addition/Subtraction
- Dot product
- Angle between 2 vectors
- Linear Algebra Part 2 | Matrices (computation)
- What are matrices?
- Types of Matrices
- Matrix Equality
- Scalar Operation
- Matrix Addition, Subtraction, multiplication
- Transpose of a Matrix
- Determinant
- Inverse of Matrix
- Linear Algebra Part 3 | Matrices (Intuition)
- Basis vector
- Linear Transformations
- Linear Transformation in 3D
- Matrix Multiplication as Composition
- Determinant and Inverse
- Transformation for non-square matrix?
- Session 48: Introduction to Machine Learning
- About Machine Learning (History and Definition)
- Types of ML
- Supervised Machine Learning
- Unsupervised Machine Learning
- Semi supervised Machine Learning
- Reinforcement Learning
- Batch/Offline Machine Learning
- Instance based learning
- model-based learning
- Instance vs model-based learning
- Challenges in ML
- Data collection
- Insufficient/Labelled data
- Non-representative date
- Poor quality data
- Irrelevant features
- Overfitting and Underfitting
- Offline learning
- Cost
- Machine Learning Development Life-cycle
- Different Job roles in Data Science
- Framing a ML problem | How to plan a Data Science project
- Session 49: Simple Linear regression
- Introduction and Types of Linear Regression
- Intuition of simple linear regression
- How to find m and b?
- Regression Metrics
- MAE, MSE, RMSE, R2 score, Adjusted R2 score
- Session 50: Multiple Linear Regression
- Introduction to Multiple Linear Regression (MLR)
- Mathematical Formulation of MLR
- Error function of MLR
- Session on Polynomial Regression
- Why we need Polynomial Regression?
- Formulation of Polynomial Regression
- Session on Assumptions of Linear Regression
- Session 53: Multicollinearity
- What is multicollinearity?
- How to detect and remove Multicollinearity
- Correlation
- VIF (Variance Inflation Factor)
- Session 1: Python Basics
- About Python
- Python Data Types
- Python Variables
- Python comments
- Python Keywords and Identifiers
- Python User Input
- Python Type conversion
- Python Literals
- Session 2: Python Operators + if-else + Loops
- Python Operators
- Python if-else
- Python While Loop
- Python for loop
- Break, continue, pass statement in loops
- Session 3: Python Strings
- String indexing
- String slicing
- Common String functions
- Assignments and Interview Questions
- Session 4: Python Lists
- Array vs List
- How lists are stored in a memory
- All Operations on List
- List Functions
- Session 5: Tuples + Set + Dictionary
- Tuple
- Operations on tuple
- Set functions
- Session 6: Dictionary
- Operations on dictionary
- Dictionary functions
- Assignments and Interview Questions
- Create functions.
- Arguments and parameters
- args and kwargs
- map(), filter(), reduce()
- Assignments and Interview Questions
- Session 7: OOP Part1
- What is OOP?
- What are classes and Objects?
- Methods vs Functions
- Magic/Dunder methods
- What is the true benefit of constructor?
- Concept of ‘self’
- __str__, __add__, __sub__ , __mul__ , __truediv__
- Session 8: OOP Part2
- Encapsulation
- Collection of objects
- Session 9: OOP Part3
- Class Relationship
- Inheritance and Inheritance class diagram
- Constructor example
- Types of Inheritance (Single, Multilevel, Hierarchical,Multiple )
- Code example and diamond problem
- Polymorphism
- Method Overriding and Method Overloading
- Session on Abstraction
- What is Abstraction?
- Abstract class
- 3 Interview Questions
- Session 10: File Handling + Serialization & Deserialization
- How File I/O is done
- Writing to a new text file
- append()
- Reading a file -> read() and readline()
- Seek and tell
- Working with Binary file
- Serialization and Deserialization
- JSON module -> dump() and load()
- Pickling
- Session 11: Exception Handling
- Syntax/Runtime Error with Examples
- Why we need to handle Exception?
- Exception Handling (Try-Except-Else-Finally)
- Handling Specific Error
- Raise Exception
- Create custom Exception
- Exception Logging
- Session 12: Decorators
- Decorators with Examples
- Session on Generator
- What is a generator?
- Why to use Generator?
- Yield vs Return
- 4 Interview Questions
- Session 13: Numpy Fundamentals
- Numpy Theory
- Numpy array
- Matrix in numpy
- Array operations
- Scalar and Vector operations
- Session 14: Advanced Numpy
- Numpy array vs Python List
- Broadcasting
- Mathematical operations in numpy
- Sigmoid in numpy
- Mean Squared Error in numpy
- Various functions like sort, append, concatenate, percentile, flip, Set functions, etc.
