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Advanced Data Science And Artificial Intelligence

  • Live Instructor-Led Course
  • Internship with Live Projects
  • Industry-Relevant Curriculum
  • Online and Offline Training Available
  • Placement Support Until You Get Placed

Our Advanced Data Science and Artificial Intelligence certification course, led by industry veterans who built AI platforms like QuestionPaper.ai and vigyana, provides comprehensive training online and offline in Kochi, Kottayam, and Calicut, Kerala. This program features an industry-relevant curriculum, hands-on experience through internships, and placement support – making IPSR the top data science training institution in Kerala to propel your career.

  • Advanced
  • Last updated 24 June, 2024
  • English
Course Description

The speciality of our Advanced Data Science and Artificial Intelligence certification course at IPSR is that you will be getting training and guidance from the experts who have already created AI platforms like QuestionPaper.ai and vigyana. Our training team is lead by AI Specialists, Data Science Experts and AI Product Architects. This advanced artificial intelligence and data science course in Kerala features an industry-relevant curriculum and provides both online and offline classes at our Kochi, Kottayam, and Calicut locations. Gain hands-on experience through internships with live projects and benefit from IPSR’s placement support until you get placed. Join us for top-notch data science training institution in Kerala and advance your skills in this dynamic field.

What you’ll learn
  • Python
  • Data Analytics using python .
  • Data Analysis with NumPy
  • Data Manipulation with pandas
  • R Programming
  • NLP
  • Generative AI
Introduction to Python - Overview of Python programming language, Installation and setup of Python interpreter,Setting up python development environments (IDLE, Jupyter Notebook, Colab)
Fundamentals of Python - Understanding Statements, Expressions and Indentation,Overview of Identifiers, Keywords and Comments, Variables: Declaration, Assignment and Naming Conventions,Common Data Types: Integers, Floats and Strings, Type Casting and Conversion,Operators in Python,Hands-on Activity
Loops, Functions & Error Handling - Conditional statements (if, elif, else), Looping constructs (for, while),Loop Control Statements: Break, Continue and Pass, Defining and Calling Functions, Function Parameters and Return Values, Scope of Variables (Global and Local),Recursive Functions,Map, Reduce and Filter,Introduction to Exceptions,Try, Except and Finally Blocks,Handling Common Errors,Hands-on Activity
Data Structures in Python - Lists & Manipulation Techniques,Slicing and Indexing in Lists,Tuples & Operations on Tuples,Slicing And Indexing in Tuples, Common Operations on Both Lists and Tuples,Dictionaries & Manipulating Dictionaries,Sets & Manipulating Sets, Common Operations on Both Dictionaries and Sets,Hands-on Activity
Introduction to Object-Oriented Programming - Classes and objects, Attributes and methods, Constructor and destructor methods, Inheritance and polymorphism
File Handling, Working with Libraries and Packages in Python - Reading from and writing to files, File modes and operations, Handling file exceptions, Introduction to external libraries and packages, Using pip for package management, Installing and importing libraries
Introduction to NumPy - Overview of NumPy and its features, Installing NumPy and setting up the development environment, Introduction to NumPy arrays: creation, indexing, and slicing, Creating multidimensional arrays
Array Manipulation - Reshaping and resizing arrays, Stacking and splitting arrays, Broadcasting and vectorization
Mathematical Operations -
Data Analysis with NumPy - Loading and saving data using NumPy, Statistical analysis with NumPy, Data manipulation and filtering
Introduction to pandas - Overview of pandas and its benefits, Installation and setup, Introduction to pandas data structures: Series and DataFrame
Data Manipulation with pandas - Creating pandas Series and DataFrames, Indexing and selecting data, Filtering and sorting data
Data Cleaning and Preprocessing with pandas -
Data Aggregation and Grouping - Grouping data with pandas, Aggregation functions: sum, mean, count, etc, Combining and merging datasets
Time Series Analysis & Visualizations with pandas - Data visualization using Matplotlib (basic plots, customization, EDA), Handling time series data with pandas, Resampling and frequency conversion, Time-based indexing and slicing
Univariate Analysis with pandas - Descriptive statistics: mean, median, mode, variance, standard deviation Distribution analysis: histograms, density plots, box plots, Summary statistics and visualization for single variables
Multivariate Analysis with Pandas - Correlation analysis: Pearson correlation, Spearman correlation, Scatter plots and pair plots for exploring relationships between variables, Heatmaps for visualizing correlation matricesTurn on screen reader support
Working with SQL - What is SQL? Difference Between RDBMS and DBMS,Difference between SQL and NoSQL,Setting Up the Environment (MySQL),SQL Basics - DDL Commands, DML Commands, DQL Commands, String Operations, Ordering the display of tuples, SET Operations,Aggregation in SQL, Grouping in SQL, HAVING, EXISTS, ANY, CASE, SubQueries, Set Membership, Performing Join Operations Turn on screen reader support
Querying SQL Databases with Python - Stored procedures, Executing SQL queries from Python
Introduction to Machine Learning (ML) - What Is ML? ML Vs AI, ML Workflow, Types of Machine Learning – Supervised, Unsupervised and Reinforcement Splitting the data into a Training and Test set, Feature Scaling, handling categorical variables, Popular ML Algorithms Clustering, Classification And Regression.
Supervised Learning - Regression (Simple Linear Regression, Multiple Linear Regression, Polynomial Regression,Evaluation of regression models), Classification (K Nearest Neighbors (KNN), Naive Bayes Classifier, Decision Tree Algorithm, XGBoost, Random Forest Algorithm, Support Vector Machines (SVM) ).
Unsupervised Learning - K-means Clustering, Hierarchical Clustering - Agglomerative & divisive clusterings,Dimensionality Reduction Techniques - PCA, Recommendation Systems
Introduction to Natural Language Processing (NLP) - Overview of NLP, Text Preprocessing in NLP - Stemming, stop words, lemmatization, vectorisation, bag of words, NLP Libraries - NLTK,Feature Extraction and Representation, LLM Models, Building A Text Classification Model
Case Study - Hands-on Activity to Implement text analysis applications
Indroduction to Deep learning - Overview of artificial neural networks (ANNs),History and evolution of deep learning,Understanding basic components: neurons, layers, and activation functions,Introduction to gradient descent and backpropagation
Building Blocks of Deep Learning - Single-layer perceptrons, Multilayer perceptrons (MLPs) and feedforward neural networks, Implementing a basic neural network from scratch using Python and NumPy
Deep Learning Architectures - Convolutional Neural Networks (CNNs) for image classification, Recurrent Neural Networks (RNNs) forsequence modeling, Long Short-Term Memory (LSTM) networks for sequential data
Optimization Algorithms - Gradient descent and backpropagation, Stochastic gradient descent (SGD) and its variants, Adaptive learning rate methods (Adam, RMSprop)
Computer Vision - Object Detection, OpenCV, Introduction to region-based detectors (RCNN) Single Shot Multibox Detector (SSD) and You Only Look Once (YOLO) algorithms,Implementing object detection models for real-time applications.
Practical Applications of Deep Learning - Computer vision tasks: object detection, image segmentation, Natural language processing tasks: sentiment analysis, language translation, Reinforcement learning applications: game playing, robotics
Introduction to Recurrent Neural Networks (RNNs) - Overview of neural networks for sequential data processing, Basics of recurrent neural networks and their architecture,challenges in training RNNs: vanishing gradients, exploding gradients
Long Short-Term Memory (LSTM) Networks - Introduction to LSTM networks and their advantages over RNNs, Structure of LSTM cells: input gate,forget gate, output gate, Gated Recurrent Unit (GRU) networks as an alternative to LSTMs
Applications of RNNs in Time Series Analysis - Time series prediction using RNNs: forecasting stock prices, weather data , Time series classification: anomaly detection, event prediction
Introduction to Recurrent Neural Networks (RNNs) - Overview of neural networks for sequential data processing, Basics of recurrent neural networks and their architecture,challenges in training RNNs: vanishing gradients, exploding gradients
Introduction to Transformer Architecture - Understanding the transformer architecture and its components (self-attention, feedforward layers), Overview of pre-trained transformer-based models (e.g., GPT, BERT), Hands-on implementation of basic transformer models usingTensorFlow or PyTorch
Training Large Language Models - Data preprocessing techniques for training LLMs, Transfer learning and fine-tuning strategies for pre-trained LLMs,Exploring training datasets and corpora for LLM development
LLMs in Creative Writing and Content Generation - Generating poetry, stories, and other creative content with LLMs
Introduction to R -
Data Types and Data Structures - Understanding vectors, matrices, arrays, lists, and data frames,Indexing and subsetting data structuresOperations on data structures
Control Structures and Functions - Conditional statements (if-else), Loops (for, while),Writing and using functions in R,Scope of variables
Data Import and Export and Data Manipulation - Importing data from various sources (CSV, Excel,), Exporting data to different formats,Handling missing data and data cleaning techniques, Introduction to the dplyr package for data manipulation,Filtering, selecting, arranging, and summarizing data
Data Visualization with ggplot2 & EDA - Introduction to ggplot2 for data visualization, Creating basic plots (scatter plots, histograms, bar plots),Customizing plots with themes and aesthetics, Descriptive statistics (mean, median, variance, standard deviation)Distribution analysis and visualizations, Correlation analysis
Machine Learning with R - Implementation of various ML algorithms in R - Regression, SVM, CART, Random forest, k Means etc
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Frequently Asked Questions

