Certified Artificial Intelligence Developer Program

1. Core Outcomes
   
   Overview of Cloud Computing: Definition, characteristics, and service models (IaaS, PaaS, SaaS).
   

2. Key concepts covered
   

   • Cloud Computing Basics

   • Major Cloud Providers

   • AI on the Cloud Introduction

Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. Pandas DataFrame consists of three principal components, the data, rows, and columns.

1. Core Outcomes
   
   AI Services Overview: Introduction to AI services provided by cloud platforms (e.g., AWS AI/ML, Azure AI, Google AI Platform).
   

2. Key concepts covered
   

   • Overview of AI Services

   • Machine Learning on the Cloud

   • NLP on the Cloud

NLP Cloud is an artificial intelligence platform that allows you to use the most advanced AI engines, and even train your own engines with your own data. This platform is focused on data privacy by design so you can safely use AI in your business without compromising confidentiality, and even deploy our AI models on-premise / at the edge. We offer both small specific AI engines and large cutting-edge generative AI engines so you can easily integrate the most advanced AI features into your application at an affordable cost.

1. Core Outcomes
   
   Big Data on the Cloud: Introduction to cloud-based data storage and processing technologies (e.g., AWS S3, Azure Data Lake, Google BigQuery).
   

2. Key concepts covered
   

   • Big Data on the Cloud

   • Data Analytics with AI

   • AI-driven Big Data Solutions

Exploratory Data Analysis (EDA) is a crucial initial step in data science projects. It involves analyzing and visualizing data to understand its key characteristics, uncover patterns, and identify relationships between variables refers to the method of studying and exploring record sets to apprehend their predominant traits, discover patterns, locate outliers, and identify relationships between variables. EDA is normally carried out as a preliminary step before undertaking extra formal statistical analyses or modeling.

1. Core Outcomes
   
   Advanced AI Services: Deep learning frameworks on the cloud (e.g., TensorFlow, PyTorch), computer vision, and speech recognition.
   

2. Key concepts covered
   

   • Advanced AI Services

   • AI Ethics and Governance

   • Edge AI and IoT Integration

   • Future Trends

1. Core Outcomes
   
   Overview of AI in Banking: Definition, significance, and key applications.
   

2. Key concepts covered
   

   • Overview of AI in Banking

   • AI Technologies in Banking

   • Use Cases

   • Core Components of Digital Transformation

1. Core Outcomes
   
   Automated Customer Service: Chatbots, virtual assistants, and automated email responses.
   

2. Key concepts covered
   

   • Automated Customer Service

   • Fraud Detection and Prevention

   • Risk Management

1. Core Outcomes
   
   Personalised Marketing and Recommendations: Customer segmentation, product recommendations, and targeted advertising.
   

2. Key concepts covered
   

   • Personalised Marketing and Recommendations

   • Predictive Analytics

   • Sentiment Analysis and VoC Analytics

1. Core Outcomes
   
   Emerging AI Technologies in Banking: Blockchain, explainable AI, and quantum computing.
   

2. Key concepts covered
   

   • Emerging AI Technologies

   • Ethical Considerations

   • Ethical Considerations

1. Core Outcomes
   
   Overview of Feature Selection: Definition, importance, and relevance in machine learning.
   

2. Key concepts covered
   

   • Overview of Feature Selection

   • Types of Features

   • Feature Selection Techniques

1. Core Outcomes
   
   Introduction to Filter Methods: Correlation-based feature selection, statistical tests (e.g., chi-square, ANOVA).
   

2. Key concepts covered
   

   • Filter Methods Overview

   • Information Gain and Mutual Information

   • Feature Importance Techniques

Information gain is also used as the split criterion for implementation of the CART information-gain algorithm (Regression and Classification Tree) found in the Python library for machine learning and scikit-learn, which use entropy calculations for model configuration.

1. Core Outcomes
   
   Overview of Wrapper Methods: Sequential feature selection, forward selection, backward elimination, and exhaustive search.
   

2. Key concepts covered
   

   • Wrapper Methods Overview

   • Recursive Feature Elimination (RFE)

   • Genetic Algorithms

1. Core Outcomes
   
   Embedded Methods: Regularisation techniques (Lasso, Ridge), feature importance in tree-based models.
   

2. Key concepts covered
   

   • Embedded Methods

   • Feature Selection with Deep Learning

   • Feature Engineering vs. Feature Selection

Feature selection is a process that chooses a subset of features from the original features so that the feature space is optimally reduced according to a certain criterion.

