Artificial Intelligence Engineer
Ranked #1 Masters in Artificial Intelligence Program by Career Karma
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Earn a recognized AI Engineer certification to boost your career
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Learn Machine Learning, NLP, Deep Learning, Generative AI, and more
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Masters in AI provides real-world projects using industry-aligned tools
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Earn IBM certificates and benefit from Masterclasses by IBM experts
In Collaboration with:
Delivered by:
Program Fees
RM 5,900
Program duration
11 Months
Learning Format
Live, Online, Interactive
Why Join this Program
IBM Advantage
Earn IBM certificates for IBM courses. Access to hackathons, masterclasses, and AMA sessions
Generative AI Edge
Live sessions on the latest AI trends, Generative AI tools, prompt engineering, and more
Applied Learning
Capstone and 25+ industry-relevant AI projects to ensure comprehensive learning
Top-notch AI course
Comprehensive AI curriculum with live online classes by industry experts
Masters in AI Overview
Elevate your career with the Masters in AI offered in collaboration with IBM. This program equips you with essential AI skills through industry-relevant training, live interactive sessions, and hands-on projects. Gain expertise in Python, ML, deep learning, NLP, and more, all designed to prepare you for a successful career in AI engineering.
Key Features
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Industry-recognized AI Engineer Master’s certificate from Simplilearn
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Dedicated live sessions by faculty of industry experts
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Masterclasses from IBM experts
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Industry-recognized IBM certifications for IBM courses
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Ask-Me-Anything (AMA) sessions with IBM leadership
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Exclusive hackathons conducted by IBM
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Capstone from 3 domains and 25+ projects
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Lifetime access to self-paced learning content
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Learn AI to build your career in Generative AI
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Program crafted to initiate your journey as an AI Engineer
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Integrated labs for hands-on learning experience
Masters in Artificial Intelligence Advantage
Get certified in Artificial Intelligence with our Masters in AI program and earn AI Engineer and IBM certificates to boost your career prospects. Benefit from exclusive access to expert-led masterclasses and interactive AMAs with industry leaders.
AI Masters Certificate
Earn Your AI Engineer Certificate
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Industry-recognized certificate by Simplilearn
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Dedicated live sessions by faculty of industry experts
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Lifetime access to self-paced learning content
IBM Certificate
Get Ahead With IBM Advantage
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Content and certificate by IBM
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Masterclasses by IBM experts
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Hackathons and AMA Sessions
About Masters in Artificial Intelligence
Artificial intelligence (AI) and AI engineering have been witnessing significant growth, and numerous statistical indicators support the attractiveness of becoming an AI engineer.
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According to the World Economic Forum, the demand for AI and machine learning specialists is expected to increase by 60% by 2025.
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In the U.S., the Bureau of Labor Statistics projected a 15% growth in employment for computer and information research scientists (which includes AI engineers) from 2019 to 2029, much faster than the average for all occupations.
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AI engineers typically command higher-than-average salaries due to their specialized skill set and high demand. In the U.S., according to Glassdoor, the average base pay for AI engineers exceeded $100,000 per year, and senior AI engineers often earned considerably more.
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Numerous industries have been embracing AI technologies. This adoption spans sectors like healthcare, finance, automotive, retail, and more, signifying many opportunities for AI engineers to apply their skills across various domains.
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The Global Generative AI market has huge potential with the current market trends. It is expected to grow to $667.9 billion by 2030.
You will obtain certificates from IBM and Simplilearn upon completing these courses. These certificates will attest to your abilities as an expert in AI. In addition, you will receive the following:
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Masterclass by IBM experts
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Ask-Me-Anything sessions with IBM leadership
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Hackathons conducted by IBM
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IBM Certificates for IBM courses
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Industry-recognized Program completion certificate from Simplilearn
You will be able to help you find a dream career after completing the AI Masters Program in collaboration with IBM. AI-certified experts are well-suited for the following positions:
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Artificial Intelligence Engineer
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Data Scientist
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Analytics Manager/Lead
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Machine Learning Engineer
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Statistical Programming Specialist
Our committed team is here to assist you through email, chat, calls, and community forums. On-demand support is available to guide you through masters in artificial intelligence. Once you finish masters in ai, you will gain lifelong access to our community forum.
Masters in Artificial Intelligence Learning Path
Accelerate your career with our top-ranked Masters in Artificial Intelligence. Learn the skills needed to showcase your machine learning skills through our curated learning path.
- 01
Delve into AI basics and generative AI principles. Grasp the importance of explainable AI. Employ prompt engineering to enhance generative AI performance. Understand ChatGPT's mechanisms, features, and constraints. Explore varied ChatGPT applications. Gain foresight into generative AI's future and challenges.
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Discover procedural and object-oriented programming. Uncover Python's benefits. Set up Python and its IDE. Master Jupyter Notebook. Apply Python basics like identifiers, indentation, and comments. Understand data types, operators, and string functions. Explore Python loops and variable scopes. Learn about OOP, its features, and elements like methods, attributes, and access modifiers.
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Kickstart your learning of Python for Data Science with this Data Scientist course and familiarize yourself with programming, tastefully crafted by IBM.
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Learn Python's tools and techniques used for data science
Experience vital skill development in Python for various data science roles
Engage in a blended learning approach and learn data science concepts
Explore practical applications for a hands-on understanding
Propel your data science career with Simplilearn's specialized training
Aligned to
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Gain career success with our comprehensive Machine Learning course
Learn from 40+ hrs of Applied Learning and interactive labs
Complete 4 hands-on projects to solidify your understanding
Receive mentoring support throughout your learning journey
Master essential ML concepts for certification
Gain the skills needed to become a successful machine learning engineer
Aligned to
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Differentiate deep learning from machine learning. Explore various neural network types. Excel at forward and backward propagation in deep neural networks. Introduce modeling and performance enhancement in deep learning. Understand hyperparameter tuning and model interpretability. Learn dropout and early stopping implementation. Master CNNs, object detection, and RNN fundamentals. Grasp PyTorch basics and neural network creation.
