Artificial Intelligence
Basic to Advanced
Course Syllabus
Artificial intelligence (AI) is expected to transform industries and redefine what's possible in business and society. According to a PWC article, AI could contribute $15.7 trillion to the global economy by 2035. As AI capabilities continue to advance, there is a growing demand for people who can design and deploy AI systems. According to Resume Builder, 96% of recruiters in 2024 prioritize candidates with AI skills.
AI Provides Brighter Career Opportunity. Studying artificial intelligence gives you versatile skill sets and opens the door to several career opportunities.
Tools You'll Learn
Essential Math & Statistics
MS Power BI
R Language
Machine Learning
Advanced Excel
MySQL
Deep Learning
Artificial Intelligence
Python
Tableau
Git & GitHub
Placement Training
Our Roadmap
Mathematics & Statistics
A significant portion of your ability to translate your Machine Learning skills into real-world scenarios depends on your success and understanding of mathematics. Machine Learning careers require mathematical and stattistical study because algorithms, and performing analysis and discovering insights from data require math and stats.
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Linear Algebra
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Vectors and vector operations
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Matrices and matrix operations
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Linear transformations
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Eigenvalues and eigenvectors
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Singular Value Decomposition (SVD)
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Calculus
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Limits and Continuity
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Derivatives and Differentiation
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Partial Derivatives and Gradients
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Integrals and Integration
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Optimization Techniques
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Gradient Descent, Newton's Method
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Probability Theory
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Random variables
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Probability Distributions
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Bayes' theorem and Bayesian Inference
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Central Limit Theorem
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Conditional probability and independence
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Sampling and Monte Carlo methods
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Complexity Analysis
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Big O Notation
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Time and Space Complexity
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Amortized Analysis
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Search Algorithms
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Breadth-First Search
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Depth-First Search
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A* Search
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Branch and Bound
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Sorting Algorithms & Data Structures
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Statistics & Descriptive statistics
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Measures of Central Tendancy
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Inferential statistics
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Hypothesis testing, Confidence intervals
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Correlation and covariance
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Regression analysis
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Linear, Logistic
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Other regression techniques
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Bias-variance tradeoff and regularization
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Entropy and Information Gain
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Mutual Information
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Cross-entropy and KL divergence
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Decision trees and information gain
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Gradient descent and variants
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Stochastic, Mini-Batch, Momentum
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Constrained optimization
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Convex optimization
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Quasi-Newton methods
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Genetic algorithms
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Evolutionary optimization
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Overfitting and Regularization
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L1, L2, Dropout
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Early Stopping
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Cross-Validation Techniques
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K-Fold, Stratified, Leave-One-Out
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Nested Cross-Validation
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Model Selection
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Evaluation Metrics
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Accuracy, Precision, Recall
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F1-Score, ROC-AUC, Log Loss
Advanced Excel
(8+ Live Projects)
Excel is a spreadsheet application that can be used for data analytics. Data analysts use Excel to analyze large amounts of data quickly and easily. With its wide range of charting and graphing options, Excel can help users to present data in a way that is easy to understand. Charts and graphs are essential tools for data analysis, as they allow users to quickly identify patterns and trends in data. What you'll learn here:
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Excel Interface
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Data Formatting Techniques
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Data Cleaning Techniques
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Data Study Techniques
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Conditional Formatting
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Data Validation
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Calculations in Excel
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Operators in Excel
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Different Types of Operators
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Mathematical & Statistical Calculations
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Financial Calculations
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Basic Inbuilt Excel Functions (SUM, MIN, MAX, COUNT etc.)
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Advanced Functions (VLOOKUP, SUMIFS, COUNTIS etc.)
