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The Foundations module comprises two courses where we get our hands dirty with Python programming language for Artificial Intelligence and Machine Learning and Statistical Learning, head-on. These two courses set our foundations for Artificial Intelligence and Machine Learning online course so that we sail through the rest of the journey with minimal hindrance. Welcome to the program.
- Python Programming Fundamentals
- Python for Data Science - NumPy and Pandas
- Data Visualization using Python
- Exploratory Data Analysis
- Data Pre-processing
Python is an essential programming language in the tool-kit of an AI & ML professional. In this course, you will learn the essentials of Python and its packages for data analysis and computing, including NumPy, SciPy, Pandas, Seaborn and Matplotlib.
Python is a widely used high-level, interpreted programming language, having a simple, easy-to-learn syntax that highlights code readability.
This module will teach you how to work with Python syntax to executing your first code using essential Python fundamentals
NumPy is a Python package for scientific computing like working with arrays, such as multidimensional array objects, derived objects (like masked arrays and matrices), etc. Pandas is a fast, powerful, flexible, and simple-to-use open-source library in Python to analyse and manipulate data.
This module will give you a deep understanding of exploring data sets using Pandas and NumPy.
Data visualization is an important skill and one can create compelling visual representations of data to enable effective analysis and communication of insights. Python provides libraries to do this in a simple and effective manner.
Exploratory Data Analysis, or EDA, is essentially a type of storytelling for statisticians. It allows us to uncover patterns and insights, often with visual methods, within data.
This module will give you a deep insight into EDA in Python and visualization tools-Matplotlib and Seaborn.
Data preprocessing is a crucial step in any machine learning project and involves cleaning, transforming, and organizing raw data to improve its quality and usability. The preprocessed data is used both analysis and modeling.
- Descriptive Statistics
The study of data analysis by describing and summarising numerous data sets is called Descriptive Analysis. It can either be a sample of a region’s population or the marks achieved by 50 students.
This module will help you understand Descriptive Statistics in Python for AI ML.
- Inferential Statistics
Inferential Statistics helps you how to use data for estimation and assess theories. You will know how to work with Inferential Statistics using Python.
- Probability & Conditional Probability
Probability is a mathematical tool used to study randomness, like the possibility of an event occurring in a random experiment. Conditional Probability is the likelihood of an event occurring provided that several other events have also occurred.
In this module, you will learn about Probability and Conditional Probability in Python for AI ML.
- Hypothesis Testing
Hypothesis Testing is a necessary Statistical Learning procedure for doing experiments based on the observed/surveyed data.
You will learn Hypothesis Testing used for AI and ML in this module.
- Chi-square & ANOVA
Chi-Square is a Hypothesis testing method used in Statistics, where you can measure how a model compares to actual observed/surveyed data.
Analysis of Variance, also known as ANOVA, is a statistical technique used in AI and ML. You can split observed variance data into numerous components for additional analysis and tests using ANOVA.
This module will teach you how to identify the significant differences between the means of two or more groups.
Statistical Learning is a branch of applied statistics that deals with Machine Learning, emphasizing statistical models and assessment of uncertainty. This course on statistics will work as a foundation for Artificial Intelligence and Machine Learning concepts learnt in this AI ML PG program.
The next module is the Machine Learning online course, where you will learn Machine Learning techniques and all the algorithms popularly used in Classical ML that fall in each category.
- Linear Regression
- Logistic Regression
- Decision Trees
Supervised Machine Learning aims to build a model that makes predictions based on evidence in the presence of uncertainty. In this course, you will learn about Supervised Learning algorithms of Linear Regression and Logistic Regression.
Linear Regression is one of the most popular supervised ML algorithms used for predictive analysis, resulting in producing the best outcomes. You can use this technique to assume a linear relationship between the independent variable and the dependent variable. You will cover all the concepts of Linear Regression in this module.
Logistic Regression is also one of the most popular supervised ML algorithms, like Linear Regression. It is a simple classification algorithm where you can predict the categorical dependent variables with independent variables’ assistance. You will cover all the concepts of Logistic Regression in this module.