- Session 16: Pandas Series
- What is Pandas?
- Introduction to Pandas Series
- Series Methods
- Session 17: Pandas DataFrame
- Introduction Pandas DataFrame
- Creating DataFrame and read_csv()
- Selecting cols and rows from dataframe
- Filtering a Dataframe
- Adding new columns
- Session 18: Important DataFrame Methods
- Sort, index, reset_index, isnull, dropna, fillna, drop_duplicates, value_counts, apply etc.
- Session 19: GroupBy Object
- What is GroupBy?
- Applying builtin aggregation fuctions on groupby objects
- Session 20: Merging, Joining, Concatenating
- Pandas concat method
- Merge and join methods
- Practical implementations
- Session 21: MultiIndex Series and DataFrames
- Session on Pandas Case Study
- Session 23: Plotting Using Matplotlib
- Get started with Matplotlib
- Plotting simple functions, labels, legends, multiple plots
- About scatter plots
- Bar chart
- Histogram
- Pie chart
- Changing styles of plots
- Session 25: Plotting Using Seaborn
- Why seaborn?
- Categorical Plots
- Stripplot
- Swarmplot
- Categorical Distribution Plots
- Boxplot
- Violinplot
- Barplot
- Session on Data Cleaning and Data Preprocessing Case Study
- Quality issues
- Tidiness issues
- Data Cleaning
- Session 29: Exploratory Data Analysis (EDA)
- Introduction to EDA
- Why EDA?
- Steps for EDA
- Univariate, Bivariate Analysis
- Feature Engineering
- Data Preprocessing steps.
- Session 30: Database Fundamentals
- Introduction to Data and Database
- CRUD operations
- Types of Database
- MySQL workbench
- DDL ,DML ,DQL ,DCL Commands
- Selecting & Retrieving Data with SQL
- Filtering, Sorting, and Calculating Data with SQL
- Sub Queries and Joins in SQL
- Session 38: Descriptive Statistics Part 1
- What is Statistics?
- Types of Statistics
- Population vs Sample
- Types of Data
- Measures of central tendency
- Measure of Dispersion
- Quantiles and Percentiles
- Five Number Summary
- Boxplots
- Scatterplots
- Covariance
- Correlation
- Probability Distribution Functions (PDF, CDF, PMF)
- Random Variables
- Probability Distributions
- Probability Distribution Functions and its types
- Probability Mass Function (PMF)
- Cumulative Distribution Function (CDF) of PMF
- Probability Density Function (PDF)
- Density Estimation
- Parametric and Non-parametric Density Estimation
- Kernel Density Estimate (KDE)
- Cumulative Distribution Function (CDF) of PDF.
- Session 41: Normal Distribution
- How to use PDF in Data Science?
- 2D density plots
- Normal Distribution (importance, equation, parameter, intuition)
- Standard Normal Variate (importance, z-table, empirical rule)
- Skewness
- Use of Normal Distribution in Data Science
- Session 42: Non-Gaussian Probability Distributions
- Kurtosis and Types
- Transformation
- Mathematical Transformation
- Log Transform
- Reciprocal Transform / Square or sqrt Transform
- Power Transformer
- Box-Cox Transform
- Session 43: Central Limit Theorem
- Bernouli Distribution
- Binomial Distribution
- Intuition of Central Limit Theorem (CLT)
- CLT in code
- Session 44: Confidence Intervals
- Confidence Interval
- Ways to calculate CI
- Applications of CI
- Confidence Intervals in code
- Confidence Interval
- Session 45: Hypothesis Testing (Part 1)
- Key idea of hypothesis testing
- Null and alternate hypothesis
- Steps in Hypothesis testing
- Performing z-test
- Rejection region and Significance level
- Type-1 error and Type-2 Error
- One tailed vs. two tailed test
- Applications of Hypothesis Testing
- Hypothesis Testing in Machine Learning
- Session 46: Hypothesis Testing (Part 2) | p-value and t-tests
- What is p-value?
- Interpreting p-value
- T-test
- Types of t-test
- Single sample t-Test
- Independent 2-sample t-Test
- Paired 2 sample t-Test
- Code examples of all of the above
- Session on Chi-square test
- Chi-square test
- Goodness of fit test (Steps, Assumptions, Examples)
- Test for Independence (Steps, Assumptions, Examples)
- Applications in machine learning
- Session on ANOVA
- F-distribution
- One/Two-way ANOVA
- Session on Tensors | Linear Algebra part 1(a)
- What are tensors?