Our certification course provides an in-depth understanding of data science and artificial intelligence, focusing on advanced techniques and tools. You'll learn about machine learning, deep learning, data analysis, and AI applications. Designed for both students and working professionals, this course equips you with the skills to handle complex data problems and implement AI solutions effectively. You will be getting training and guidance from the experts who have already created AI platforms like QuestionPaper.ai and vigyana. Our training team is led by AI Specialists, Data Science Experts, and AI Product Architects.

This certification course is ideal for students aiming to enter the fields of data science and AI, as well as professionals looking to upskill or transition into these high-demand areas. Whether you're a beginner or have some experience, you'll gain valuable insights and practical skills to advance your career.

Language: Python, Popular Library: Numpy, Panda, Matplot, Keras, Framework: Tensorflow/Pytorch, Power BI, Tableau

This certification course combines theoretical knowledge with practical applications. You'll cover topics such as data preprocessing, machine learning algorithms, neural networks, and AI implementation. The hands-on projects and internships will help solidify your learning and provide real-world experience.

You'll work on a range of projects, including data analysis, predictive modeling, and AI-based solutions. These live projects are designed to simulate real-world scenarios and enhance your practical skills.

Graduates after completing this certification course are well-equipped for roles such as Data Scientist, AI Engineer, Machine Learning Engineer, and more. With the increasing demand for data science and AI professionals, you'll have numerous opportunities to advance your career in these dynamic fields.
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