A decision tree is a Supervised ML algorithm, which is used for both classification and regression problems. It is a hierarchical structure where internal nodes indicate the dataset features, branches represent the decision rules, and each leaf node indicates the result.
- Bagging and Random Forests
- Cross Validation
- Class Imbalance Handling
- Hyperparameter Tuning
Ensemble methods help to improve the predictive performance of Machine Learning models. In this machine learning online course, you will learn about different Ensemble methods that combine several Machine Learning techniques into one predictive model in order to decrease variance, bias or improve predictions.
In this module, you will learn Random Forest, a popular supervised ML algorithm that comprises several decision trees on the provided several subsets of datasets and calculates the average for enhancing the predictive accuracy of the dataset, and Bagging, an essential Ensemble Method.
Boosting is an Ensemble Method which can enhance the stability and accuracy of machine learning algorithms, converting them into robust classification, etc.
Cross-validation is a technique used to evaluate the performance of machine learning models, which helps in getting a clearer picture of an ML model's generalization ability. K-fold cross validation is one of the most common approaches.
Class imbalance handling is a crucial task in machine learning when the distribution of classes in the dataset is uneven. There are different techniques like RandomUnderSampler, SMOTE, etc. used for this purpose.
Hyperparameter tuning is the process of finding the optimal values for hyperparameters in a machine learning algorithm. It involves exploring different combinations of hyperparameter values and evaluating their impact on the model's performance, often using techniques like grid search, random search, etc
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
Unsupervised Learning finds hidden patterns or intrinsic structures in data. In this machine learning online course, you will learn about commonly-used clustering techniques like K-Means Clustering and Hierarchical Clustering along with Dimension Reduction techniques like Principal Component Analysis.
K-means clustering is a popular unsupervised ML algorithm, which is used for resolving the clustering problems in Machine Learning. In this module, you will learn how the algorithm works and later implement it. This module will teach you the working of the algorithm and its implementation.
Hierarchical Clustering is another popular unsupervised ML technique or algorithm, which is used for building a hierarchy or tree-like structure of clusters. For example, you can combine a list of unlabeled datasets into a cluster in the hierarchical structure.
PCA is a dimensionality reduction technique used to transform a high-dimensional dataset into a lower-dimensional space. This can help in choosing and retaining only the variable which capture the highest amount of variablity in the data as they will be the most important ones.
- Packaging Models
Model Packaging helps you package all the necessary assets to host a model as a web service. It also enables you to download either a fully built Docker image or the files required to make one.
- Rest APIs, Dockers
RESTful API, also known as Representational State Transfer, is an API that uses HTTP requests like GET, PUT, POST, and DELETE to communicate with web services.
Docker is one of the most popular tools, which is used to create, deploy, and run applications with the help of containers.
This module will teach how to package up an application using containers.
- ML Pipeline and Model Scalability
In this module, you will learn everything you need to know about ML Pipeline and Model Scalability used for ML models.
This last module of the machine learning online course will discuss the model deployment techniques and techniques around making your model scalable, robust, and reproducible.
The AI and Deep Learning course will take us beyond the traditional ML into the realm of Neural Networks. From the regular tabular data, we move on to training our models with unstructured data like Text and Images.
- Deep Learning and its history
- Multi-layer Perceptron
- Activation functions
- Optimizers and its types
- Weight Initialization and Regularization
In this artificial intelligence online course, you will learn about the basic building blocks of Artificial Neural Networks. In this deep learning online course, you’ll learn how Deep Learning Networks can be successfully applied to data for knowledge discovery, knowledge application, and knowledge-based prediction.
Deep Learning carries out the Machine Learning process using an ‘Artificial Neural Net’, which is composed of several levels arranged in a hierarchy. It has a rich history that can be traced back to the 1940s, but significant advancements occurred in the 2000s with the introduction of deep neural networks and the availability of large datasets and computational power.