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Macros, VBA, Power Pivot & Power Query
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Analytics Using Functions
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Pivot Tables
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Analytics Using Pivot Tables
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Data Visualization in Excel
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Applying Charts & Graphs
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Dashboard & Report Building
Python
(5+ Live Projects)
Python has become a popular programming language for machine learning and data science due to its simplicity, versatility, and a wide range of libraries and frameworks specifically designed for data manipulation, exploration, and visualization. It's easy to learn, widly used & versatile. It also has wide range of libraries and frameworks. What you'll learn here:
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Introduction to Python
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Environment Setup
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Installing Anaconda
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Working with Jupyter Notebooks & Lab
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Python Basics
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Syntax
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Variables
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Data Types
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Type Casting
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Keywords & Identifiers
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Operators
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Types of Operators
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Mathematical Calculations
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Data Structures in Python
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Int and Float
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Complex Numbers
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Boolean
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Strings
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String Methods
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Lists
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Multidimensional Lists
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List Methods
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Tuples
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Tuple Methods
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Sets and Frozen Sets
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Set Methods
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Dictionary & Methods
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Comprehensions
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Functions
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Modules
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Libraries
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Importing Libraries
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Complete OOP
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Data Analytics in Python
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Introduction to NumPy
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NumPy Methods
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NumPy Data Types
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NumPy Calculations
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Data Manipulation with NumPy
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Introduction to Pandas
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Pandas Data Structures
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Working with DataFrames
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Importing Data File Types
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Data Manipulations
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Data Cleaning
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Data Wrangling
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Generating Insights
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Exporting Data
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Data Visualization with Matplotlib
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Data Visualization with Seaborn
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Working with Different Chart Types
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Effective Data Visualization
R Language
(5+ Live Projects)
R is a programming language used for statistical computing and graphics. It's used by data scientists and business leaders in many fields, including academics and business. R is used for data analysis, statistical modeling, and handling, storing, and analyzing data. What you'll learn here:
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Introduction to R Language
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Introduction to RStudio
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Basic syntax and data types in R
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R packages and their importance
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Introduction to R Markdown
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Data Structures in R
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Vectors, matrices, and arrays
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Factors and data frames
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Lists and their applications
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Introduction to the dplyr package
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Data Manipulation with dplyr
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Selecting and filtering data
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Mutating and summarizing data
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Grouping and aggregating data
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Working with missing data
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Introduction to Data Visualization
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Data Visualization with ggplot2
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Basic plotting with ggplot2
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Customizing plots
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Working with colors, labels, and themes
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Visualizing distributions and relationships
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Advanced plotting with ggplot2
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Faceting and layering plots
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Creating complex visualizations
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Working with heatmaps, boxplots, & more
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Exporting plots for publication
Machine Learning
(10+ Live Projects)
Data is meaningless until it's converted into valuable information. Machine learning can be used as the key to unlock the value of corporate and customer data and enact decisions that keep a company ahead of the competition. Machine learning and Data Science are hence two sides of a coin without which Data Science operations are unachievable. Data Scientists must grasp Machine Learning knowledge for accurate forecasts and estimates. What you'll learn here:
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Introduction to Machine Learning
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Essential Maths for Machine Learning
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Essential Statistics for Machine Learning
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Exploratory Data Analysis (EDA)
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Hypothesis Testing
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t-tests and chi-square tests
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Using Python for Machine Learning
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Important Python Libraries for ML
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Scikit-learn
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Data preparation and preprocessing
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Train-test split and cross-validation
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Supervised learning algorithms
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Linear models
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Tree-based models
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SVMs
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Unsupervised learning algorithms
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Clustering
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Dimensionality Reduction
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Model evaluation and metrics
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Feature engineering
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Categorical encoding
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Feature scaling
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Normalization
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Feature selection techniques
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Introduction to neural networks
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TensorFlow and Keras
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Building and training neural networks
