+91 8062212555

AI-Powered Digital Marketing Mastery Course

(8 Weeks)

Welcome to AppliedTech’s AI-Powered Digital Marketing Mastery Course! Here, innovation seamlessly merges with marketing excellence. Explore the limitless potential of leveraging artificial intelligence to elevate your digital marketing strategies and achieve unparalleled success. This comprehensive course empowers you to unlock the secrets of cutting-edge AI technologies, data-driven insights, and automation techniques that will revolutionize your marketing campaigns.

Enroll in AppliedTech's AI-Powered Digital Marketing Mastery Course

In association with
Xaltius Logoa Singapore based Technology Education Enterprise.

Elevate your digital marketing game with AppliedTech’s AI-Powered Digital Marketing Mastery Course! This comprehensive 10-week program is meticulously designed for marketers eager to harness the revolutionary capabilities of artificial intelligence to drive exceptional results. Delivered through live online sessions, this course provides an engaging learning experience that combines theoretical knowledge with practical application.

Throughout the program, you will explore a wide array of topics that include AI-driven data analysis, customer personalization, social media marketing, search engine optimization (SEO), and more. You’ll gain insights into how AI can enhance advertising and conversion optimization, streamline email marketing through automation, and improve content creation and optimization. With over 40 hours of live instruction from renowned industry practitioners, you will have the opportunity to engage directly with experts who will guide you through the intricacies of AI in digital marketing.

In addition to live sessions, the course features 20+ assignments and quizzes to reinforce your learning, along with four in-depth case studies to apply your newfound skills to real-world scenarios. You’ll also have access to discussion boards and live webinars to foster collaboration and networking among peers.

Upon completing the course, you will receive a Certificate of Completion, validating your expertise in AI-powered digital marketing. Furthermore, you will have the chance to participate in a one-month internship, providing invaluable experience that will enhance your resume and career prospects.

At AppliedTech, we believe in the power of mentorship, offering personalized one-on-one support to help you navigate your learning journey. Our commitment to your success extends beyond the classroom, as we provide career counseling assistance to hone your soft skills and prepare you for the competitive job market.

Join AppliedTech’s AI-Powered Digital Marketing Mastery Course today, and unlock the skills necessary to transform your marketing strategies and achieve unparalleled success in your career. Don’t miss this opportunity to stay ahead in the rapidly evolving digital landscape—enroll now and become a leader in the future of marketing!

An overview of what you will learn from this program.

Program Highlights

Comprehensive Curriculum

Engage in a structured 10-week program covering essential topics like AI-driven data analysis, customer personalization, SMM, SEO, optimization, and content creation.

Hands-On Learning Experience

Participate in live projects that enable you to apply your knowledge to real-world datasets, solving practical marketing challenges through interactive sessions and hands-on experiences.

Experienced Faculty Members​

Learn from top industry experts. With a low student-to-instructor ratio, we guarantee close interaction with faculty and offer personalized support throughout your learning journey.

Enhancing Employability

To develop skilled talent that meets industry needs, mentors at AppliedTech Academy offer comprehensive support, preparing you for the challenges and opportunities of Industry 4.0.

Internship Certification

Gain valuable experience through a one-month certified internship, offering hands-on exposure to AI-powered digital marketing practices in a environment while enhancing your skills.

Career Counseling Services

Access career counseling assistance focused on improving your soft skills and preparing you for the competitive job market in digital marketing and related fields.

About the AI-Powered Digital Marketing Mastery Course

AI-Powered Digital Marketing Mastery Course: Your Path to Professional Excellence

Welcome to the AI-Powered Digital Marketing Mastery Course at AppliedTech, where we bridge the gap between marketing and cutting-edge technology. This 10-week program is specifically designed for professionals who aspire to revolutionize their marketing strategies by leveraging the immense capabilities of artificial intelligence. As the digital landscape evolves, the integration of AI is no longer just an advantage—it’s a necessity for staying competitive.

In this comprehensive course, participants will dive deep into various facets of AI-driven marketing, gaining insights into how AI technologies can optimize marketing efforts. From understanding AI-powered data analysis and consumer insights to mastering personalization techniques that enhance customer experiences, this course covers it all. You will also learn how to utilize AI for social media marketing, search engine optimization (SEO), and targeted advertising to boost conversions effectively.

The curriculum is enriched with practical assignments, case studies, and live projects, ensuring you not only grasp theoretical concepts but also apply them to real-world scenarios. Our industry-expert instructors bring years of experience, guiding you through hands-on sessions that facilitate interactive learning and foster collaboration among peers.

Beyond technical skills, this course emphasizes employability through mentorship, career counseling, and an internship opportunity, positioning you as a sought-after candidate in the job market. By the end of the program, you will receive a Certificate of Completion, along with recognition for your internship, empowering you to showcase your skills and knowledge to potential employers.

Embark on your journey toward professional excellence with the AI-Powered Digital Marketing Mastery Course at AppliedTech. Equip yourself with the skills needed to thrive in the digital age and lead your marketing efforts into the future!