The multilayer perceptron (MLP) is a type of artificial neural network with multiple layers of interconnected neurons, including an input layer, one or more hidden layers, and an output layer. It is a versatile architecture capable of learning complex patterns from data.
Activation Function is used for defining the output of a neural network from numerous inputs.
Backpropagation is a key algorithm used in training artificial neural networks, enabling the calculation of gradients and the adjustment of weights and biases to iteratively improve the performance of a neural network.
Optimizers are algorithms used to adjust the parameters of a neural network model during training to minimize the loss function. Different types of optimizers are Gradient Descent, RMSProp, Adam, etc.
Weight initialization is the process of setting initial values for the weights of a neural network, which can significantly impact the model's training and convergence. Regularization is a technique used in machine learning/ neural networks to prevent the model from overfitting, which helps improve the model's generalization ability.
- Overview of Computer Vision (CV)
- Understanding images (Color Pixel Theory and Image Representation)
- Convolution Operation and Convolutional Neural Networks (CNNs)
- CNN Architectures
- Transfer Learning
The next module is the Computer Vision course. This module will reflect on the ability of a computer system to see and make sense of visuals using CNN (Convolutional Neural Network). It will enable you to efficiently handle image data for the purpose of feeding into CNNs.
In this module, you will drive through all the business applications of computer vision and learn how it impacted several business industries.
This module will teach you how to process the image and extract all the data from it, where you can use the data for image recognition in deep learning.
Convolutional Neural Networks (CNN) are used for image processing, classification, segmentation, and numerous other applications. This module will give you a deep understanding of CNNs from scratch.
CNN architectures are specialized deep learning models designed for processing grid-like data, such as images or audio. Common architectures include VGG16, ResNet, InceptionNet, etc.
Transfer learning is a technique in neural networks where a pre-trained model, usually trained on a large dataset, is used as a starting point for solving a related task. It allows leveraging the knowledge and learned features from the pre-trained model, reducing the need for extensive training on limited data, and often leads to improved performance and faster convergence.
- Overview of Natural Language Processing (NLP)
- Text Preprocessing
- Word Representation and Vectorization Techniques
- Recurrent Neural Networks (RNNs)
- Long Short Term Memory Networks (LSTMs)
- Sentimental Analysis
This module of the NLP online course talks about yet another interesting implementation of Neural Networks that revolves around equipping computers to understand human language. You will learn to understand sentiments from the texts.
This module will get you comfortable with the introduction to NLP and, later, teach you all the essential business applications you need to know about NLP. Natural Language Processing (NLP) applies computational linguistics to build real-world applications, which work with languages consisting of varying structures. First, we teach the computer to learn languages and then expect it to understand them with relevant, efficient algorithms.
Text preprocessing is a technique for cleaning and preparing text data. In this module, you learn how to work with text data using all the steps involved in preprocessing a text, such as Text Cleansing, Tokenization, Stemming, etc.
Word representation and vectorization techniques aim to convert words or text into numerical representations that can be processed by machine learning models. Methods like one-hot encoding and word embeddings (such as Word2Vec or GloVe) provide effective ways to capture semantic and contextual information about words.
Recurrent Neural Networks (RNNs) are a type of neural network architecture specifically designed to process sequential data, such as time series or natural language. They introduce feedback connections and excel in capturing temporal dependencies in data.
Long Short-Term Memory (LSTM) networks are a specialized type of recurrent neural network (RNN) designed to overcome the limitations of standard RNNs. They utilize memory cells and a gating mechanism which helps in capturing long-term dependencies.
Sentiment Analysis is an NLP technique to check whether the data is positive, negative, or neutral. Twitter is the most commonly used example of Sentiment Analysis.
- Overview of ChatGPT and OpenAI
- Timeline of NLP and Generative AI
- Frameworks for understanding ChatGPT and Generative AI
- Implications for work, business, and education
- Output modalities and limitations
- Business roles to leverage ChatGPT
- Prompt engineering for fine-tuning outputs
- Practical demonstration and bonus section on RLHF
Gain an understanding of what ChatGPT is and how it works, as well as delve into the implications of ChatGPT for work, business, and education. Additionally, learn about prompt engineering and how it can be used to fine-tune outputs for specific use cases.