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The capstone project allows you to implement the skills you learned throughout this bootcamp. You will solve industry-specific challenges by leveraging various AI and ML techniques. The capstone project will help you showcase your expertise to employers.
Learning Path
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Enhance ML capabilities with deep learning techniques. Acquire expertise in TensorFlow and Keras. Master deep learning principles. Build artificial neural networks. Explore data abstraction layers. Unleash data's potential for AI progress.
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Dive into advanced computer vision and deep learning. Focus on practical skills and deep understanding. Explore image formation, CNNs, and object detection. Learn about image segmentation and generative models. Delve into optical character recognition. Explore distributed and parallel computing. Investigate Explainable AI (XAI). Master deep learning model deployment techniques.
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The Natural Language Processing course gives you a detailed look at the science of applying machine learning algorithms to process large amounts of natural language data. NLP is driving the growth of the AI market, and this course helps you develop the skills required to become an NLP Engineer.
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Hands - On Reinforcement Learning with Python
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Grasp transformers' significance in contemporary AI. Evaluate neural networks' suitability for generative tasks. Differentiate between VAEs, GANs, transformers, and autoencoders. Assess ideal scenarios for each generative model. Evaluate attention mechanisms' efficacy in diverse generative tasks. Analyze GPT and BERT architectural distinctions and objectives in generative AI models.
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Attend these online live sessions delivered by industry experts to gain insights about the latest advancements in the AI space. Some of the areas and concepts covered include Generative AI and its Applications, leveraging the power of generative modeling to build innovative products, building and deploying GPT-powered applications. Learn ChatGPT, its architecture, training methodology, and business applications. {*Areas mentioned above are subject to change}
Elective:
- 01
Lesson 01: Introduction
1.01 Introduction
- 02
Lesson 01 - Course Introduction 1.1 Course Introduction
Lesson 02 - Programming Basics
2.1 Learning Objectives 2.2 Introduction to Software 2.3 Categories of Software 2.4 Programming Models 2.5 Program Structure 2.6 Key Takeaways
Lesson 03 - Introduction to Python Programming
3.1 Learning Objectives 3.2 Introduction to Python 3.3 Python Installation 3.4 Python IDE 3.5 First Python Program 3.6 Key Takeaways
Lesson 04 - Python Data Types and Operators
4.1 Learning Objectives 4.2 Data Types and Data Assignment 4.3 Operators in Python 4.4 Strings in Python 4.5 Key Takeaways
Lesson 05 - Conditional Statements and Loops
5.1 Learning Objectives 5.2 Decision Control Structures in Python 5.3 Loops 5.4 Loop Control Statements 5.5 Loop Else Statements 5.6 Key Takeaways
Lesson 06 - Python Functions
6.1 Learning Objectives 6.2 Functions and Its Advantages 6.3 Function Arguments 6.4 Return Statement 6.5 Scope of a Variable 6.6 Generators Function 6.7 Function Types 6.8 Key Takeaways
Lesson 07 - OOPs Concepts with Python
7.1 Learning Objectives 7.2 Object-Oriented Programming Language 7.3 Objects and Classes 7.4 Methods and Attributes 7.5 Access Modifiers 7.6 Encapsulation 7.7 Inheritance 7.8 Polymorphism 7.9 Abstraction 7.10 Key Takeaways
Lesson 08 - Threading
8.1 Learning Objectives 8.2 Introduction to Threading 8.3 Introduction to Multi-Threading 8.4 Creating a New Thread 8.5 Synchronizing Threads 8.6 Key Takeaways
- 03
Lesson 01: Introduction
Python for Data Science
- 04
Lesson 01 - Course Introduction
1.01 Course Introduction 1.02 What You Will Learn
Lesson 02 - Introduction to Data Science
2.01 Introduction 2.02 Data Science and Its Applications 2.03 The Data Science Process: Part 1 2.04 The Data Science Process: Part 2 2.05 Recap
Lesson 03 - Essentials of Python Programming
3.01 Introduction 3.02 Setting Up Jupyter Notebook: Part 1 3.03 Setting Up Jupyter Notebook: Part 2 3.04 Python Functions 3.05 Python Types and Sequences 3.06 Python Strings Deep Dive 3.07 Python Demo: Reading and Writing CSV Files 3.08 Date and Time in Python 3.09 Objects in Python Map 3.10 Lambda and List Comprehension 3.11 Why Python for Data Analysis? 3.12 Python Packages for Data Science 3.13 StatsModels Package: Part 1 3.14 StatsModels Package: Part 2 3.15 Scipy Package 3.16 Recap 3.17 Spotlight
Lesson 04 - NumPy
4.01 Introduction 4.02 Fundamentals of NumPy 4.03 Array Shapes and Axes in NumPy: Part A 4.04 NumPy Array Shapes and Axes: Part B 4.05 Arithmetic Operations 4.06 Conditional Logic 4.07 Common Mathematical and Statistical Functions in NumPy 4.08 Indexing and Slicing: Part 1 4.09 Indexing and Slicing: Part 2 4.10 File Handling 4.11 Recap
