Build a successful career in Artificial Intelligence and Machine Learning

The curriculum of the PGP in Artificial Intelligence & Machine Learning is created in consultation with industry experts, academicians & program alums to ensure you learn the most cutting-edge topics.

Foundations

The Foundations module comprises of two courses where we get our hands dirty with Statistics and Code, head-on. These two courses set our foundations so that we sail through the rest of the journey with minimal hindrance.

Python for AI & ML

3 Quizzes

1 Project

This course will let us get comfortable with the Python programming language used for Artificial Intelligence and Machine Learning. We start with a high-level idea of Object-Oriented Programming and later learn the essential vocabulary(/keywords), grammar(/syntax) and sentence formation(/usable code) of this language. This course will drive you from introducing AI and ML to the core concepts using one of the most popular and advanced programming languages, Python.

• Python Basics

Python is a widely used high-level programming language and has a simple, easy-to-learn syntax that highlights readability. This module will help you drive through all the fundamentals of programming in Python, and at the end, you will execute your first Python program.

• Jupyter notebook – Installation & function

You will learn to implement Python for AI and ML using Jupyter Notebook. This open-source web application allows us to create and share documents containing live code, equations, visualisations, and narrative text.

• Python functions, packages and routines

Functions and Packages are used for code reusability and program modularity, respectively. This module will help you understand and implement Functions and Packages in Python for AI.

• Pandas, NumPy, Matplotlib, Seaborn

This module will give you a deep understanding of exploring data sets using Pandas, NumPy, Matplotlib, and Seaborn. These are the most widely used Python libraries.

• Working with data structures,arrays, vectors & data frames

Data Structures are one of the most significant concepts in any programming language. They help in the arrangement of leader-board games by ranking each player. They also help in speech and image processing for AI and ML. In this module, you will learn Data Structures like Arrays, Lists, Tuples, etc. and learn to implement Vectors and Data Frames in Python.

Applied Statistics

3 Quizzes

1 Project

Here we learn the terms and concepts vital to Exploratory Data Analysis and Machine Learning in general. From the very basics of taking a simple average to the advanced process of finding statistical evidence to confirm or deny conjectures and speculations, we will learn a specific set of tools required to analyze and draw actionable insights from data.

• Descriptive Statistics

The study of data analysis by describing and summarising several data sets is known as 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 Machine Learning.

• Inferential Statistics

This module will let you explore fundamental concepts of using data for estimation and assessing theories using Python.

• Probability & Conditional Probability

Probability is a mathematical tool used to study randomness like the possibility of an event occurrence in a random experiment. Conditional Probability is the possibility of an event occurring given that several other events have also occurred. In this module, you will learn about Probability and Conditional Probability in Python for Machine Learning.

• Probability Distributions - Types of distribution – Binomial, Poisson & Normal distribution

A statistical function reporting all the probable values that a random variable takes within a specific range is known as a Probability Distribution. This module will teach you about Probability Distributions and various types like Binomial, Poisson, and Normal Distribution in Python.

• Hypothesis Testing

This module will teach you about Hypothesis Testing in Machine Learning using Python. Hypothesis Testing is a necessary procedure in Applied Statistics for doing experiments based on the observed/surveyed data.

Machine Learning

The next module is the Machine Learning online course that will teach us all the Machine Learning techniques from scratch, and the popularly used Classical ML algorithms that fall in each of the categories.

Supervised Learning

4 Quizzes

1 Project

In this course we learn about Supervised ML algorithms, working of the algorithms and their scope of application - Regression and Classification.

• Multiple Variable Linear regression

Linear Regression is one of the most popular ML algorithms used for predictive analysis in Machine Learning, resulting in producing the best outcomes. It is a technique assuming a linear relationship between the independent variable and dependent variable.

• Multiple regression

Multivariate Regression is a supervised machine learning algorithm involving multiple data variables for analysis. It is used for predicting one dependent variable using various independent variables. This module will drive you through all the concepts of Multiple Regression used in Machine Learning.