- Mathematical Fundamentals for Generative AI
- VAEs: First Generative Neural Networks
- GANs: Photorealistic Image Generation
- Conditional GANs and Stable Diffusion: Control & Improvement in Image Generation
- Transformer Models: Generative AI for Natural Language
- ChatGPT: Conversational Generative AI
- Hands-on ChatGPT Prototype Creation
- Next Steps for Further Learning and understanding
Dive into the development stack of ChatGPT by learning the mathematical fundamentals that underlie generative AI. Further, learn about transformer models and how they are used in generative AI for natural language.
- Popularity-based Model
A popularity-based model is a recommendation system, which operates based on popularity or any currently trending models.
- Market Basket Analysis
Market Basket Analysis, also called Affinity Analysis, is a modeling technique based on the theory that if you purchase a specific group of items, then you are more probable to buy another group of items.
- Content-based Model
First, we accumulate the data explicitly or implicitly from the user. Next, we create a user profile dependent on this data, which is later used for user suggestions. The user gives us more information or takes more recommendation-based actions, which subsequently enhances the accuracy of the system. This technique is called a Content-based Recommendation System.
- Collaborative Filtering
Collaborative Filtering is a collective usage of algorithms where there are numerous strategies for identifying similar users or items to suggest the best recommendations.
- Hybrid Recommendation Systems
A Hybrid Recommendation system is a combination of numerous classification models and clustering techniques. This module will lecture you on how to work with a Hybrid Recommendation system.
The last module in this Artificial Intelligence and Machine Learning online course is Recommendation Systems. A large number of companies use recommender systems, which are software that select products to recommend to individual customers. In this course, you will learn how to produce successful recommender systems that use past product purchase and satisfaction data to make high-quality personalized recommendations.
Earn a Postgraduate Certificate in the top-rated Artificial Intelligence and Machine Learning online course from the University of Texas, Austin. The course’s comprehensive Curriculum will foster you into a highly-skilled professional in Artificial Intelligence and Machine Learning. It will help you land a job at the world’s leading corporation and power ahead your career transition.
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Frequently Asked Questions
No, PGP-AIML is an online professional certificate program offered by the McCombs School of Business in collaboration with Great Learning. Since it is not a degree/full-time program offered by the university, therefore, there are no grade sheets or transcripts for this program by the university. You will receive marks on each assessment to test your understanding and marks on each module to determine your eligibility for the certificate.
Upon successful completion of the program, i.e. after completing all the modules as per the eligibility of the certificate, you are issued a certificate from the McCombs School of Business at the University of Texas at Austin.
Each week involves around 2-3 hours of recorded lectures and an additional 2-hour mentored learning session each weekend, which includes hands-on practical applications and problem-solving. The program also involves around an hour of practice exercises or assessments each week. Additionally, based on your background, you should expect to invest 2 to 4 hours every week in self-study and practice. So, that amounts to a time commitment of 8-10 hours per week.
Artificial Intelligence is the technology used to build intelligent machines that act as humans do. The AI enabled systems to mimic human behavior and perform tasks as we do. This intelligence is built using complex algorithms and mathematical functions.
Artificial Intelligence is the technology that is being applied in almost every industry and business. AI is literally everywhere. We are witnessing the presence of Artificial Intelligence every single day of our lives. Artificial Intelligence is applied in smartphones, smart window treatments, banking, self-driving cars, healthcare, social media, video games, surveillance, and many other aspects of our daily life.
Machine Learning is an important subset of Artificial Intelligence. Machine learning is one of the most interesting careers that you could choose. Machine learning is perceived as one of the fastest-growing technologies.
Machine learning is a subset of artificial intelligence that provides systems the ability to automatically learn and progress from experience without being specifically instructed. By employing Machine Learning techniques, businesses can automate routine tasks and maximize profits. Hence, pursuing a PG in achine learning and artificial intelligence would fetch you the best career opportunities.