Lesson 05 - Linear Algebra
5.01 Introduction 5.02 Introduction to Linear Algebra 5.03 Scalars and Vectors 5.04 Dot Product of Two Vectors 5.05 Linear Independence of Vectors 5.06 Norm of a Vector 5.07 Matrix 5.08 Matrix Operations 5.09 Transpose of a Matrix 5.10 Rank of a Matrix 5.11 Determinant of a Matrix and Identity Matrix or Operator 5.12 Inverse of a Matrix and Eigenvalues and Eigenvectors 5.13 Calculus in Linear Algebra 5.14 Recap
Lesson 06 - Statistics Fundamentals
6.01 Introduction 6.02 Importance of Statistics with Respect to Data Science 6.03 Common Statistical Terms 6.04 Types of Statistics 6.05 Data Categorization and Types 6.06 Levels of Measurement 6.07 Measures of Central Tendency 6.08 Measures of Dispersion 6.09 Random Variables 6.10 Sets 6.11 Measures of Shape (Skewness) 6.12 Measures of Shape (Kurtosis) 6.13 Covariance and Correlation 6.14 Recap
Lesson 07 - Probability Distribution
7.01 Introduction 7.02 Probability, Its Importance, and Probability Distribution 7.03 Probability Distribution: Binomial Distribution 7.04 Probability Distribution: Poisson Distribution 7.05 Probability Distribution: Normal Distribution 7.06 Probability Distribution: Uniform Distribution 7.07 Probability Distribution: Bernoulli Distribution 7.08 Probability Density Function and Mass Function 7.09 Cumulative Distribution Function 7.10 Central Limit Theorem 7.11 Estimation Theory 7.12 Recap
Lesson 08 - Advanced Statistics
8.01 Introduction 8.02 Distribution 8.03 Kurtosis, Skewness, and Student's T-Distribution 8.04 Hypothesis Testing and Mechanism 8.05 Hypothesis Testing Outcomes: Type I and II Errors 8.06 Null Hypothesis and Alternate Hypothesis 8.07 Confidence Intervals 8.08 Margins of Error 8.09 Confidence Level 8.10 T-Test and P-Values (Lab) 8.11 Z-Test and P-Values 8.12 Comparing and Contrasting T-Test and Z-Test 8.13 Bayes Theorem 8.14 Chi-Square Distribution 8.15 Chi-Square Distribution: Demo 8.16 Chi-Square Test and Goodness of Fit 8.17 Analysis of Variance (ANOVA) 8.18 ANOVA Terminologies 8.19 Assumptions and Types of ANOVA 8.20 Partition of Variance Using Python 8.21 F-Distribution 8.22 F-Distribution Using Python 8.23 F-Test 8.24 Recap 8.25 Spotlight
Lesson 09 - Pandas
9.01 Introduction 9.02 Introduction to Pandas 9.03 Pandas Series 9.04 Querying a Series 9.05 Pandas DataFrames 9.06 Pandas Panel 9.07 Common Functions in Pandas 9.08 Pandas Functions: Data Statistical Function, Windows Function 9.09 Pandas Function: Data and Timedelta 9.10 IO Tools: Explain All the Read Functions 9.11 Categorical Data 9.12 Working with Text Data 9.13 Iteration 9.14 Sorting 9.15 Plotting with Pandas 9.16 Recap
Lesson 10 - Data Analysis
10.01 Introduction 10.02 Understanding Data 10.03 Types of Data: Structured, Unstructured, Messy, etc. 10.04 Working with Data: Choosing Appropriate Tools, Data Collection, Data Wrangling 10.05 Importing and Exporting Data in Python 10.06 Regular Expressions in Python 10.07 Manipulating Text with Regular Expressions 10.08 Accessing Databases in Python 10.09 Recap 10.10 Spotlight
Lesson 11 - Data Wrangling
11.01 Introduction 11.02 Pandorable or Idiomatic Pandas Code 11.03 Loading, Indexing, and Reindexing 11.04 Merging 11.05 Memory Optimization in Python 11.06 Data Preprocessing: Data Loading and Dropping Null Values 11.07 Data Preprocessing: Filling Null Values 11.08 Data Binning, Formatting, and Normalization 11.09 Data Binning Standardization 11.10 Describing Data 11.11 Recap
Lesson 12 - Data Visualization
12.01 Introduction 12.02 Principles of Information Visualization 12.03 Visualizing Data Using Pivot Tables 12.04 Data Visualization Libraries in Python: Matplotlib 12.05 Graph Types 12.06 Data Visualization Libraries in Python: Seaborn 12.07 Data Visualization Libraries in Python: Plotly 12.08 Data Visualization Libraries in Python: Bokeh 12.09 Using Matplotlib to Plot Graphs 12.10 Plotting 3D Graphs for Multiple Columns Using Matplotlib 12.11 Using Matplotlib with Other Python Packages 12.12 Using Seaborn to Plot Graphs 12.13 Plotting 3D Graphs for Multiple Columns Using Seaborn 12.14 Introduction to Plotly 12.15 Introduction to Bokeh 12.16 Recap
Lesson 13 - End-to-End Statistics Application with Python
13.01 Introduction 13.02 Basic Statistics with Python: Problem Statement 13.03 Basic Statistics with Python: Solution 13.04 Scipy for Statistics: Problem Statement 13.05 Scipy for Statistics: Solution 13.06 Advanced Statistics with Python 13.07 Advanced Statistics with Python: Solution 13.08 Recap 13.09 Spotlight
- 05Machine Learning using Python
Lesson 01 - Course Introduction
1.01 Course Introduction 1.02 What You Will Learn
Lesson 02 - Introduction to Machine Learning
2.01 Introduction 2.02 What Is Machine Learning? 2.03 Types of Machine Learning 2.04 Machine Learning Pipeline and MLOP's 2.05 Introduction to Python Packages Used in Machine Learning 2.06 Recap
Lesson 03 - Supervised Learning