- 0D, 1D and 2D Tensors
- Nd tensors
- Example of 1D, 2D, 3D, 4D, 5D tensors
- Session on Vectors | Linear Algebra part 1(b)
- What is Linear Algebra?
- What are Vectors?
- Vector example in ML
- Row and Column vector
- Distance from Origin
- Euclidean Distance
- Scalar Addition/Subtraction (Shifting)
- Vector Addition/Subtraction
- Dot product
- Angle between 2 vectors
- Linear Algebra Part 2 | Matrices (computation)
- What are matrices?
- Types of Matrices
- Matrix Equality
- Scalar Operation
- Matrix Addition, Subtraction, multiplication
- Transpose of a Matrix
- Determinant
- Inverse of Matrix
- Linear Algebra Part 3 | Matrices (Intuition)
- Basis vector
- Linear Transformations
- Linear Transformation in 3D
- Matrix Multiplication as Composition
- Determinant and Inverse
- Transformation for non-square matrix?
- Session 48: Introduction to Machine Learning
- About Machine Learning (History and Definition)
- Types of ML
- Supervised Machine Learning
- Unsupervised Machine Learning
- Semi supervised Machine Learning
- Reinforcement Learning
- Batch/Offline Machine Learning
- Instance based learning
- model-based learning
- Instance vs model-based learning
- Challenges in ML
- Data collection
- Insufficient/Labelled data
- Non-representative date
- Poor quality data
- Irrelevant features
- Overfitting and Underfitting
- Offline learning
- Cost
- Machine Learning Development Life-cycle
- Different Job roles in Data Science
- Framing a ML problem | How to plan a Data Science project
- Session 49: Simple Linear regression
- Introduction and Types of Linear Regression
- Intuition of simple linear regression
- How to find m and b?
- Regression Metrics
- MAE, MSE, RMSE, R2 score, Adjusted R2 score
- Session 50: Multiple Linear Regression
- Introduction to Multiple Linear Regression (MLR)
- Mathematical Formulation of MLR
- Error function of MLR
- Session on Polynomial Regression
- Why we need Polynomial Regression?
- Formulation of Polynomial Regression
- Session on Assumptions of Linear Regression
- Session 53: Multicollinearity
- What is multicollinearity?
- How to detect and remove Multicollinearity
- Correlation
- VIF (Variance Inflation Factor)
Testimonials
What they say
Blockchain and AI Technology Course FAQs
The Professional Certificate Program in Blockchain and AI Technology is an extensive training initiative aimed at equipping individuals with the essential skills and knowledge necessary to thrive in the rapidly evolving fields of Blockchain and AI. This program covers foundational concepts as well as advanced techniques, preparing participants for various roles in these cutting-edge technologies.
To become a Blockchain developer, it’s crucial to learn Blockchain programming languages like Solidity and acquire expertise in areas such as smart contract development, decentralized application creation, and Blockchain architecture design. Practical experience through projects and continuous learning will enhance your skills in this dynamic field.
To earn your certificate in the Blockchain and AI Certification Program, you will need to gain proficiency in Blockchain programming languages like Solidity, as well as develop skills in smart contract development, decentralized applications, and Blockchain architecture. Additionally, you should familiarize yourself with AI technologies such as machine learning, pre-trained models, and Python programming.
Taking this course offers numerous advantages, including acquiring in-demand skills in Blockchain and AI technologies, earning a respected certification recognized by leading companies, and gaining access to flexible online learning resources that cater to your schedule. These benefits position you for success in the fast-paced realms of Blockchain and AI.
Absolutely! The demand for Blockchain and AI technologies remains strong as they continue to transform various sectors, including finance, healthcare, and logistics. Companies are actively seeking skilled professionals capable of designing, developing, and implementing innovative Blockchain and AI solutions.
Pursuing a Blockchain and AI certification distinguishes you in the job market by showcasing your expertise in these transformative technologies. You will be better positioned for:
- Improved Job Opportunities: Employers are increasingly looking for professionals knowledgeable in both AI and Blockchain.
- Increased Earning Potential: Expertise in these high-demand fields can lead to higher salary prospects.
- Career Resilience: This certification prepares you for the convergence of AI and Blockchain technologies and their implications across industries.