Data Science & AI

Data Science And Artificial Intelligence

The Data Science and Artificial Intelligence (AI) course provides an in-depth understanding of the principles and practices that drive data analysis and AI solutions. This comprehensive program covers a broad range of topics, from fundamental data science techniques to advanced AI models.

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

The Data Science and Artificial Intelligence (AI) course provides an in-depth understanding of the principles and practices that drive data analysis and AI solutions. This comprehensive program covers a broad range of topics, from fundamental data science techniques to advanced AI models.

What you’ll learn
  • Develop proficiency in data handling, analysis, and visualization.
  • Master machine learning and deep learning concepts and their applications.
  • Gain expertise in building and deploying AI models using popular frameworks and tools.
  • Develop a strong understanding of data science and AI concepts and their applications in real-world scenarios.
  • Enhance your skills in data storytelling and communication.
  • Prepare for a career in data science and AI with hands-on experience and practical case studies.
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. Covers variables declaration, assignment, and naming conventions, common data types (integers, floats, strings), type casting, conversion, operators in Python. Includes hands-on activity.
Loops, Functions & Error Handling - Conditional statements (if, elif, else), looping constructs (for, while), defining and calling functions, function parameters and return values, recursive functions. Also covers map, reduce, filter, introduction to exceptions, try, except, and finally blocks, handling common errors. Includes hands-on activity.
Data Structures in Python - Lists and manipulation techniques, tuples and operations on tuples, dictionaries and manipulating dictionaries, sets and manipulating sets.
Introduction to Object-Oriented Programming - Covers 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 - Covers reshaping and resizing arrays, stacking and splitting arrays, broadcasting and vectorization.
Mathematical Operations - Element-wise operations, universal functions (ufuncs) in NumPy, aggregation and reduction 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 - Handling missing values: dropping, filling, interpolation. Removing duplicates, data transformation, and reshaping.
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 matrices.
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.
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 for 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. Dimensionality Reduction Techniques - PCA, Recommendation Systems.
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.
Convolutional Neural Networks (CNN) - Introduction to CNNs and their architecture. Convolutional layers, pooling layers, and fully connected layers. Common CNN architectures (LeNet, AlexNet, VGG, etc.).
Recurrent Neural Networks (RNN) - Introduction to RNNs and their architecture. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells.
Deep Learning Frameworks - Overview of popular deep learning frameworks (TensorFlow, PyTorch, Keras). Hands-on exercises using TensorFlow or PyTorch for building and training neural networks.
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.
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.
Introduction to Flask framework - Flask Configuration, Application creation, Routing, Variable Rules, URL Building, HTTP methods, Templates, Static Files, Request Object, Sending Form Data to Template, Sessions, Redirect & Errors, File Uploading.
Case study - Hands-on Activity to deploy any ML Model as Flask Web application.
Introduction to R - Overview of R and its applications, Installing R and RStudio, Basic syntax and data types in R, Introduction to R packages and libraries.
Data Types and Data Structures - Understanding vectors, matrices, arrays, lists, and data frames, Indexing and subsetting data structures. Operations 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 with dplyr - 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.
Introduction to Large Language Models - Overview of Generative AI, Evolution of Gen AI, Evolution of Large Language Models: from traditional to transformer-based architectures. Key challenges and considerations in developing and deploying LLMs.
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 using TensorFlow 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 Cloud Computing and AWS - Understanding cloud computing basics, Introduction to AWS services, Setting up an AWS account, Navigating the AWS Management Console.
Compute Services - Overview of Amazon EC2 (virtual servers), Introduction to AWS Lambda (serverless computing).
Storage Services - Introduction to Amazon S3 (object storage).
Security and Identity Services - Overview of AWS IAM (Identity and Access Management).
Stock Price Prediction (Regression) - Stock Price Prediction (Regression)
Medical Insurance Forecast (Regression) - Medical Insurance Forecast (Regression)
Credit Card Fraud Detection (Classification) - Credit Card Fraud Detection (Classification)
Movie Recommendation System - Movie Recommendation System
Diabetic Retinopathy Detection - Diabetic Retinopathy Detection
Black and white image colorization with OpenCV and Deep Learning - Black and white image colorization with OpenCV and Deep Learning
Emotion Detection (Image processing) - Emotion Detection (Image processing)
Object Detection using Yolo - Object Detection using Yolo
Driver Drowsiness Detection - Driver Drowsiness Detection
Amazon Alexa Review Analysis using NLP & LSTM - Amazon Alexa Review Analysis using NLP & LSTM
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Frequently Asked Questions

You don't need extensive expertise to begin learning data science! One of the great things about starting this journey is its accessibility to people from various backgrounds. Having solid knowledge of Mathematics, Statistics and Programming will be beneficial.

Data Science has become one of the most sought-after and lucrative careers due to the exponential growth of data and the critical need for organizations to use data-driven strategies. There are numerous prospects for the Data Science profession, including Job Opportunities, High Salaries, Career Advancement, Solving Real-World Problems,Interdisciplinary Skills.

Completing a data science and AI course opens doors to a wide range of career paths. You could pursue roles such as data scientist, machine learning engineer, AI researcher, data analyst, or business intelligence analyst in industries like healthcare, finance, technology, and more. These roles often come with competitive salaries and opportunities for professional growth and advancement.

Sure this course will provide you with the essential support and preparation needed. You'll gain proficiency in core data science concepts and hands-on projects. Upon completion, you'll be well-prepared to pursue diverse career opportunities in data science, such as data scientist, machine learning engineer, data analyst, or research scientist. These roles are in high demand across various industries and offer competitive salaries, making this course a valuable step towards achieving your career goals in data science.

While a degree in a related field (such as computer science, mathematics, or statistics) can be beneficial, many data scientists come from diverse educational backgrounds. Practical skills and experience often matter more than formal education.
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