3.01 Introduction 3.02 Supervised Learning 3.03 Applications of Supervised Learning 3.04 Preparing and Shaping Data 3.05 What is Overfitting and Underfitting? 3.06 Detecting and Preventing Overfitting and Underfitting 3.07 Regularization 3.08 Recap
Lesson 04 - Regression and Applications
4.01 Introduction 4.02 What is Regression? 4.03 Regression Types: Introduction 4.04 Linear Regression 4.05 Working with Linear Regression 4.06 Critical Assumptions for Linear Regression 4.07 Logistic Regression 4.08 Data Exploration Using SMOTE 4.09 Over Sampling Using SMOTE 4.10 Polynomial Regression 4.11 Data Preparation Model Building and Performance Evaluation Part A 4.12 Ridge Regression 4.13 Data Preparation Model Building: Part B 4.14 LASSO Regression 4.15 Data Preparation Model Building: Part C 4.16 Recap 4.17 Spotlight
Lesson 05 - Classification and Applications
5.01 Introduction 5.02 What are Classification Algorithms? 5.03 Types of Classification 5.04 Types and Selection of Performance Parameters 5.05 Naive Bayes 5.06 Applying Naive Bayes Classifier 5.07 Stochastic Gradient Descent 5.08 Applying Stochastic Gradient Descent 5.09 K Nearest Neighbors 5.10 Applying K Nearest Neighbors 5.11 Decision Tree 5.12 Applying Decision Tree 5.13 Random Forest 5.14 Applying Random Forest 5.15 Boruta Explained 5.16 Automatic Feature Selection with Boruta 5.17 Support Vector Machine 5.18 Applying Support Vector Machine 5.19 Cohen's Kappa Measure 5.20 Recap
Lesson 06 - Unsupervised Algorithms
6.01 Introduction 6.02 What are Unsupervised Algorithms? 6.03 Types of Unsupervised Algorithms: Clustering and Associative 6.04 When to Use Unsupervised Algorithms? 6.05 Visualizing Outputs 6.06 Performance Parameters 6.07 Clustering Types 6.08 Hierarchical Clustering 6.09 Applying Hierarchical Clustering 6.10 K-Means Clustering: Part 1 6.11 K-Means Clustering: Part 2 6.12 Applying K-Means Clustering 6.13 KNN - K Nearest Neighbors 6.14 Outlier Detection 6.15 Outlier Detection Algorithms in PyOD 6.16 Demo: KNN for Anomaly Detection 6.17 Principal Component Analysis 6.18 Applying Principal Component Analysis (PCA) 6.19 Correspondence Analysis & Multiple Correspondence Analysis (MCA) 6.20 Singular Value Decomposition 6.21 Applying Singular Value Decomposition 6.22 Independent Component Analysis 6.23 Applying Independent Component Analysis 6.24 BIRCH 6.25 Applying BIRCH 6.26 Recap 6.27 Spotlight
Lesson 07 - Ensemble Learning
7.01 Introduction 7.02 What is Ensemble Learning? 7.03 Categories in Ensemble Learning 7.04 Sequential Ensemble Technique 7.05 Parallel Ensemble Technique 7.06 Types of Ensemble Methods 7.07 Bagging 7.08 Demo: Bagging 7.09 Boosting 7.10 Demo: Boosting 7.11 Stacking 7.12 Demo: Stacking 7.13 Reducing Errors with Ensembles 7.14 Applying Averaging and Max Voting 7.15 Hello World TensorFlow 7.16 Hands-on with TensorFlow: Part A 7.17 Keras 7.18 Hands-on with TensorFlow: Part B 7.19 Recap
Lesson 08 - Recommender System
8.01 Introduction 8.02 How do Recommendation Engines Work? 8.03 Recommendation Engine: Use Cases 8.04 Examples of Recommender Systems and Their Designs 8.05 Leveraging PyTorch to Build a Recommendation Engine 8.06 Collaborative Filtering and Memory-Based Modeling 8.07 Item-Based Collaborative Filtering 8.08 User-Based Collaborative Filtering 8.09 Model-Based Collaborative Filtering 8.10 Dimensionality Reduction and Matrix Factorization 8.11 Accuracy Matrices in ML 8.12 Recap 8.13 Spotlight
Math Refresher (FREE COURSE)Lesson 01: Course Introduction
1.01 About Simplilearn 1.02 Introduction to Mathematics 1.03 Types of Mathematics 1.04 Applications of Math in Data Industry 1.05 Learning Path 1.06 Course Components
Lesson 02: Probability and Statistics
2.01 Learning Objectives 2.02 Basics of Statistics and Probability 2.03 Introduction to Descriptive Statistics 2.04 Measures of Central Tendencies 2.05 Measures of Asymmetry 2.06 Measures of Variability 2.07 Measures of Relationship 2.08 Introduction to Probability 2.09 Key Takeaways 2.10 Knowledge Check
Lesson 03: Coordinate Geometry
3.01 Learning Objectives 3.02 Introduction to Coordinate Geometry 3.03 Coordinate Geometry Formulas 3.04 Key Takeaways 3.05 Knowledge Check
Lesson 04: Linear Algebra
4.01 Learning Objectives 4.02 Introduction to Linear Algebra 4.03 Forms of Linear Equation 4.04 Solving a Linear Equation 4.05 Introduction to Matrices 4.06 Matrix Operations 4.07 Introduction to Vectors 4.08 Types and Properties of Vectors 4.09 Vector Operations 4.10 Key Takeaways 4.11 Knowledge Check
Lesson 05: Eigenvalues, Eigenvectors, and Eigendecomposition
5.01 Learning Objectives 5.02 Eigenvalues 5.03 Eigenvectors 5.04 Eigendecomposition 5.05 Key Takeaways 5.06 Knowledge Check
Lesson 06: Introduction to Calculus
6.01 Learning Objectives 6.02 Basics of Calculus 6.03 Differential Calculus 6.04 Differential Formulas 6.05 Integral Calculus 6.06 Integration Formulas 6.07 Key Takeaways 6.08 Knowledge Check
Statistics Essential for Data Science (FREE COURSE)Lesson 01: Course Introduction
1.01 Course Introduction 1.02 What Will You Learn