Feature selection is a critical step in the feature construction process. In text categorization problems, some words simply do not appear very often. Perhaps the word “groovy” appears in exactly one training document, which is positive. Is it really worth keeping this word around as a feature ? It’s a dangerous endeavor because it’s hard to tell with just one training example if it is really correlated with the positive class or is it just noise. You could hope that your learning algorithm is smart enough to figure it out. Or you could just remove it.

There are three general classes of feature selection algorithms: Filter methods, wrapper methods and embedded methods.

1. Core Outcomes
   
   Overview of Chatbots: Definition, history, and importance and types of chatbots.
   

2. Key concepts covered
   

   • Overview of Chatbots

   • Types of Chatbots

   • Introduction to NLP

   • Chatbot Design Considerations

1. Core Outcomes
   
   Introduction to AI-based Chatbots: Machine learning and deep learning approaches.
   

2. Key concepts covered
   

   • AI-based Chatbots Overview

   • Dialog Systems

   • Chatbot Platforms and Frameworks

   • Training Chatbots

1. Core Outcomes
   
   Natural Language Understanding (NLU): Named Entity Recognition (NER), sentiment analysis, and context understanding.
   

2. Key concepts covered
   

   • NLU and Sentiment Analysis

   • Conversational AI

   • Deployment and Integration

   • Chatbot Analytics

1. Core Outcomes
   
   Ethical Considerations in Chatbot Development: Privacy, bias, transparency, and accountability.
   

2. Key concepts covered
   

   • Ethical Considerations

   • Chatbots in Business and Society

   • Future Trends

1. Core Outcomes
   
   Overview of AI Bias: Definition, types of bias (e.g., algorithmic bias, dataset bias).
   

2. Key concepts covered
   

   • Overview of AI Bias

   • Importance of Ethical AI

   • Legal and Regulatory Landscape

   • Bias Mitigation Techniques

1. Core Outcomes
   
   Importance of Interpretability: Understanding how AI models make decisions.
   

2. Key concepts covered
   

   • Importance of Interpretability

   • Interpretability Techniques

   • Explainability vs. Transparency

   • Bias Detection with Interpretability

1. Core Outcomes
   
   Definition of Fairness: Different definitions of fairness (e.g., individual fairness, group fairness).
   

2. Key concepts covered
   

   • Definition of Fairness

   • Fairness Metrics

   • Fairness-aware AI Techniques

   • Evaluating Fairness

1. Core Outcomes
   
   Real-world Case Studies: Examples of AI bias and ethics failures in various domains (e.g., criminal justice, healthcare).
   

2. Key concepts covered
   

   • Real-world Case Studies

   • Best Practices for Ethical AI Development

   • Stakeholder Engagement

   • Responsible AI Governance

1. Core Outcomes
   
   Python Basics: Syntax, variables, data types, and basic operations.
   

2. Key concepts covered
   

   • Python Basics

   • Data Structures

   • Control Flow

   • Functions and Modules

Machine learning has revolutionised the way we approach data-driven problems, enabling computers to learn from data and make predictions or decisions without explicit programming. Python, with its rich ecosystem of libraries and tools, has become the de facto language for implementing machine learning algorithms. Whether you’re new to the field or looking to expand your skills, understanding the fundamentals of machine learning and how to apply them using Python is essential.

1. Core Outcomes
   
   Introduction to NumPy: Arrays, array creation, indexing, and slicing.
   

2. Key concepts covered
   

   • Introduction to NumPy

   • Introduction to Pandas

   • Data Cleaning and Preprocessing

   • Data Visualisation

Pandas is an open-source, BSD-licensed library written in Python Language. Pandas provide high-performance, fast, easy-to-use data structures, and data analysis tools for manipulating numeric data and time series.

Pandas is built on the NumPy library and written in languages like Python, Cython, and C. In Pandas, we can import data from various file formats like JSON, SQL, Microsoft Excel, etc.

Numpy is the fundamental library of Python, used to perform scientific computing. It provides high-performance multidimensional arrays and tools to deal with them.

A Numpy array is a grid of values (of the same type) that are indexed by a tuple of positive integers, Numpy arrays are fast, easy to understand, and give users the right to perform calculations across arrays.

1. Core Outcomes
   
   Introduction to Machine Learning: Types of machine learning (supervised, unsupervised, reinforcement).
   

2. Key concepts covered
   

   • Overview of Machine Learning

   • Supervised Learning: Classification and Regression

   • Model Evaluation

   • Model Training and Evaluation

1. Core Outcomes
   
   Real-world Applications of Machine Learning: Examples in various domains (e.g., healthcare, finance, marketing).
   

