Professional Certificate in Artificial Intelligence R

Python Introduction

The module will commence with the basic introduction to Python and why there arises a need to learn Python in Data Science. Gain knowledge about the installation of Anaconda and get a basic understanding of the Jupyter Notebook. We will advance the course with the understanding of the Python Syntax followed by Variables and Operators.

Data Types

This module will talk about strings, lists, sets, tuples, and dictionaries. Basically, we will be covering all types of data.

Control Flow and Conditional Statements

Learn about the If, Elif, and Else statements in Python along with While Loops, For Loops, Nested Loops, and List and Dictionary Comprehensions.

Functions

Gain valuable information about User Defined functions, Lambda Functions, Map, Filter, and Reduce.

Numpy

This Data Visualization and EDA module will equip you with the knowledge of Arrays, Basic Operations in Numpy, Indexing, and Array Processing.

Pandas

Gain information related to Series, DataFrames, Indexing and Slicing, Groupby, Concatenating, Merging Joining, Missing Values, Operations, Data Input and Output, Pivot, and Cross tab.

Visualization with Matplotlib and Seaborn

Introduction to Matplotlib. Learn about Line plots, histograms, box, and Violin Plots, Scatterplot, Heatmaps, and Subplots, and how to use them in Data Science.

Exploratory Data Analysis

Learn in-depth about EDA on a dataset, analysis, and visualization, treating missing values, outlier detection and treatment, and feature engineering.

Web Scraping

Gain knowledge of Web Scraping basics and libraries, web scraping advanced, and what machine learning is. Along with this, learn about Supervised versus unsupervised learning and Regression VS Classification.

Introduction to Machine Learning

Get exposure to the basic understanding of machine learning and learn about the major differences between Supervised and Unsupervised Learning. The module will further cover the difference between Regression and Classification.

Linear Regression

Gain valuable information related to Simple Linear Regression, Multiple Linear Regression, OLS Assumptions, and Regression Evaluation Metrics.

Telecom

The first project dedicated to supervised learning will be about the telecom sector and will equip students with mobile phone activity prediction.

Automobile

This Automobile project will teach the students about Automobile Mileage Prediction with the data available.

Manufacturing and Logistic

This project will deal with the Mercedes Car Production and time prediction with the available data.

BFSI

In this project, the students will learn about medical insurance premium prediction.

eCommerce and Retail

The eCommerce and Retail project deals with equipping the students with the knowledge of predicting the business value.

Medical and Pharmacy

Learn how to predict Ventilator Pressure in the Medical and Pharmacy project you will be doing under the mentorship of skilled professionals.

Transportation

This project will equip the students with the knowledge to predict the fare price of cabs.

Classification Overview

Get an overview of classification and why it has got the upper hand over Linear Regression. Know everything about it in this module.

Logistic Tregression

Learn everything about the Logistic Regression, the Logistic Model, Logit and Sigmoid Functions, and setting the threshold and understanding decision boundary.

Evaluation Techniques for Classification

Gain information on the confusion matrix and learn how to attain maximum accuracy while minimizing the error rate. The module will deal with learning TPR and FPR, precision and recall, F1 Score, and AUC-ROC.

Telecom

The project in the telecom domain will help you learn about customer churn prediction.

Automobile

The students will learn about Automobile Loan Default Detection and other associated details in this project.

Manufacturing and Logistic

This project will discuss steel defect detection in the manufacturing and logistic sector.

BFSI

The project will deal with the credit card customer churn prediction.

eCommerce and Retail

You will learn about predicting user behavior in the eCommerce and Retail project.

Medical and Pharmacy

The students will learn how to predict the scale of dementia in the Medical and Pharmacy sector.

Transportation

In this project, students will learn about road traffic injury prediction.

Decision Tree

This module will equip the students with the basic terminology in Decision Tree, Root Node and Terminal Node, Regression Trees, Classification Trees, advantages and disadvantages of Trees, Gini Index, Information Gain/Entropy, and Reduction in Variance, and Overfitting and Pruning.

Bagging - Random Forest

Learn in detail about what is Ensemble Learning, Bagging, and how it functions. Gain valuable information about what exactly Random Forest is and its functioning.

Resampling

Resampling deals with Cross-Validation, k-Fold Cross-Validation, and Stratified Cross-Validation.

Hyperparameter Tuning

This module deals with Parameters and Hyperparameters and teaches how to perform Hyperparameter Tuning, grid search, and randomized search.

Telecom

This project will help you in predicting the category of telecom customers.

Automobile

The automobile project will deal with automobile insurance prediction.