Lesson 02: Introduction to Statistics
2.01 Learning Objectives 2.02 What Is Statistics 2.03 Why Statistics 2.04 Difference between Population and Sample 2.05 Different Types of Statistics 2.06 Importance of Statistical Concepts in Data Science 2.07 Application of Statistical Concepts in Business 2.08 Case Studies of Statistics Usage in Business 2.09 Applications of Statistics in Business: Time Series Forecasting 2.10 Applications of Statistics in Business Sales Forecasting 2.11 Recap
Lesson 03: Understanding the Data
3.01 Learning Objectives 3.02 Types of Data in Business Contexts 3.03 Data Categorization and Types of Data 3.04 Types of Data Collection 3.05 Types of Data 3.06 Structured vs. Unstructured Data 3.07 Sources of Data 3.08 Data Quality Issues 3.09 Recap
Lesson 04: Descriptive Statistics
4.01 Learning Objectives 4.02 Descriptive Statistics 4.03 Mathematical and Positional Averages 4.04 Measures of Central Tendency: Part A 4.05 Measures of Central Tendency: Part B 4.06 Measures of Dispersion 4.07 Range Outliers Quartiles Deviation 4.08 Mean Absolute Deviation (MAD) Standard Deviation Variance 4.09 Z Score and Empirical Rule 4.10 Coefficient of Variation and Its Application 4.11 Measures of Shape 4.12 Summarizing Data 4.13 Recap 4.14 Case Study One: Descriptive Statistics
Lesson 05: Data Visualization
5.01 Learning Objectives 5.02 Data Visualization 5.03 Basic Charts 5.04 Advanced Charts 5.05 Interpretation of the Charts 5.06 Selecting the Appropriate Chart 5.07 Charts Do's and Don'ts 5.08 Storytelling with Charts 5.09 Data Visualization: Example 5.10 Recap 5.11 Case Study Two: Data Visualization
Lesson 06: Probability
6.01 Learning Objectives 6.02 Introduction to Probability 6.03 Probability Example 6.04 Key Terms in Probability 6.05 Conditional Probability 6.06 Types of Events: Independent and Dependent 6.07 Addition Theorem of Probability 6.08 Multiplication Theorem of Probability 6.09 Bayes Theorem 6.10 Recap
Lesson 07: Probability Distributions
7.01 Learning Objectives 7.02 Probability Distribution 7.03 Random Variable 7.04 Probability Distributions Discrete vs. Continuous: Part A 7.05 Probability Distributions Discrete vs. Continuous: Part B 7.06 Commonly Used Discrete Probability Distributions: Part A 7.07 Discrete Probability Distributions: Poisson 7.08 Binomial by Poisson Theorem 7.09 Commonly Used Continuous Probability Distribution 7.10 Application of Normal Distribution 7.11 Recap
Lesson 08: Sampling and Sampling Techniques
8.01 Learning Objectives 8.02 Introduction to Sampling and Sampling Errors 8.03 Advantages and Disadvantages of Sampling 8.04 Probability Sampling Methods: Part A 8.05 Probability Sampling Methods: Part B 8.06 Non-Probability Sampling Methods: Part A 8.07 Non-Probability Sampling Methods: Part B 8.08 Uses of Probability Sampling and Non-Probability Sampling 8.09 Sampling 8.10 Probability Distribution 8.11 Theorem Five Point One 8.12 Center Limit Theorem 8.13 Sampling Stratified: Sampling Example 8.14 Probability Sampling: Example 8.15 Recap 8.16 Case Study Three: Sample and Sampling Techniques 8.17 Spotlight
Lesson 09: Inferential Statistics
9.01 Learning Objectives 9.02 Inferential Statistics 9.03 Hypothesis and Hypothesis Testing in Businesses 9.04 Null and Alternate Hypothesis 9.05 P Value 9.06 Levels of Significance 9.07 Type One and Type Two Errors 9.08 Z Test 9.09 Confidence Intervals and Percentage Significance Level: Part A 9.10 Confidence Intervals: Part B 9.11 One Tail and Two Tail Tests 9.12 Notes to Remember for Null Hypothesis 9.13 Alternate Hypothesis 9.14 Recap 9.15 Case Study Four: Inferential Statistics
Lesson 10: Application of Inferential Statistics
10.01 Learning Objectives 10.02 Bivariate Analysis 10.03 Selecting the Appropriate Test for EDA 10.04 Parametric vs. Non-Parametric Tests 10.05 Test of Significance 10.06 Z Test 10.07 T Test 10.08 Parametric Tests ANOVA 10.09 Chi-Square Test 10.10 Sign Test 10.11 Kruskal Wallis Test 10.12 Mann Whitney Wilcoxon Test 10.13 Run Test for Randomness 10.14 Recap
Lesson 11: Relation between Variables
11.01 Learning Objectives 11.02 Correlation 11.03 Karl Pearson's Coefficient of Correlation 11.04 Karl Pearson's: Use Cases 11.05 Correlation Example 11.06 Spearman's Rank Correlation Coefficient 11.07 Causation 11.08 Example of Regression 11.09 Coefficient of Determination 11.10 Quantifying Quality 11.11 Recap
Lesson 12: Application of Statistics in Business
12.01 Learning Objectives 12.02 How to Use Statistics in Day-to-Day Business 12.03 Example: How to Not Lie with Statistics 12.04 How to Not Lie with Statistics 12.05 Lying Through Visualizations 12.06 Lying About Relationships 12.07 Recap 12.08 Spotlight
Lesson 13: Assisted Practice
Assisted Practice: Problem Statement
Assisted Practice: Solution
- 06
Lesson 01: Course Introduction
1.01 Course Introduction 1.02 What You Will Learn
Lesson 02: Introduction to Deep Learning