Artificial Intelligence is one of the most latest trending technologies. Artificial Intelligence is not just about creating robots or building computer systems that can think as humans do. Artificial Intelligence is a technology that understands humans and makes their lives easy. From Apple's Siri to Google's voice assistant, from facebook friend recommendations to Netflix's movie recommendations, Artificial Intelligence is playing the most pivotal role in making our lives easy. AI in simple words can be defined as an interface to us and the computer devices, it is the technology that makes the systems understand humans so well. The technology of AI is just growing at a rapid pace and the number of industries and businesses adapting this technology is reaching the skies. There is a huge demand for AI professionals across the globe. Hence, taking up the best Artificial Intelligence course and pursuing a career in this domain stands as the best choice you could make for yourself.
AI has been around for years. In a world full of data, Artificial Intelligence is already making our lives easier in a lot of days today.
Experts predict that the world would be changed forever by the revolution of AI by the year 2050.
But let us understand what AI does. Artificial Intelligence is nothing but software that automatically gathers and analyses data to derive meaningful insights from that. The Facebook newsfeed would automatically adjust according to our interests. The AI algorithms intake flood of data, filter them out, and display them for us at the top of the page.
Artificial Intelligence does mimic the human brain. In other words, AI enables machines to think and act as humans do. Artificial Intelligence provides the machines the ability to adapt, reason, and provide solutions. AI in simple terms can be explained as the technology to make the machines artificially intelligent. This requires huge data and immense computer capacity to process the applications. Even as more powerful technologies such as Blockchain, the Internet of Things, and more are being developed, AI is constantly causing a revolution in every domain.This is why the demand for artificial Intelligence is growing day by day. Therefore, many are seeking for ai course to encounter a career transition into the job roles of AI.
The pay scale offered in the domain of Artificial Intelligence is one of the major factors that is motivating many to pursue a career in this domain. The job roles offered in this domain are considered to be one of the highest-paid across the globe. In the United States, the pay scale of Artificial Intelligence and Machine Learning professionals ranges from $90k to $305k per annum. The average pay scale is expected to be $164,769 per annum. While in India it ranges from 6 to 35 lakh per annum and the average pay scale is estimated as 21,86,857 per annum. Hence, the demand for Artificial Intelligence and Machine Learning courses is at its peak across the world.
If you are interested in taking up an Artificial Intelligence training, choose to learn the prerequisites and fundamentals of artificial intelligence.
Here are a few obvious prerequisites that assist you in mastering the techniques of Artificial Intelligence and Machine Learning thoroughly.
1. Programming languages
As AI and ML are all about training computer systems, it is essential to learn programming languages. Programming languages are the basic source to interact with the machines. Java, C++, R, Python, etc are a few languages that are extensively used in machine learning. Python specifically is the most popular programming language used by most AI and Machine Learning professionals.
2. Mathematics and Statistics
You need not be an expert in mathematics to learn Artificial Intelligence and Machine Learning technologies. Nevertheless, you shouldn't be a novice to several mathematical concepts that are applied in Artificial Intelligence and Machine Learning. A fundamental understanding of calculus, probability, statistics is crucial to understand the concepts of AIML.
3.Data Visualisation Tools
Data Visualisation is one of the primary job roles of AI professionals performed on a daily basis. Hence, being good at several data visualization tools such as Tableau, Microsoft Power BI and more would assist you to learn the techniques of AI at a faster pace.
Besides the above-mentioned prerequisites, having a concise understanding of the fundamentals of Artificial Intelligence would also promote a great understanding of the several concepts of Artificial Intelligence.
The Artificial Intelligence Courses designed by Great Learning are suitable for someone who is:
- As computer science with artificial intelligence is an exciting combination, a developer who wants to become a Machine Learning Engineer or Artificial Intelligence Scientist would take up an AI learning course.
- Analytics Managers that drive a team composed of Analysts could learn AI.