• Logistic regression

Logistic Regression is one of the most popular ML algorithms, like Linear Regression. It is a simple classification algorithm to predict the categorical dependent variables with the assistance of independent variables. This module will drive you through all the concepts of Logistic Regression used in Machine Learning.

• K-NN classification

k-NN Classification or k-Nearest Neighbours Classification is one of the most straightforward machine learning algorithms for solving regression and classification problems. You will learn about the usage of this algorithm through this module.

• Naive Bayes classifiers

Naive Bayes Algorithm is used to solve classification problems using Baye’s Theorem. This module will teach you about the theorem and solving the problems using it.

• Support vector machines

Support Vector Machine or SVM is also a popular ML algorithm used for regression and classification problems/challenges. You will learn how to implement this algorithm through this module.

Unsupervised Learning

2 Quizzes

1 Project

We learn what Unsupervised Learning algorithms are, working of the algorithms and their scope of application - Clustering and Dimensionality Reduction.

• K-means clustering

K-means clustering is a popular unsupervised learning algorithm to resolve the clustering problems in Machine Learning or Data Science. In this module, you will learn how the algorithm works and later implement it.

• Hierarchical clustering

Hierarchical Clustering is an ML technique or algorithm to build a hierarchy or tree-like structure of clusters. For example, it is used to combine a list of unlabeled datasets into a cluster in the hierarchical structure. This module will teach you the working and implementation of this algorithm.

• High-dimensional clustering

High-dimensional Clustering is the clustering of datasets by gathering thousands of dimensions.

• Dimension Reduction-PCA

Principal Component Analysis for Dimensional Reduction is a technique to reduce the complexity of a model like eliminating the number of input variables for a predictive model to avoid overfitting. Dimension Reduction-PCA is a well-known technique in Python for ML, and you will learn everything about this method in this module.

Ensemble Techniques

2 Quizzes

1 Project

In this Machine Learning online course, we discuss supervised standalone models’ shortcomings and learn a few techniques, such as Ensemble techniques to overcome these shortcomings.

• Decision Trees

Decision Tree is a Supervised Machine Learning algorithm 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.

• Random Forests

Random Forest is a popular supervised learning algorithm in machine learning. As the name indicates, it comprises several decision trees on the provided dataset’s several subsets. Then, it calculates the average for enhancing the dataset’s predictive accuracy.

• Bagging

Bagging, also known as Bootstrap Aggregation, is a meta-algorithm in machine learning used for enhancing the stability and accuracy of machine learning algorithms, which are used in statistical classification and regression.

• Boosting

As the name suggests, Boosting is a meta-algorithm in machine learning that converts robust classifiers from several weak classifiers. Boosting can be further classified as Gradient boosting and ADA boosting or Adaptive boosting.

Featurization, Model Selection & Tuning

2 Quizzes

1 Project

Learn various concepts that will be useful in creating functional machine learning models like model selection and tuning, model performance measures, ways of regularisation, etc.

• Feature engineering

Feature engineering is transforming data from the raw state to a state where it becomes suitable for modelling. It converts the data columns into features that are better at representing a given situation in terms of clarity. Quality of the component in distinctly representing an entity impacts the model’s quality in predicting its behaviour. In this module, you will learn several steps involved in Feature Engineering.

• Model selection and tuning

This module will teach you which model best suits architecture by evaluating every individual model based on the requirements.

• Model performance measures

In this module, you will learn how to optimise your machine learning model’s performance using model evaluation metrics.

• Regularising Linear models

In this module, you will learn the technique to avoid overfitting and increase model interpretability.

• ML pipeline

This module will teach you how to automate machine learning workflows using the ML Pipeline. You can operate the ML Pipeline by enabling a series of data to be altered and linked together in a model, which can be tested and evaluated to achieve either a positive or negative result.

• Bootstrap sampling

Bootstrap Sampling is a machine learning technique to estimate statistics on population by examining a dataset with replacement.