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Convolutional Neural Networks (CNNs)
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Recurrent Neural Networks (RNNs)
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Transfer learning
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Fine-Tuning
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PyTorch
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Tensors
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Tensor Operations
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Custom layers
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Loss functions
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Natural Language Processing (NLP)
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Text preprocessing and vectorization
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Bag-of-Words and TF-IDF
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Word embeddings
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Word2Vec, GloVe
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Sequence models
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RNNs, LSTMs, GRUs
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Transformer models
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BERT, GPT
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NLP libraries
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NLTK, spaCy, Hugging Face
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Computer Vision
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Image processing
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Manipulation with OpenCV
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CNN for image classification
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Object detection and segmentation
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Transfer learning for CV tasks
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Data augmentation techniques
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Ensemble methods and stacking
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Imbalanced data
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Class imbalance techniques
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Interpretability and Explainable AI
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Federated learning
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Privacy-preserving ML
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Reinforcement learning
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Decision-Making
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Machine Learning project lifecycle
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Data pipelines
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Machine Learning Pipelines
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Workflow management
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Model versioning
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Reproducibility
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Deployment strategies
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Batch, Online, APIs
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Containerization
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Cloud deployment
Deep Learning
(10+ Live Projects)
Deep learning is a machine learning technique that uses artificial neural networks to process unstructured data, such as text and images, without manual feature extraction. It's considered the fastest-growing field in machine learning and is used by many companies to create new business models. Deep learning eliminates some of data pre-processing that is typically involved with machine learning. These algorithms can ingest and process unstructured data, like text and images, and it automates feature extraction, removing some of the dependency on human experts.
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Introduction to Neural Networks
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Artificial Neural Networks (ANNs)
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Perceptrons
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Activation Functions
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Sigmoid, ReLU, Leaky ReLU, Tanh, Swish
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Loss Functions
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Mean Squared Error, Cross-Entropy
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Focal Loss, Huber Loss
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Optimization Algorithms
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Gradient Descent, Momentum
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Adam, RMSProp, Adagrad, Adadelta
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Feedforward Neural Networks
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Multilayer Perceptrons (MLPs)
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Backpropagation Algorithm
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Regularization Techniques
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L1, L2, Dropout, Early Stopping
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Data Augmentation
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Weight Initialization Techniques
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Xavier, He, LeCun
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Batch Normalization
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Model Architectures
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ResNet, DenseNet, EfficientNet
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Convolutional Neural Networks (CNNs)
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Convolution and Pooling Operations
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CNN Architectures
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LeNet, AlexNet, VGGNet, ResNet
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DenseNet, EfficientNet
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MobileNets, ShuffleNets
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Transfer Learning and Fine-tuning
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Applications in Computer Vision
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Image Classification, Object Detection
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Segmentation, Pose Estimation
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Attention Mechanisms
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Squeeze-and-Excitation Networks
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Non-Local Networks
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Recurrent Neural Networks (RNNs)
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Simple RNNs
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Vanishing/Exploding Gradient Problem
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Long Short-Term Memory (LSTM)
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Gated Recurrent Units (GRU)
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Bidirectional RNNs
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Sequence-to-Sequence Models
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Encoder-Decoder, Attention Mechanisms
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Applications in NLP
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Language Modeling, Machine Translation
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Text Generation, Sentiment Analysis
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Generative Models
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Generative vs. Discriminative Models
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Applications of GenAI
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Autoencoders and VAEs
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Generative Adversarial Networks (GANs)
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DCGAN, CycleGAN, Pix2Pix, StyleGAN
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Diffusion Models (DDPM, DDIM)
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Autoregressive Models (PixelRNN, PixelCNN)
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Applications in Image Generation
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Style Transfer
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Deep Learning Best Practices
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Transfer Learning and Fine-tuning
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Model Compression and Quantization
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Pruning, Knowledge Distillation,
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Quantization-Aware Training
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Distributed Training
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Data Parallelism, Model Parallelism,
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Gradient Checkpointing
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Deployment and Serving
TensorFlow Serving, TorchServe, ONNX
Artificial Intelligence
(6+ Live Projects)
Artificial intelligence (AI) is a rapidly growing field that is transforming the way people live, work, and communicate. AI is used in many industries, including robotics, automation, healthcare, logistics, and manufacturing. As industries adopt AI to make better decisions and streamline operations, the demand for AI specialists will likely increase. Learning AI can help future-proof your career and make you an attractive candidate for developing, managing, and planning AI solutions.