Manufacturing and Logistic

The manufacturing and logistic project will equip you with the knowledge of product quality prediction.

BFSI

In this project, the students will get exposure to the loan defaulters' prediction process and credit card recommendations.

eCommerce and Retail

In this project, we will teach the students how to forecast retail sales and the prediction of the amount spent by book retailers.

Medical and Pharmacy

The project will deal with the classification of deaths related to drugs.

Transportation

In this project, the students will learn about the predictions based on flight delays and predicting future bike shares.

Boosting Techniques

Learn what Boosting is and how it works. Gain information related to Ada Boosting, Gradient Boosting, and XG Boosting.

Telecom

The Telecom domain-specific project following the Boosting Techniques will help you equip the knowledge of monthly charge prediction.

Automobile

In this project focused on the Automobile domain, we will be focusing on car price prediction.

Manufacturing and Logistic

This particular project will discuss the Bosch production line and performance predictions.

BFSI

In this project, the students will learn how to detect fraud in insurance claims with the available data.

eCommerce and Retail

This module will deal with Real Estate Price Prediction and how to handle data.

Medical and Pharmacy

The medical and pharmacy dedicated project will talk about drug classification.

Transportation

In this project, we will be discussing cab customer behavior prediction.

Support Vector Machine

The Support Vector Machine module deals with Hyperplane, the Maximal Margin Classifier, Support Vector Classifiers, hard and soft Margin classification, classification with non-linear decision boundaries, and kernel trick.

K-Nearest Neighbor

Learn about the KNN algorithm, Linear, Polynomial, and Radial, and how to decide the number of neighbors in KNN.

Naive Bayes Algorithm

Get familiar with the Bayes Theorem and gain exposure to the terminology in Naive Bayes. Become knowledgeable of the types of Naive Bayes Classifier, Multinomial Naive Bayes, Bernoulli Naive Bayes, and Gaussian Naive Bayes.

Automobile

The Automobile domain-specific project will talk about train ticket price prediction.

Manufacturing and Logistic

In this project, we will be discussing Microsoft Azure Predictive Maintenance.

BFSI

This project will help you learn how to deal with data to determine whether the user will subscribe to a term deposit.

eCommerce and Retail

The eCommerce and Retail project will discuss the product recommendations for eCommerce websites.

Medical and Pharmacy

This project will provide you with hands-on experience in predicting heart disease rates and personalized cancer treatment.

Transportation

Prediction of taxi duration is what this transportation project will be all about.

Principal Component Analysis

Get a basic understanding of the concept of Dimensionality Reduction and why there arises a need to conduct it. Learn about the Principal Components and demonstration of 2D PCA and 3D PCA. Gain information about the Eigen Values, Eigen Vectors, and Orthogonality. This module will equip you with the knowledge of transforming the Eigenvalues into a new data set and will explain the proportion of variance in PCA.

Clustering

This particular module is designed in order to equip the students with the various Clustering methods and give exposure to K-Means Clustering, Centroids, and Medoids. Learn how to decide the optimal value of ‘k’ with the use of the Elbow Method. You will also be learning in detail about Hierarchical Clustering, Divisive and Agglomerative Clustering, and Dendrograms, along with their interpretation.

Association Rules

In this module, you will be introduced to the concept of Association Rules Mining, where you will learn in detail about the Association rules, market basket analysis, and Apriori/Support/Confidence/Lift.

Telecom

This project will deal with the Customer Requirements Segmentation.

Automobile

In this project, the students will gain knowledge about clustering the same types of cars together.

Manufacturing and Logistic

In the manufacturing and logistic project, the students will learn in detail about clustering pump data in order to detect anomalies.

BFSI

The BFSI domain-specific project will deal with the clustering process of mobile banking users.

eCommerce and Retail

The eCommerce and Retail project will talk about market basket analysis of groceries.

Medical and Pharmacy

In this project, the students will learn how to cluster similar patients together.

Transportation

The project deals with the clustering process of drivers based on the cars hired.

Time Series (Forecasting)

This module will provide you with a thorough understanding of what exactly is Times Series Data. Learn in detail about the Stationarity in Time Series Data and Augmented Dickey-Fuller Test, the Box-Jenkins Approach, the AR Process, and the MA Process. Gain valuable knowledge and skills related to ARIMA, SARIMA, ACF, PACF, and IACF plots. Additionally, the module will cover topics such as the decomposition of Times Series Trend, Seasonality, and Cyclic, Exponential Smoothing, and EWMA.