• Grid search CV

Grid search CV is the process of performing hyperparameter tuning to determine the optimal values for any machine learning model. The performance of a model significantly depends on the importance of hyperparameters. Doing this process manually is a tedious task. Hence, we use GridSearchCV to automate the tuning of hyperparameters.

• Randomized search CV

Randomized search CV is used to automate the tuning of hyperparameters similar to Grid search CV. Randomized search CV is provided for a random search, and Grid search CV is provided for a grid search.

• K fold cross-validation

K-fold cross-validation is a way in ML to improve the holdout method. This method guarantees that our model’s score does not depend on how we picked the train and test set. The data set is divided into k number of subsets, and the holdout method is repeated k number of times.

Self-paced Module: Demystifying ChatGPT and Applications

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.

• 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

Introduction to SQL

3 Quizzes

1 Project

Here, we will cover everything you need to know about SQL programming, such as DBMS, Normalization, Joins, etc.

• Introduction to DBMS

Database Management Systems (DBMS) is a software tool where you can store, edit, and organise data in your database. This module will teach you everything you need to know about DBMS.

• ER diagram

An Entity-Relationship (ER) diagram is a blueprint that portrays the relationship among entities and their attributes. This module will teach you how to make an ER diagram using several entities and their attributes.

• Schema design

Schema design is a schema diagram that specifies the name of record type, data type, and other constraints like primary key, foreign key, etc. It is a logical view of the entire database.

• Key constraints and basics of normalization

Key Constraints are used for uniquely identifying an entity within its entity set, in which you have a primary key, foreign key, etc. Normalization is one of the essential concepts in DBMS, which is used for organising data to avoid data redundancy. In this module, you will learn how and where to use all key constraints and normalization basics.

• Joins

As the name implies, a join is an operation that combines or joins data or rows from other tables based on the common fields amongst them. In this module, you will go through the types of joins and learn how to combine data.

• Subqueries involving joins and aggregations

This module will teach you how to work with subqueries/commands that involve joins and aggregations.

• Sorting

As the name suggests, Sorting is a technique to arrange the records in a specific order for a clear understanding of reported data. This module will teach you how to sort data in any hierarchy like ascending or descending, etc.

• Independent subqueries

The inner query that is independent of the outer query is known as an independent subquery. This module will teach you how to work with independent subqueries.

• Correlated subqueries

The inner query that is dependent on the outer query is known as a correlated subquery. This module will teach you how to work with correlated subqueries.

• Analytic functions

A function that determines values in a group of rows and generates a single result for every row is known as an Analytic Function.

• Set operations

The operation that combines two or more queries into a single result is called a Set Operation. In this module, you will implement various set operators like UNION, INTERSECT, etc.

• Grouping and filtering

Grouping is a feature in SQL that arranges the same values into groups using some functions like SUM, AVG, etc. Filtering is a powerful SQL technique, which is used for filtering or specifying a subset of data that matches specific criteria.

Artificial Intelligence

The next module is the Artificial Intelligence online course that will teach us from the introduction to Artificial Intelligence to taking us beyond the traditional ML into Neural Nets’ realm. We move on to training our models with Unstructured Data like Text and Images from the regular tabular data.

Introduction to Neural Networks and Deep Learning

3 Quizzes

1 Project

In this Artificial Intelligence online course, we start with the motive behind using the terms Neural network and look at the individual constituents of a neural network. Installation of and building familiarity with TensorFlow library, appreciate the simplicity of Keras and build a deep neural network model for a classification problem using Keras. We also learn how to tune a Deep Neural Network.

• Gradient Descent

Gradient Descent is an iterative process that finds the minima of a function. It is an optimisation algorithm that finds the parameters or coefficients of a function’s minimum value. However, this function does not always guarantee to find a global minimum and can get stuck at a local minimum. In this module, you will learn everything you need to know about Gradient Descent.

• Introduction to Perceptron & Neural Networks

Perceptron is an artificial neuron, or merely a mathematical model of a biological neuron. A Neural Network is a computing system based on the biological neural network that makes up the human brain. In this module, you will learn all the neural networks’ applications and go much deeper into the perceptron.