Hours Live Sessions from Renowned Practitioners

0 +
Assignments and Quizzes
0 +
Case Studies
0 +
Discussion Boards
0 +
Live Webinars with Industry Practitioners
0
Month Internship with AppliedTech
0

Get Ahead with AI-Powered Digital Marketing Mastery Certificate!

Certificate of Completion for the Program
Internship Certificate from Participating Companies
Letter of Recommendation

Languages and Tools Covered :

Why choose AppliedTech :

At AppliedTech, we envision a future where individuals are empowered to navigate and excel in the rapidly evolving realm of technology. Our dedicated team is committed to revolutionizing the learning experience, instilling innovative thinking and adaptability to keep pace with the ever-changing technological landscape.

Our mission at AppliedTech is to cultivate a culture of continuous learning and development, nurturing individuals from diverse backgrounds, whether rooted in the world of IT or branching out into non-IT domains. We firmly believe that knowledge has no boundaries, and we are dedicated to breaking down barriers to make technology education accessible to all.

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 AI-Powered Digital MarketingProfessional?

Are you ready to take your digital marketing strategies to the next level? Welcome to the AI-Powered Digital Marketing Mastery Course, where cutting-edge innovation meets marketing excellence. This comprehensive program is designed to help you harness the full potential of artificial intelligence to revolutionize your marketing campaigns.

In this advanced course, we explore the realm of AI-driven marketing, providing you with the knowledge and skills necessary to thrive in today’s fast-paced digital environment. By utilizing AI technologies, data-driven insights, and automation techniques, you’ll learn how to elevate your marketing efforts and achieve remarkable success.

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
    • 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)
    •  
    •  
    •  
      1.  
    • 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
    • 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)
    •  
    •  
    •  
      1.  

    Testimonials

    What they say

    “The AI-powered Digital Marketing course was a game-changer for me. I learned how to leverage AI tools to analyze data, automate tasks, and personalize campaigns at scale. It has dramatically improved the efficiency and effectiveness of my strategies. I highly recommend this course to anyone serious about modern marketing."
    Rohit V.Digital Marketing Strategist
    “This course taught me how AI can revolutionize social media marketing. From automating content scheduling to optimizing ad targeting, I now use AI-driven insights to get better results for my clients. The hands-on projects were particularly useful in building my confidence with these new tools.”
    Sneha Bhardwaj Sneha Bhardwaj
    “The AI-powered marketing techniques I learned in this course helped me optimize my e-commerce store’s performance. AI-driven customer segmentation and personalized marketing strategies have increased my sales and customer engagement. This course is a must for anyone running an online business."
    Akash Patel E-commerce Entrepreneur
    “The course gave me a deep understanding of how AI can enhance data analysis and decision-making in digital marketing. From predictive analytics to real-time customer insights, I now use AI to make data-driven marketing decisions with more precision. It’s transformed my approach to campaign optimization."
    Pooja MenonMarketing Analyst
    “AI has completely changed the way I approach branding, and this course helped me understand how. From AI-powered customer journey mapping to hyper-personalized content generation, I’m now equipped to build more impactful brand experiences. The mentors were fantastic @ AppliedTech, providing clear and actionable insights."
    Rihan DeshmukhBrand Manager

    AI Powered Digital Marketing Course FAQs

    The AI-Powered Digital Marketing Mastery Course is a comprehensive program designed to equip participants with the skills and knowledge to leverage artificial intelligence in digital marketing strategies. This course explores the latest tools and techniques, helping marketers optimize campaigns and enhance customer engagement.

    This course is ideal for digital marketing professionals, business owners, and anyone interested in harnessing AI to improve their marketing efforts. Whether you are new to marketing or looking to enhance your existing skills, this program provides valuable insights for all levels.

    No prior experience or technical knowledge is required to enroll in this course. It is designed to be accessible to individuals at all skill levels, providing a foundational understanding of digital marketing and AI concepts.

    The course covers a wide range of topics, including AI-driven marketing strategies, data analytics, customer segmentation, content creation, automated marketing tools, and performance measurement. Participants will gain a holistic understanding of how to integrate AI into their marketing practices.

    The course is delivered through a combination of online lectures, interactive workshops, and hands-on projects. This format allows participants to learn at their own pace while engaging with the material and applying their knowledge in practical scenarios.

    Yes, participants will receive a certification upon successful completion of the course, validating their expertise in AI-powered digital marketing.

    Yes, you will retain access to all course materials after completion, allowing you to review the content and continue your learning journey at your convenience.

    Yes, support is available throughout the course. Participants can reach out to instructors and peers for assistance, ensuring a collaborative and supportive learning environment.

    Empowering future through AppliedTech excellence.

    Icon-facebook Linkedin Youtube Instagram

    Quick Link

    Courses

    Contact Us

    Copyright © 2023 Appliedtech, All rights reserved.