2.01 Introduction 2.02 Introduction to Deep Learning 2.03 Brief History of AI 2.04 Motivation for Deep Learning 2.05 Difference Between Deep Learning and Machine Learning 2.06 Deep Learning Successes in the Last Decade 2.07 Applications of Deep Learning 2.08 Challenges of Deep Learning 2.09 Deep Learning Frameworks 2.10 Full Cycle of a Deep Learning Project 2.11 Neural Networks and Types of Neural Networks 2.12 Recap
Lesson 03: Perceptron
3.01 Introduction 3.02 What is a Perceptron 3.03 Forward Propagation in Perceptron 3.04 Role of Activation Functions 3.05 Backward Propagation in Perceptron 3.06 Gradient Descent Algorithm 3.07 Limitations of Perceptron 3.08 Recap 3.09 Spotlight: Introduction to Artificial Intelligence
Lesson 04: Deep Neural Networks (DNN)
4.01 Introduction 4.02 What is a Deep Neural Network (DNN) and Why is it Useful? 4.03 Loss Functions: Part 1 4.04 Loss Functions: Part 2 4.05 Forward Propagation in DNN 4.06 Backward Propagation in DNN 4.07 Introduction to TensorFlow 4.08 Training DNN Using TensorFlow 4.09 Introduction to TensorFlow Playground 4.10 Recap
Lesson 05: TensorFlow 2
5.01 Introduction 5.02 Overview of TensorFlow 5.03 Installation of TensorFlow 2 5.04 Introduction to Tensors 5.05 Sequential APIs in TensorFlow 5.06 Functional APIs in TensorFlow 5.07 Keras: An Introduction 5.08 Recap
Lesson 06: Model Optimization and Performance Improvement
6.01 Introduction 6.02 Introduction to Optimization Algorithms 6.03 Introduction to SGD 6.04 Implementation of SGD 6.05 Introduction to Momentum 6.06 Implementation of Momentum 6.07 Introduction to Adagrad 6.08 Implementation of Adagrad 6.09 Introduction to Adadelta 6.10 Implementation of Adadelta 6.11 Introduction to RMSProp 6.12 Implementation of RMSProp 6.13 Introduction to Adam 6.14 Implementation of Adam 6.15 What is Batch Normalization? 6.16 Batch Normalization Implementation 6.17 Exploding and Vanishing Gradients 6.18 Introduction to Hyperparameter Tuning 6.19 Implementation of Hyperparameter Tuning 6.20 Model Interpretability 6.21 Dropout and Early Stopping 6.22 Implementation of Dropout 6.23 Implementation of Early Stopping 6.24 Recap 6.25 Spotlight: Introduction to Neural Networks and Computer Vision
Lesson 07: Convolutional Neural Networks (CNN)
7.01 Introduction 7.02 Getting Started with Image Data 7.03 What is a Convolutional Neural Network (CNN)? 7.04 CNN Architecture 7.05 ResNet 50 7.06 Filters in CNN 7.07 Working of CNN 7.08 Pooling in CNN 7.09 Image Classification Using CNN 7.10 Introduction to TensorBoard 7.11 Recap
Lesson 08: Transfer Learning
8.01 Introduction 8.02 Introduction to Transfer Learning 8.03 How to Select Pre-Trained Models 8.04 Advantages of Transfer Learning 8.05 Recap
Lesson 09: Object Detection
9.01 Introduction 9.02 Introduction to Object Detection 9.03 Object Detection for Multiple Objects 9.04 High-Level Overview of YOLO v3 Algorithm 9.05 Dataset Preparation for YOLO v3 9.06 Object Detection with YOLO v3: Part A 9.07 Object Detection with YOLO v3: Part B 9.08 Introduction to TF Lite 9.09 Converting TF Model into TF Lite Model 9.10 Recap 9.11 Spotlight: Advanced Computer Vision
Lesson 10: Recurrent Neural Networks (RNN)
10.01 Introduction 10.02 What is Sequence Modeling? 10.03 Introduction to Recurrent Neural Networks (RNN) 10.04 Architecture of RNN 10.05 Forward and Back Propagation in RNN 10.06 Introduction to Hybrid Modeling 10.07 Architecture of a CNN and RNN Hybrid Model 10.08 Recap
Lesson 11: Transformer Models for NLP
11.01 Introduction 11.02 Overview of Transformer Models 11.03 Architecture of the Transformer Model 11.04 Introduction to BERT Model (BERT Architecture and Use Cases) 11.05 Recap
Lesson 12: Getting Started with Autoencoders
12.01 Introduction 12.02 Introduction to Unsupervised Deep Learning 12.03 What are Autoencoders? 12.04 Architecture of Autoencoders 12.05 Use Cases of Autoencoders 12.06 Training Autoencoders 12.07 Recap
Lesson 13: PyTorch
13.01 Introduction 13.02 Introduction to PyTorch 13.03 Getting Started with PyTorch 13.04 Creating a Neural Network in PyTorch 13.05 Recap 13.06 Spotlight: Advanced Deep Learning
Course Content
12+ Skills Covered
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Generative AI
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Prompt Engineering
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ChatGPT
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Data Wrangling
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Python Programming
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NumPy and SciPy
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Supervised and Unsupervised Learning
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Linear and Logistic Regression
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Deep Learning
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Reinforcement Learning
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NLP
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CNN and RNN
15+ Tools Covered
Industry Projects
Project 1
Sales Analysis for Business Growth
Analyze the sales data of a retail clothing company and support the management in formulating their sales and growth strategy.