• Batch Normalization

Normalisation is a technique to change the values of numeric columns in the dataset to a standard scale, without distorting differences in the ranges of values. In Deep Learning, rather than just performing normalisation once in the beginning, you’re doing it all over the network. This is called batch normalisation. The output from the activation function of a layer is normalised and passed as input to the next layer.

• Activation and Loss functions

Activation Function is used for defining the output of a neural network from several inputs. Loss Function is a technique for prediction error of neural networks.

• Hyper parameter tuning

This module will drive you through all the concepts involved in hyperparameter tuning, an automated model enhancer provided by AI training.

• Deep Neural Networks

An Artificial Neural Network (ANN) having several layers between the input and output layers is known as a Deep Neural Network (DNN). You will learn everything about deep neural networks in this module.

• Tensor Flow & Keras for Neural Networks & Deep Learning

TensorFlow is created by Google, which is an open-source library for numerical computation and wide-ranging machine learning. Keras is a powerful, open-source API designed to develop and evaluate deep learning models. This module will teach you how to implement TensorFlow and Keras from scratch. These libraries are widely used in Python for AIML.

Computer Vision

3 Quizzes

2 Projects

In this Computer Vision course, we will learn how to process and work with images for Image classification using Neural Networks. Going beyond plain Neural Networks, we will also learn a more advanced architecture - Convolutional Neural Networks.

• Introduction to Image data

This module will teach you how to process the image and extract all the data from it, which can be used for image recognition in deep learning.

• Introduction to Convolutional Neural Networks

Convolutional Neural Networks (CNN) are used for image processing, classification, segmentation, and many more applications. This module will help you learn everything about CNN.

• Famous CNN architectures

In this module, you will learn everything you need to know about several CNN architectures like AlexNet, GoogLeNet, VGGNet, etc.

• Transfer Learning

Transfer learning is a research problem in deep learning that focuses on storing knowledge gained while training one model and applying it to another model.

• Object detection

Object detection is a computer vision technique in which a software system can detect, locate, and trace objects from a given image or video. Face detection is one of the examples of object detection. You will learn how to detect any object using deep learning algorithms in this module.

• Semantic segmentation

The goal of semantic segmentation (also known as dense prediction) in computer vision is to label each pixel of the input image with the respective class representing a specific object/body.

• Instance Segmentation

Object Instance Segmentation takes semantic segmentation one step ahead in a sense that it aims towards distinguishing multiple objects from a single class. It is considered as a Hybrid of Object Detection and Semantic Segmentation tasks.

• Other variants of convolution

This module will drive you several other essential variants in Convolutional Neural Networks (CNN).

• Metric Learning

Metric Learning is a task of learning distance metrics from supervised data in a machine learning manner. It focuses on computer vision and pattern recognition.

• Siamese Networks

A Siamese neural network (sometimes called a twin neural network) is an artificial neural network that contains two or more identical subnetworks which means they have the same configuration with the same parameters and weights. This module will help you find the similarity of the inputs by comparing the feature vectors of subnetworks.

• Triplet Loss

In learning a projection where the inputs can be distinguished, the triplet loss is similar to metric learning. The triplet loss is used for understanding the score vectors for the images. You can use the score vectors of face descriptors for verifying the faces in Euclidean Space.

Natural Language Processing

4 Quizzes

2 Projects

Learn how to work with natural language processing with Python using traditional machine learning methods. Then, deep dive into the realm of Sequential Models and state of the art language models.

• Introduction to NLP

Natural language processing applies computational linguistics to build real-world applications that work with languages comprising varying structures. We try to teach the computer to learn languages, and then expect it to understand it, with suitable, efficient algorithms. This module will drive you through the introduction to NLP and all the essential concepts you need to know.

• Preprocessing text data

Text preprocessing is the method to clean and prepare text data. This module will teach you all the steps involved in preprocessing a text like Text Cleansing, Tokenization, Stemming, etc.