2. Key concepts covered
   

   • Model Deployment

   • Real-world Applications of Machine Learning

   • Best Practices and Pitfalls

   • Case Studies

1. Core Outcomes
   
   Overview of Supervised Learning: Definition, types of supervised learning tasks (classification, regression).
   

2. Key concepts covered
   

   • Overview of Supervised Learning

   • Introduction to Linear Regression

   • Simple Linear Regression

   • Multiple Linear Regression

   • Model Evaluation Metrics

What is Linear Regression?

Linear regression is a type of supervised machine learning algorithm that computes the linear relationship between the dependent variable and one or more independent features by fitting a linear equation to observed data.

When there is only one independent feature, it is known as Simple Linear Regression, and when there are more than one feature, it is known as Multiple Linear Regression.

Similarly, when there is only one dependent variable, it is considered Univariate Linear Regression, while when there are more than one dependent variables, it is known as Multivariate Regression.

Why Linear Regression is Important?

The interpretability of linear regression is a notable strength. The model’s equation provides clear coefficients that elucidate the impact of each independent variable on the dependent variable, facilitating a deeper understanding of the underlying dynamics. Its simplicity is a virtue, as linear regression is transparent, easy to implement, and serves as a foundational concept for more complex algorithms.

Linear regression is not merely a predictive tool; it forms the basis for various advanced models. Techniques like regularization and support vector machines draw inspiration from linear regression, expanding its utility. Additionally, linear regression is a cornerstone in assumption testing, enabling researchers to validate key assumptions about the data.

1. Core Outcomes
   
   Introduction to Classification: Definition, types of classification algorithms (e.g., logistic regression, decision trees, support vector machines).
   

2. Key concepts covered
   

   • Introduction to Classification

   • Logistic Regression

   • Decision Trees

   • Model Evaluation for Classification

What is Classification in Machine Learning?

Classification in machine learning is a type of supervised learning approach where the goal is to predict the category or class of an instance that are based on its features. In classification it involves training model ona dataset that have instances or observations that are already labeled with Classes and then using that model to classify new, and unseen instances into one of the predefined categories.

List of Machine Learning Classification Algorithms

Classification algorithms organize and understand complex datasets in machine learning. These algorithms are essential for categorizing data into classes or labels, automating decision-making and pattern identification. Classification algorithms are often used to detect email spam by analysing email content. These algorithms enable machines to quickly recognize spam trends and make real-time judgments, improving email security.

Some of the top-ranked machine learning algorithms for Classification are:

1. Logistic Regression

2. Decision Tree

3. Random Forest

4. Support Vector Machine (SVM)

5. Naive Bayes

6. K-Nearest Neighbors (KNN)

1. Core Outcomes
   
   Support Vector Machines (SVM): Maximising margin, kernel tricks, and non-linear classification.
   

2. Key concepts covered
   

   • Support Vector Machines (SVM)

   • Ensemble Methods

   • Hyperparameter Tuning

   • Model Selection and Evaluation

Decision Tree

Decision Trees are versatile and simple classification and regression techniques. Recursively splitting the dataset into key-criteria subgroups provides a tree-like structure. Judgments at each node produce leaf nodes. Decision trees are easy to understand and depict, making them useful for decision-making. Overfitting may occur, therefore trimming improves generality. A tree-like model of decisions and their consequences, including chance event outcomes, resource costs and utility.

The algorithm used for both classification and regression tasks. They model decisions and their possible results as tree, with branches representing choices and leaves representing outcomes.

Features of Decision Tree

1. Tree-Like Structure: Decision Trees have a flowchart-like structure, where each internal node represents a “test” on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represent classification rules.

2. Simple to Understand and Interpret: One of the main advantages of Decision Trees is their simplicity and ease of interpretation. They can be visualised, which makes it easy to understand how decisions are made and explain the reasoning behind predictions.

3. Versatility: Decision Trees can handle both numerical and categorical data and can be used for both regression and classification tasks, making them versatile across different types of data and problems.

4. Feature Importance: Decision Trees inherently perform feature selection, giving insights into the most significant variables for making the predictions. The top nodes in a tree are the most important features, providing a straightforward way to identify critical variables.

1. Core Outcomes
   
   Overview of Unsupervised Learning: Definition, types of unsupervised learning tasks (clustering, dimensionality reduction).
   

2. Key concepts covered
   

   • Overview of Unsupervised Learning

   • Introduction to Clustering

   • K-means Clustering

   • Hierarchical Clustering

   • Evaluating Clustering Performance

What is Clustering ?

The task of grouping data points based on their similarity with each other is called Clustering or Cluster Analysis. This method is defined under the branch of Unsupervised Learning, which aims at gaining insights from unlabelled data points, that is, unlike supervised learning we don’t have a target variable.