Disclaimer - The projects have been built leveraging real publicly available datasets from organizations.
Join the AI industry
The artificial intelligence market size was valued at USD 150 billion in 2023 and is expected to reach USD 1345 Billion by 2030, growing at a CAGR of 36.8%, as per the Markets and Markets report.
$15.7 trillion
Expected Total Contribution Of AI To The Global Economy By 2030
Source: US bureau of Labor
64%
Businesses expect AI to increase their productivity
Source: Dice 2020 report
155K-264K
Average Annual Salary
Source: Glassdoor
Companies hiring AI Engineer
Batch Profile
This Data Analytics Course caters to working professionals across industries. Learner diversity adds richness to class discussions and interactions.
Industry
Learner Reviews
Sajitha Smiley Masilla Mony
Lecturer
The course curriculum was beneficial in real-world scenarios and was up-to-date. The faculties are skillful and well experienced. The class sessions are very interactive, and the faculties are always ready to clear our doubts to their best. Also, the flexible class helps us to learn the same course with other faculty and gain more knowledge.
Financing
The admission fee for this program is RM 5,900
Total Program Fee
RM 5,900
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After completing the Masters in AI Engineering program, you can explore opportunities in various industry sectors, primarily in BFSI, IT and Consultancy, Manufacturing, Real Estate, and Food services.
As a certified AI Engineer, you can explore job roles like AI Engineer, Data Scientist, Machine Learning Engineer, or NLP Specialist. The skills you gain from this course will enable you to develop cutting-edge AI solutions and contribute to innovative projects in your organization. You can also get into management positions by polishing your leadership and problem-solving abilities.
Yes, this master’s in artificial intelligence can be worth it for several reasons:
The learning material gives you a deeper understanding of the latest AI theories, techniques and practices.
It helps you gain a competitive edge and build job-ready skills, allowing you to access more opportunities.
It allows you to specialize in AI concepts like machine learning, natural language processing, computer vision, deep learning, and neural networks.
It provides strong credentials to your resume, attesting to your skills in the field.
Taking up the course will allow you to network with like-minded peers and grow further.
Yes, you can pursue an AI course without a formal background in computer science. Simplilearn’s Masters in AI program caters to working professionals from varied industries and backgrounds. The curriculum is designed for beginners and covers foundational concepts from scratch.
However, it’s helpful if you are proficient in mathematics, especially linear algebra and statistics. Rest assured that this course covers all in-demand AI skills, helping you build a career in this field with a computer science degree.
Yes, AI engineering is a promising career for a variety of reasons:
With AI growing, the demand for AI engineers rapidly increases across diverse industries like retail, automotive and ecommerce.
The role involves working on cutting-edge innovations, providing opportunities for continuous learning and impact across various industries.
Its high demand also means a greater sense of competition and competitive salaries.
Being an AI engineer means working with other like-minded individuals in the field and opening up opportunities to build your network.
The difficulty of learning an online master's in artificial intelligence can vary depending on an individual's prior knowledge and experience in the field. Simplilearn’s master's program in AI is designed to be accessible to beginners and suitable for individuals with a basic understanding of the field. However, it is recommended that students possess a solid background in mathematics, programming, and computer science for an enhanced learning experience.
An undergraduate degree is necessary to register for this master's in AI program. A foundation in engineering, computer science, mathematics, and others can be beneficial. Moreover, possessing basic technical skills is sufficient to begin with.
The Artificial Intelligence Engineer Program, offered in collaboration with IBM, is designed by industry experts to help initiate your journey as an AI engineer and accelerate your career growth. Enrolling in this course with Simplilearn will give you a range of benefits including:
Industry-recognized certificates from Simplilearn and IBM for respective modules
Dedicated live sessions by faculty of industry experts
Masterclasses and hackathons conducted by IBM experts
Integrated labs for hands-on learning experience
Lifetime access to self-paced learning content
Access to the latest skills and techniques, including data science with Python, machine learning, deep learning, NLP, ChatGPT and more
Simplilearn's JobAssist helps you get noticed by top hiring companies
If you need help accessing the course modules, you can contact us using the form on the right of any page on the Simplilearn website via the live chat feature or by requesting a callback.
To join this master's in AI program:
Candidates must complete the application form after clicking the enroll now option.
Payment can be made securely online using Visa credit or debit card, MasterCard, American Express, Diner’s Club, or PayPal.
Once payment is processed, candidates will receive a receipt, and access details will be emailed.
Instructors for this master’s in AI program are industry professionals with extensive experience in the field. They are selected based on their expertise, teaching ability, track record and certifications in AI and ML. The selection process includes rigorous vetting to ensure they can provide high-quality education and real-world insights for the best learning experience.