• Bag of Words Model

Bag of words is a Natural Language Processing technique of text modelling. In technical terms, we can say that it is a method of feature extraction with text data. This approach is a flexible and straightforward way of extracting features from documents. In this module, you will learn how to keep track of words, disregard the grammatical details, word order, etc.

• TF-IDF

TF is the term frequency (TF) of a word in a document. There are several ways of calculating this frequency, with the simplest being a raw count of instances a word appears in a document. IDF is the inverse document frequency(IDF) of the word across a set of documents. This suggests how common or rare a word is in the entire document set. The closer it is to 0, the more common is the word.

• N-grams

An N-gram is a series of N-words. They are broadly used in text mining and natural language processing tasks.

• Word2Vec

Word2vec is a method to create word embeddings by using a two-layer neural network efficiently. It was developed by Tomas Mikolov et al. at Google in 2013 to make the neural-network-based training of the embedding more efficient and since then has become the de facto standard for developing pre-trained word embedding.

• GLOVE

GloVe (Global Vectors for Word Representation) is an unsupervised learning algorithm, which is an alternate method to create word embeddings. It is based on matrix factorisation techniques on the word-context matrix.

• POS Tagging & Named Entity Recognition

We have learned the differences between the various parts of speech tags such as nouns, verbs, adjectives, and adverbs in elementary school. Associating each word in a sentence with a proper POS (part of speech) is known as POS tagging or POS annotation. POS tags are also known as word classes, morphological classes, or lexical tags. NER, short for, Named Entity Recognition is a standard Natural Language Processing problem which deals with information extraction. The primary objective is to locate and classify named entities in text into predefined categories such as the names of persons, organisations, locations, events, expressions of times, quantities, monetary values, percentages, etc.

• Introduction to Sequential models

A sequence, as the name suggests, is an ordered collection of several items. In this module, you will learn how to predict what letter or word appears using the Sequential model in NLP.

• Need for memory in neural networks

This module will teach you how critical is the need for memory in Neural Networks.

• Types of sequential models – One to many, many to one, many to many

In this module, you will go through all the types of Sequential models like one-to-many, many-to-one, and many-to-many.

• Recurrent Neural networks (RNNs)

An artificial neural network that uses sequential data or time-series data is known as a Recurrent Neural Network. It can be used for language translation, natural language processing (NLP), speech recognition, and image captioning.

• Long Short Term Memory (LSTM)

LSTM is a type of Artificial Recurrent Neural Network that can learn order dependence in sequence prediction problems.

• GRU

Great Recurrent Unit (GRU) is a gating mechanism in RNN. You will learn all you need to about the mechanism in this module.

• Applications of LSTMs

You will go through all the significant applications of LSTM in this module.

• Sentiment analysis using LSTM

An NLP technique to determine whether the data is positive, negative, or neutral is known as Sentiment Analysis. The most commonly used example is Twitter.

• Time series analysis

Time-Series Analysis comprises methods for analysing data on time-series to extract meaningful statistics and other relevant information. Time-Series forecasting is used to predict future values based on previously observed values.

• Neural Machine Translation

Neural Machine Translation (NMT) is a task for machine translation that uses an artificial neural network, which automatically converts source text in one language to the text in another language.

• Advanced Language Models

This module will teach several other widely used and advanced language models used in NLP.

Capstone Project

You will get your hands dirty with a real-time project under industry experts’ guidance from introducing you to Python to the introduction to artificial intelligence and machine learning and everything in between Python for AIML. Successful completion of the project will earn you a post-graduate certificate in artificial intelligence and machine learning.

Career Assistance: Resume building and Mock interviews

This post-graduate certificate program on artificial intelligence and machine learning will guide you through your career path to building your professional resume, attending mock interviews to boost your confidence and nurture you nailing your professional interviews.

PG Certificate from Great Lakes & UT Austin

Earn a Postgraduate Certificate in the top-rated Artificial Intelligence and Machine Learning online course from Great Lakes & UT Austin. Its exhaustive Curriculum will foster you into a highly-skilled professional and help you land a job at the world’s leading corporations.