- Analytics professionals that desire to work in AI or Machine Learning
- Fresh graduates who want to secure a career in Machine Learning or AI could take up the pg in artificial intelligence courses.
- Managers or Business owners who desire to become AI-enabled professionals can opt for the AI for leaders course.
- Experienced working professionals that want to employ AI in their existing work field.
The technology of Artificial Intelligence has a lot more to contribute to any industry than individuals do. Hence many businesses are applying advanced artificial intelligence to draw the best outcomes.
Let us understand a few of the benefits.
- Building better business strategy: By employing Artificial Intelligence, organizations can develop the best business plan. Artificial Intelligence renders solutions to come up with the best business plan that supports companies' flourish. Today, most of the top-notch companies are applying Artificial Intelligence in project and operation management to obtain better outcomes.
- Better Research and Inventions: Organizations must be conscious of the latest trends in their market. An AI-enabled business team would shape their business in the best way that suits the requirements of end customers. An AI-enabled organization would learn current technological trends, plan a business strategy that delivers the best services. Businesses with a good vision and well versed with AI can compose a groundbreaking solution. AI assists businesses to add value to their products by adapting themselves to the latest trends in the market, technology.
- Cost Reduction: Cost reduction is one of the major benefits that AI contributes to any business. Small and medium scale certainly strive for their endurance considering their limited budget and resources. With a substantial demand for AI professionals, these companies may not be able to afford such resources to meet their needs. Hence, businesses need to adopt AI so that they can reduce costs to the company. AI in business draws more customers that explore solutions for their problems. Therefore, taking up an AI certification course would fetch you with the best career opportunities in several industries in the market.
Many believe that Artificial Intelligence and Machine Learning are limited to the IT industry. AI is being applied everywhere in every industry across the world.
Let us understand how AI is being employed in several industries today.
- Customer Support: The domain of AI is observed to replace many customer support job roles. Today, most websites are using chatbots to assist customers. The AI-enabled chatbot systems are capable of addressing customer's problems and provide the user with the most meaningful product recommendations at a faster pace.
- E-commerce: With the employment of an AI recommendation system, E-commerce websites are offering personalized shopping experiences to their users. The systems study the user's past purchase records and recommend the most suitable products. The system learns the customer's choice and presents the most meaningful recommendations. This makes the user experience a personalized shopping experience. In this way, AI is benefitting the E-commerce industry by enhancing the customer experience. Today, a lot of e commerce giants such as Amazon employ AI to drive their businesses.
Artificial Intelligence in Social Media
Social Media has become an indispensable part of our daily lives. We spend most of our time on Social media platforms such as Facebook, Twitter, Instagram, and more. There is a huge amount of data being generated through social media websites in the form of messages, tweets, posts, and more. In social media platforms like Facebook, Artificial Intelligence is used for face recognition while Machine Learning and Deep Learning concepts are used to recognize the facial features of people and automatically suggest you tag them. Twitter's AI is being used to identify hate speech and terroristic language in tweets by employing Natural Language Processing.
Hence, check out the best courses in Artificial Intelligence, learn AI today, and get into the most in-demand job roles of the 21st century.
Please note that submitting the admission fee does constitute enrolling in the program and the below cancellation penalties will be applied:
1) Full refund can only be issued within 48 hours of enrollment
2) Admission Fee - If cancellation is requested after 48 hours of enrollment, the admission fee will not be refunded.
3) Fee paid in excess of the admission fee:
1. Refund or dropout requests requested more than 4 weeks before the Commencement Date are eligible for a full refund of the amount paid in excess of the admission fee
2. Refund or dropout requests requested more than 2 weeks before the Commencement Date are eligible for a 75% refund of the amount paid in excess of the admission fee
3. Refund or dropout requests requested more than 24 hours before the Commencement Date are eligible for a 50% refund of the amount paid in excess of the admission fee
4. Requests received after the Commencement Date are not eligible for a refund.
Cancellation must be requested in writing to the program office.
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