An AI engineer's salary varies depending on many factors, such as skill set, years of experience, industry sector and location. On average, AI Engineers in the United States make roughly USD 137,000 per year (source: Payscale). In India, AI Engineers make roughly INR 12 Lakhs annually (source: AmbitionBox).
The master’s in artificial intelligence is a rigorous training program designed to help you learn about AI from scratch and develop work-ready AI skills. The program focuses on the study and development of AI technologies and applications. It provides in-depth knowledge and skills in various aspects to prepare you for roles in research, development, and implementation. Some key areas of study include:
Essentials of generative AI
Programming essentials
Natural Language Processing (NLP)
Deep learning specializations
Reinforcement learning
If you feel unsatisfied, you can cancel your ongoing enrollment. After deducting an administration charge, we will reimburse the course money. Please see our Refund Policy for more information.
No, coding knowledge isn't mandatory for this master's in artificial intelligence course. However, it's recommended as it helps to grasp the concepts faster.
An AI engineer designs, develops, and implements artificial intelligence solutions to solve complex problems. They are known to work with machine learning models, neural networks, and data analytics to create intelligent systems that can learn and adapt. They often also collaborate with other data scientists and software developers to integrate AI technologies into applications.
The master’s in artificial intelligence is a rigorous training program designed to help you learn about AI from scratch and develop work-ready AI skills. The program focuses on the study and development of AI technologies and applications. It provides in-depth knowledge and skills in various aspects to prepare you for roles in research, development, and implementation. Some key areas of study include:
Essentials of generative AI
Programming essentials
Natural Language Processing (NLP)
Deep learning specializations
Reinforcement learning
The Masters in Artificial Intelligence offered by Simplilearn, in conjunction with IBM, is designed by industry experts to help you accelerate the growth of your career. It includes industry-relevant courses, such as Data Science with Python, Machine Learning, Deep Learning, NLP, and Chat GPT. It also features hackathons and AMA sessions hosted by IBM, Capstone projects, practical labs, live sessions, and hands-on projects.
Here are some reasons why it is the best masters in artificial intelligence and machine learning:
Flexibility: The online Master's in AI at Simplilearn offers the flexibility to learn at your own pace and schedule.
Industry-Relevant Curriculum: Simplilearn's online Master's in AI program provides a comprehensive curriculum that covers essential topics such as machine learning, deep learning, natural language processing, and data science.
Practical Hands-on Experience: The program emphasizes practical learning through real-world projects and case studies. This will help you gain valuable experience applying AI techniques to solve actual industry challenges, preparing you for the demands of the job market.
Career Advancement Opportunities: Completing an online Master's in AI from Simplilearn can enhance your career prospects in the rapidly growing field of artificial intelligence, as it provides networking opportunities with industry professionals and access to job assistance services.
To master artificial intelligence, professionals must have a varied skillset that usually comprises technical and analytical skills. Possessing a combination of these will make you a valued asset in your organization.
Technical skillset:
Mastering programming languages like Python and R.
Possessing knowledge in mathematics like statistics and algebra.
Having a grasp of core concepts like NLP, machine learning, data science and computer vision.
Analytical skillset:
Being able to analyze large sets of data to make further assessments.
Using critical thinking to evaluate and improve existing algorithms
Coming up with multiple approaches to solving problems innovatively.
The difficulty of learning an online master's in artificial intelligence can vary depending on an individual's prior knowledge and experience in the field. Simplilearn offers an online master's program in AI designed to be accessible to beginners and suitable for individuals with a basic understanding of the field. However, it is recommended that students possess a solid background in mathematics, programming, and computer science for an enhanced learning experience.
To register for AI Engineer Course, having a graduate degree is necessary. It is advantageous to have a foundation in engineering, computer science, mathematics, and others. Moreover, possessing basic technical skills is sufficient to begin with.
We offer 24/7 support through chat for urgent issues. For other queries,we have a dedicated team that offers email assistance and callbacks on request.
No, missing a live class will not affect your ability to complete the course. With our 'flexi-learn' feature, you can watch the recorded session of any missed class at your convenience. This allows you to stay up-to-date with the course content and meet the necessary requirements to progress and earn your certificate. Simply visit the Simplilearn learning platform, select the missed class, and watch the recording to have your attendance marked.
Simplilearn’s Artitificial Intelligence Engineer program is best suited for newcomers and experienced professionals. To be eligible to enroll in this course, applicants must have a bachelor's degree in any relevant field. A basic understanding of artificial intelligence is preferred but is not mandatory. Prior working experience is not compulsory.
Simplilearn’s Artificial Intelligence Engineer is well-known for its industry-relevant curriculum and high-value collaborations. Known for its comprehensive curriculum and experienced instructors, Simplilearn’s Artificial Intelligence Engineer has garnered positive acclaim for its ability to provide upskilling and career advancement opportunities. The flexible learning options and supportive community have made it a popular choice for individuals seeking to upskill or acquire new skills in Artificial Intelligence Engineer. Additionally, numerous Simplilearn reviews highlight the effectiveness of this Artificial Intelligence Engineer in meeting learners' expectations and industry standards.
Yes, Simplilearn’s Artificial Intelligence Engineer, offered in collaboration with IBM, is eligible for employer reimbursement. We'd recommend confirming the specific terms of educational benefits or tuition assistance programs with your HR department or employer. IBM also accepts tuition vouchers, which can streamline reimbursement.
To claim your reimbursement, Simplilearn offers completion certificates, detailed receipts, and course breakdowns, which can be submitted to your employer or HR department.