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Natural Language Processing (NLP)
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Text Preprocessing
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Feature Extraction
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Word Embeddings, TF-IDF, Subword Tokenization
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Language Models
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N-gram, RNN, Transformer
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Text Generation
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Sentiment Analysis
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Emotion Detection
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Named Entity Recognition (NER)
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Relation Extraction
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Machine Translation and Chatbots
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Transformer Models
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BERT, GPT, T5, XLNet, RoBERTa
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Contextual Embeddings
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Transfer Learning in NLP
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Computer Vision
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Image Preprocessing and Augmentation
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Geometric Transformations
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Brightness/Contrast Adjustments
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Mixup, CutMix
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Object Detection
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YOLO, Faster R-CNN, SSD, RetinaNet
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Instance Segmentation
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Mask R-CNN, U-Net, DeepLab
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Facial Recognition and Analysis
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FaceNet, ArcFace, VGGFace2
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Video Analysis and Action Recognition
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I3D, SlowFast Networks, Temporal Shift Module
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Generative Models for Image Synthesis
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StyleGAN, DALL-E, Imagen, Stable Diffusion
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Reinforcement Learning
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Markov Decision Processes (MDPs)
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Q-Learning and Deep Q-Networks (DQN)
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Policy Gradients
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REINFORCE, PPO, A2C/A3C
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Actor-Critic Methods
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A2C, A3C, DDPG, TD3, SAC
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Exploration Strategies
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ε-greedy, Boltzmann, Noisy Nets
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Autonomous Systems
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Ethical AI and Responsible Development
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Fairness, Accountability
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Transparency in AI
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Bias Detection and Mitigation
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AI Safety and Security
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Adversarial Attacks and Defenses
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Privacy-Preserving AI
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Differential Privacy, Federated Learning
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Societal Impacts and Governance of AI
Power BI
(10+ Live Projects)
Power BI is a business intelligence service from Microsoft that helps businesses and individuals transform raw data into insights. It is a group of BI (business intelligence) services and products that converts data from various sources into reports, visualizations, and interactive dashboards. Power BI is a valuable tool for illustrating what’s happening within an organization in the present. It also has applications for helping to anticipate what may transpire in the future. What you'll learn here:
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Introduction to Business Intelligence
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Different BI Tools
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Introduction to Power BI
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Installing Power BI Desktop
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Working with Interface
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Understanding 4 Views
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Report, Table, Model & Query View
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Importing Different Data Files
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Types of Import
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Introduction to Big Data
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Working with Canvas
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Working with Charts
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Choosing Correct Chart Types
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Formatting Charts
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Types of Formatting
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Importing Visuals
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Applying Themes
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Building Dashboards & Reports
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Data Tranformation
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Data Cleaning
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Introduction to Power Query
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Power Query Editor
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Importing Data in Power Query Editor
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Bulk Import Data
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Applying Data Cleaning Techniques
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Introduction to Data Modeling
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Working with Relationships
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Cardinality & Direction
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Types of Tables
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Schemas & Types of Schemas
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Creating Schemas
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Schema Conversion
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Introduction to Functional Programming
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Introduction to DAX
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Syntax
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Data Types
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Operators & Types of Operators
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Keywords & Identifiers
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Inbuilt Functions
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Calculation Types
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Creating Calculated Columns
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Creating Calculated Measures
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Creating Calculated Tables
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Contexts
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Types of Contexts
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Variables and Returns
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Scope of Variables & Calculations