Clustering aims at forming groups of homogeneous data points from a heterogeneous dataset. It evaluates the similarity based on a metric like Euclidean distance, Cosine similarity, Manhattan distance, etc. and then group the points with highest similarity score together.

1. Core Outcomes
   
   Density-based Clustering: DBSCAN algorithm, density-reachability, and density-connectivity.
   

2. Key concepts covered
   

   • Density-based Clustering: DBSCAN

   • Model Evaluation for Density-based Clustering

   • Introduction to Dimensionality Reduction

   • Principal Component Analysis (PCA)

Why DBSCAN?

Partitioning methods (K-means, PAM clustering) and hierarchical clustering work for finding spherical-shaped clusters or convex clusters. In other words, they are suitable only for compact and well-separated clusters. Moreover, they are also severely affected by the presence of noise and outliers in the data.

Real-life data may contain irregularities, like:

1. Clusters can be of arbitrary shape such as those shown in the figure below.

2. Data may contain noise.

Parameters Required For DBSCAN Algorithm

1. eps: It defines the neighborhood around a data point i.e. if the distance between two points is lower or equal to ‘eps’ then they are considered neighbors. If the eps value is chosen too small then a large part of the data will be considered as an outlier. If it is chosen very large then the clusters will merge and the majority of the data points will be in the same clusters. One way to find the eps value is based on the k-distance graph.

2. MinPts: Minimum number of neighbors (data points) within eps radius. The larger the dataset, the larger value of MinPts must be chosen. As a general rule, the minimum MinPts can be derived from the number of dimensions D in the dataset as, MinPts >= D+1. The minimum value of MinPts must be chosen at least 3.

1. Core Outcomes
   
   Introduction to Association Rule Mining: Definition, support, confidence, and lift.
   

2. Key concepts covered
   

   • Introduction to Association Rule Mining: Apriori Algorithm

   • Evaluating Association Rules

   • Introduction to Anomaly Detection

   • Anomaly Detection Techniques

Apriori algorithm is given by R. Agrawal and R. Srikant in 1994 for finding frequent itemsets in a dataset for boolean association rule. Name of the algorithm is Apriori because it uses prior knowledge of frequent itemset properties. We apply an iterative approach or level-wise search where k-frequent itemsets are used to find k+1 itemsets.

To improve the efficiency of level-wise generation of frequent itemsets, an important property is used called Apriori property which helps by reducing the search space.

Apriori Property –
All non-empty subset of frequent itemset must be frequent. The key concept of Apriori algorithm is its anti-monotonicity of support measure. Apriori assumes that

All subsets of a frequent itemset must be frequent(Apriori property).
If an itemset is infrequent, all its supersets will be infrequent.

1. Core Outcomes
   
   Advanced Clustering Techniques: Gaussian Mixture Models (GMM), spectral clustering.
   

2. Key concepts covered
   

   • Advanced Clustering Techniques: Gaussian Mixture Models (GMM), spectral clustering

   • Semi-supervised Learning

   • Real-world Applications

   • Best Practices and Pitfalls

1. Core Outcomes
   
   Overview of Dimensionality Reduction: Definition, importance, and applications.
   

2. Key concepts covered
   

   • Overview of Dimensionality Reduction

   • Curse of Dimensionality

   • Introduction to PCA

   • PCA Algorithm

   • Interpreting PCA Results

What is Principal Component Analysis(PCA)?

Principal Component Analysis(PCA) technique was introduced by the mathematician Karl Pearson in 1901. It works on the condition that while the data in a higher dimensional space is mapped to data in a lower dimension space, the variance of the data in the lower dimensional space should be maximum.

• Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation that converts a set of correlated variables to a set of uncorrelated variables.PCA is the most widely used tool in exploratory data analysis and in machine learning for predictive models. Moreover,

• Principal Component Analysis (PCA) is an unsupervised learning algorithm technique used to examine the interrelations among a set of variables. It is also known as a general factor analysis where regression determines a line of best fit.

• The main goal of Principal Component Analysis (PCA) is to reduce the dimensionality of a dataset while preserving the most important patterns or relationships between the variables without any prior knowledge of the target variables.

Principal Component Analysis (PCA) is used to reduce the dimensionality of a data set by finding a new set of variables, smaller than the original set of variables, retaining most of the sample’s information, and useful for the regression and classification of data.

1. Core Outcomes
   
   Singular Value Decomposition (SVD): Understanding SVD and its relationship with PCA.
   