Introduction to Deep Learning

This module will deal with the concept of Deep Learning and will equip the students with the knowledge of how Deep Learning is utilized in real life. Gain exposure to the domains covered by Deep Learning along with its key advantages and roles to play in the field.

Introduction to NLP

This module will take you to the discussion of what NLP is and why it is important. In addition to this, you will get exposure to the applications of NLP, the unstructured data it deals with, and the life cycle of NLP. Gain an understanding of the tools for NLP, Libraries for NLP, and NLTK.

Text Preprocessing

The text Preprocessing module will deal with text normalizing, stop words removal, stemming, and Lemmatization. Along with this, the module will be pouring light on Tokenization, Text standardization, and exploratory data analysis.

Feature Engineering

Feature Engineering will equip the students with the knowledge of One hot encoding, Count Vectorizer, and TFIDF.

Part of Speech

Gain information about Part of Speech tagging, NER, and learn the core concepts of Topic modeling and Text classification.

NLP and Deep Learning for NLP

Learn what exactly is a sequence-based model. This module deals with concepts such as Vanishing Gradient, Exploding Gradient, Recurrent Neural Networks, LSTM, GRU, Batching Sequence Models, and information retrieval using word embedding. In addition, the course module will consist of learning text classification using Deep Learning approaches, Natural language generation, transformers and BERT, and text summarization using LSTM encoder and decoder.

Telecom

The students will learn about customer sentiment analysis towards companies along with gaining information about consumer complaints segmentation.

Automobile

This project will discuss the sentiment analysis of train derailment from Twitter comments.

BFSI

In this project, students will learn how to carry out a sentiment analysis of a stock using News Headlines.

eCommerce and Retail

This project will discuss the details of the Jio Mart reviews analysis. In addition to this, the students will discuss store app review analysis.

Medical and Pharmacy

Following the completion of this project, the students will be able to predict the rating of drugs based on their reviews.

Transportation

This project deals with the sentiment analysis of car companies based on Tweets.

Introduction to Neural Networks

What is Neural Network? Introduction to Neuron and Perceptron along with Primitive Neuron and Sigmoid Neuron. This course module will also explain the types of Activation functions that are used in deep learning networks. Gain an in-depth understanding of Cost Functions, Gradient Descent, Stochastic Gradient Descent, the feedforward model of the neural network, and the disadvantages of the feedforward model. Where applying weights to the feedforward model leads to and backpropagation algorithm.

Artificial Neural Network

This module will deal with the core understanding of Neural Networks, biological inspiration, perceptron learning, binary classification, backpropagation Learning, and Object Recognition.

Tensorflow and Keras

The Tensorflow and Keras module gives students exposure to the Tensorflow, Debugging, and Monitoring, Keras for classification and regression in Typical Data Science Problems, and Setting up KERAS. Learn about the different layers in KERAS and how to create a Neural Network. Gain valuable information on the training models and monitoring along with Artificial Neural Networks.

Recurrent Neural Network

This module will provide the students with a brief introduction to RNN, its network structure, types, Bidirectional RNN, and its limitations. Additionally, it will deal with training an RNN with a use case and introduce the students to LSTM and its architecture and variants. The Recurrent Neural Network model will also help in time series forecasting or sequential modeling using LSTM.

Convolutional Neural Network

Introduction to CNN and Convolutional operations are what this module will deal with alongside its features, ReLu, Pooling, and fully connected layer. The students will learn about training a CNN and Image Classification.

Telecom

This project talks about consumer complaints segmentation and how to use that data.

Automobile

In this project, the students will be equipped with the knowledge to predict the horsepower of the car.

Manufacturing and Logistic

The manufacturing and logistic project will discuss the product casting defect detection and number plate detection techniques.

BFSI

This project is proven extremely helpful in carrying out stock market predictions.

eCommerce and Retail

The eCommerce and Retail project at Data Folkz will talk about product recommendations through images and product demand forecasting.

Medical and Pharmacy

Learn how to detect diabetic retinopathy with this project, along with getting equipped with the knowledge of classifying lung diseases from X-Rays.

Open CV

This module will give the students an exposure to the introduction to CV and how to use OpenCV to work with image files. Additionally, the students will learn more about image manipulation involving smoothing and blurring, translation, rotation, and cropping. Learn about thresholding and morphological operations and how to open and stream video with OpenCV. Gain valuable information such as creating color histograms with OpenCV, face recognition, and template matching.

Advanced Computer Vision

Learn about transfer learning using Keras and gain information related to VGG, RESNET, object detection, drawing bounding boxes, and Yolo.

Deployment

This topic will discuss creating pickle and frozen files, cloud deploying machine learning, and a Deep Learning model for production.