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Comments
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Working with Inbuilt Functions
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Types of Inbuilt Functions
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Analyzing Data with DAX
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Introduction to Power BI Management
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Workspaces
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Publishing Dashboards & Reports
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Introduction to Data Pipelines and Pipelining
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Introduction to Data Mining
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Understanding ETL Process
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Creating Data Flow
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Working with Semantic Model
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Connecting Data Flow to Semantic Model
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Creating Real Time and Automated Reports
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Building End to End Data Pipeline
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Using SQL Queries in Power BI
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Using Python in Power BI
MySQL
(5+ Live Projects)
MySQL is a relational database management system (RDBMS) that's used as an interface for SQL. SQL is a structured query language (SQL) that allows you to query, define, manipulate, control, and analyze data in a database. SQL databases are essential for data analysts who work on data architecture and storage systems. MySQL is one of the most sought-after skills in any data-related job, and can boost your job prospects by over 40%. What you'll learn here:
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Introduction to DBMS & RDBMS
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Introduction to Structured Query Language
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Installing MySQL
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Working with MySQL Workbench
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Understanding Databases
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Database Designing
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Keys and Types of Keys
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Relationships and Normalization
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MySQL Query Basics
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MySQL Data Types
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MySQL Functions
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DDL(Data Definition Language)
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DML(Data Manipulation Language)
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DQL(Data Query Language)
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TCL(Transaction Control Language)
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DCL(Data Control Language)
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MySQL SELECT
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MySQL WHERE
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MySQL AND, OR, NOT
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MySQL ORDER BY
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MySQL INSERT INTO
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MySQL NULL Values
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MySQL UPDATE
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MySQL DELETE
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MySQL LIMIT
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MySQL MIN and MAX
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MySQL COUNT, AVG, SUM
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MySQL LIKE
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MySQL Wildcards
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MySQL IN
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MySQL BETWEEN
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MySQL Aliases
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MySQL Joins
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Sub-Queries & CTE
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MySQL INNER JOIN
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MySQL LEFT JOIN
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MySQL RIGHT JOIN
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MySQL CROSS JOIN
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MySQL Self Join
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MySQL UNION
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MySQL GROUP BY
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MySQL HAVING
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MySQL EXISTS
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MySQL ANY, ALL
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MySQL INSERT SELECT
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MySQL CASE WHEN
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MySQL Null Functions
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MySQL Comments
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MySQL Operators
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MySQL Create DB
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MySQL Drop DB
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MySQL Create Table
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MySQL Drop Table
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MySQL Alter Table
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MySQL Constraints
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MySQL Not Null
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MySQL Unique
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MySQL Primary Key
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MySQL Foreign Key
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MySQL Check
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MySQL Default
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MySQL Create Index
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MySQL Auto Increment
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MySQL Dates
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MySQL Views
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Window Functions
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MySQL Triggers
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MySQL Stored Procedures
Tableau
(5+ Live Projects)
Tableau is a business intelligence tool that helps organizations analyze and process large amounts of data. Tableau is an end-to-end data analytics platform that allows you to prep, analyze, collaborate, and share your big data insights. Tableau excels in self-service visual analysis, allowing people to ask new questions of governed big data and easily share those insights across the organization. What you'll learn here:
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Installing Tableau
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Tableau Fundamentals
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Tableau Desktop
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Tableau Server
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Tableau Online
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Tableau Reader
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Tableau Public
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Connecting With Data
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Creating Views and Analysis
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Dashboard Designs
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Creating Reports
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Data Cleaning
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LOD Expressions