2. Key concepts covered
   

   • Singular Value Decomposition (SVD)

   • Non-negative Matrix Factorization (NMF)

   • Comparison of PCA, SVD, and NMF

Principal Component Analysis

This method was introduced by Karl Pearson. It works on the condition that while the data in a higher dimensional space is mapped to data in a lower dimension space, the variance of the data in the lower dimensional space should be maximum.

It involves the following steps:

• Construct the covariance matrix of the data.

• Compute the eigenvectors of this matrix.

• Eigenvectors corresponding to the largest eigenvalues are used to reconstruct a large fraction of variance of the original data.

Hence, we are left with a lesser number of eigenvectors, and there might have been some data loss in the process. But, the most important variances should be retained by the remaining eigenvectors.

Advantages of Dimensionality Reduction

• It helps in data compression, and hence reduced storage space.

• It reduces computation time.

• It also helps remove redundant features, if any.

• Improved Visualization: High dimensional data is difficult to visualize, and dimensionality reduction techniques can help in visualizing the data in 2D or 3D, which can help in better understanding and analysis.

• Overfitting Prevention: High dimensional data may lead to overfitting in machine learning models, which can lead to poor generalization performance. Dimensionality reduction can help in reducing the complexity of the data, and hence prevent overfitting.

• Feature Extraction: Dimensionality reduction can help in extracting important features from high dimensional data, which can be useful in feature selection for machine learning models.

• Data Preprocessing: Dimensionality reduction can be used as a preprocessing step before applying machine learning algorithms to reduce the dimensionality of the data and hence improve the performance of the model.

• Improved Performance: Dimensionality reduction can help in improving the performance of machine learning models by reducing the complexity of the data, and hence reducing the noise and irrelevant information in the data.

1. Core Outcomes
   
   Introduction to Non-linear Dimensionality Reduction: Challenges in linear methods, need for non-linear techniques.
   

2. Key concepts covered
   

   • Introduction to Non-linear Dimensionality Reduction

   • Locally Linear Embedding (LLE)

   • t-Distributed Stochastic Neighbor Embedding (t-SNE)

   • Comparison of Linear and Non-linear Techniques

A nonlinear dimensionality reduction method used in data analysis and machine learning is called isomap, short for isometric mapping. Isomap was developed to maintain the inherent geometry of high-dimensional data as a substitute for conventional techniques like Principal Component Analysis (PCA). Isomap creates a low-dimensional representation, usually a two- or three-dimensional map, by focusing on the preservation of pairwise distances between data points.

This technique works especially well for extracting the underlying structure from large, complex datasets, like those from speech recognition, image analysis, and biological systems. Finding patterns and insights in a variety of scientific and engineering domains is made possible by Isomap’s capacity to highlight the fundamental relationships found in data.

1. Core Outcomes
   
   Autoencoders: Basics of autoencoders, encoding and decoding, applications in dimensionality reduction.
   

2. Key concepts covered
   

   • Autoencoders

   • Variational Autoencoders (VAEs)

   • Applications of Dimensionality Reduction

   • Best Practices and Pitfalls

What are Autoencoders?

Autoencoders are a specialized class of algorithms that can learn efficient representations of input data with no need for labels. It is a class of artificial neural networks designed for unsupervised learning. Learning to compress and effectively represent input data without specific labels is the essential principle of an automatic decoder. This is accomplished using a two-fold structure that consists of an encoder and a decoder. The encoder transforms the input data into a reduced-dimensional representation, which is often referred to as “latent space” or “encoding”. From that representation, a decoder rebuilds the initial input. For the network to gain meaningful patterns in data, a process of encoding and decoding facilitates the definition of essential features.

The general architecture of an autoencoder includes an encoder, decoder, and bottleneck layer.

1. Encoder

   • Input layer take raw input data

   • The hidden layers progressively reduce the dimensionality of the input, capturing important features and patterns. These layer compose the encoder.

   • The bottleneck layer (latent space) is the final hidden layer, where the dimensionality is significantly reduced. This layer represents the compressed encoding of the input data.

2. Decoder

   • The bottleneck layer takes the encoded representation and expands it back to the dimensionality of the original input.

   • The hidden layers progressively increase the dimensionality and aim to reconstruct the original input.

   • The output layer produces the reconstructed output, which ideally should be as close as possible to the input data.

3. The loss function used during training is typically a reconstruction loss, measuring the difference between the input and the reconstructed output. Common choices include mean squared error (MSE) for continuous data or binary cross-entropy for binary data.

4. During training, the autoencoder learns to minimize the reconstruction loss, forcing the network to capture the most important features of the input data in the bottleneck layer.