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Creating Parameters
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Calculated Fields
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Generating Data Stories
Looker Studio
(3+ Live Projects)
With Looker Studio, you can easily report on data from a wide variety of sources, without programing. In just a few moments, you can connect to data sets such as: Databases, including BigQuery, MySQL, and PostgreSQL. Google Marketing Platform products, including Google Ads, Analytics, Display 360, Search Ads etc. What you'll learn here:
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Introduction to Looker
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Interface
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Getting Data
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Charts & Graphs
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Grouping & Categorizing Data
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Data Blending
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Filters & Controls
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Parameters
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Sharing, Tracking & Management
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BigQuery
Business Finance
(3+ Live Projects)
Finance in data analytics is a field that applies data analysis techniques to financial data to support decision making, optimize performance, and prevent fraud. Learning finance in data analytics can help you gain valuable skills such as financial data analysis. By learning finance in data analytics, you can enhance your career prospects and opportunities in the finance industry, as well as other industries that rely on financial data. You can also improve your financial literacy and decision making for your personal or professional goals. What you'll learn here:
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Introduction to Finance
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Getting Familier with Financial Terms
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Revenue, Expense, Profit
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Gross Revenue, Gross Profit, Net Profit
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EBIT and EBITDA
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MTD, QTD, YTD
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Plan, Budget, Forecast, LE
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General Ledger
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Cash Flow and Income Statement
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Balance Sheet
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Profit and Loss Statement
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Variance
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Favorable and Unfavorable Variance
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YoY, vs LY, vs Bud
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ROI, ROE, ROA, EPS
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P/E Ratio
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NPV, IRR
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AAGR & CAGR
Google Workspace
(3+ Live Projects)
Google Sheets is a powerful tool for data analytics, offering accessibility, collaboration, cost-effectiveness, and basic data analysis capabilities. Learning it enables efficient data manipulation, visualization, and sharing, making it an essential skill for anyone handling data analysis tasks. Google Sheets provides a user-friendly interface familiar to many, facilitating a smooth learning curve. Its integration with other Google Workspace apps enhances productivity, while its cloud-based nature ensures data accessibility from anywhere. What you'll learn here:
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Getting started with Google Sheets
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Google Sheets Interface
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Data Formatting
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Data Manipulation
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Data Cleaning
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Formulas & Functions
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Basic Functions (SUM, AVERAGE, COUNT etc.)
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Advanced Functions (VLOOKUP, IF, INDEX etc.)
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Pivot Tables
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Introduction to Google Apps
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Exploring Data Collection Tools
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Presentation with Google Slides
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Documentation with Google Doc
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Collaboration
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Sharing
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Google Drive
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Google Groups
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Practice and Projects
Placement Training &
Interpersonal Skills
Interpersonal skills, also called soft skills or people skills, are important for data analysts because they help with many aspects of their work. Effective communication is essential for data analysts to effectively convey their findings, recommendations, and insights to their audiences. Also some companies do conduct aptitude tests for data analytics jobs. To prepare for such a test, you can focus on improving your problem-solving and critical thinking skills. If you're a data analyst searching for your next career opportunity, one of the most critical components of your job search is your resume or CV. A data analyst resume is your first chance to make an impression to potential employers, and it needs to stand out from the rest to land an interview. What you'll learn here:
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Effective Communication
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Reasoning & Aptitude
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HR Prep
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Techincal Interview Prep
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Presentation Skills
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Personality Development
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GitHub & LinkedIn Profiles
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Mock Interview Sessions
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Resume Making
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Applying on Job Boards
Job Profiles
You Can Target
Job Profiles
With Average Salary for Freshers
AI Engineer
Average Salary
16-32 LPA
ML Engineer
Average Salary
12-16 LPA
DL Engineer
Average Salary
14-24 LPA
CV Engineer
Average Salary
7-16 LPA
NLP Engineer
Average Salary
10-20 LPA
AI Architect
Average Salary
12-20 LPA
AIOps
Average Salary
13-26 LPA
Research Eng.
Average Salary
8-14 LPA
AI Consultant
Average Salary
7-13 LPA
Our Approach Towards Teaching
From Basics to Advanced
Learn With Ease
Embark on your learning journey from the basics with industry experts. Starting with familiar tools like Excel and fundamental math concepts
Take on Challenges
Learning technology is like learning swimming - you can truly learn it only when you practice. After each subject, dive into challenging projects that let you apply what you've learned.
Active Engagement
Stay connected through dedicated WhatsApp groups where you can post doubts—our vibrant community of students and faculty is there to provide quick solutions
Job-Ready Training by IITians
Prepare for success with our placement training led by industry experts. Dive into resume building, tackle puzzles and aptitude challenges, refine your communication skills, excel in mock interviews, engage in group discussions, and much more.