AI and Generative AI course

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100% practical sessions
Latest syllabus and Case studies
20+ AI tools
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AI and Generative AI course

6 Months

Course Duration

9 Modules

Course Modules

Offline/Online

Mode

Overview

AI is shaping the future across all industries, making it one of the most in-demand skill sets. The importance of this course is evident in today’s landscape, which is largely driven by AI and its transformative applications.

Tools Covered

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Course Curriculum

→ Variables and Data Types
→ Data Structures
→ Conditional and Looping Statements
→ Functions
→ NumPy Arrays and Functions
→ Creating, Accessing, and Modifying NumPy Arrays
→ Saving and Loading NumPy Arrays
→ Creating, Accessing, and Modifying Pandas Series
→ Creating, Accessing, and Modifying Pandas Series
→ Creating, Accessing, Modifying, and Combining DataFrames
→ Pandas Functions
→ Various Data Manipulation Functions in Pandas
→ Saving and Loading Datasets Using Pandas
→ Saving and Loading Datasets Using Pandas
→ Loading Datasets into DataFrames
→ Data Overview
→ General Statistics of the Dataset (e.g. describe(), info())
→ Univariate Analysis (Histogram, Boxplots, and Bar Graphs)
→ Bivariate/Multivariate Analysis (Line Plot, Scatterplot, Lmplot, Jointplot, Violin Plot, Stripplot, Swarmplot, Catplot, Pairplot, Heatmap)
→ Customization of Plots
→ Missing Value Treatment
→ Outlier Detection and Treatment
→ Text Processing
→ Stopword Removal
→ Stemming
→ Removing Special Characters and Whitespace
→ Text Vectorization (Bag of Words, N-grams)

→ Introduction to Learning from Data
→ Types of Machine Learning
→ Business Problem and Solution Space
→ Regression, Correlation, and Linear Relationshipsv
→ Simple and Multiple Linear Regression
→ Categorical Variables in Linear Regression
→ Regression Metrics
→ Business Problem and Solution Space - Classification
→ Introduction to Decision Trees
→ Impurity Measures and Splitting Criteria
→ Classification Metrics
→ Pruning
→ Decision Trees for Regression
→ Business Problem and Solution Space - Clustering
→ Distance Metrics
→ Introduction to Clustering
→ Types of Clustering
→ K-Means Clustering
→ t-SNE for Visualizing High-Dimensional Data

→ Introduction to Ensemble Techniques
→ Introduction to Bagging
→ Sampling with Replacement
→ Introduction to Random Forest
→ Introduction to Boosting
→ Boosting Algorithms (Adaboost, Gradient Boost, XGBoost)
→ Stacking
→ Feature Engineering
→ Cross-validation
→ Oversampling and Undersampling
→ Model Tuning and Performance
→ Hyperparameter Tuning
→ Grid Search
→ Random Search
→ Regularization

→ Deep Learning and History
→ Multi-Layer Perceptron
→ Types of Activation Functions
→ Training a Neural Network
→ Backpropagation
→ Optimizers and Their Types
→ Weight Initialization and Its Techniques
→ Regularization and Its Techniques
→ Types of Neural Networks

→ Introduction to NLP
→ History of NLP
→ Sentiment Analysis
→ Introduction to Word Embeddings
→ Word2Vec
→ GloVe
→ Semantic Search
→ Introduction to Transformers
→ Components of a Transformer
→ Different Transformer Architectures
→ Applications of Transformers
→ Introduction to LLMs
→ Working of LLMs
→ Applications of LLMs
→ Introduction to Prompt Engineering
→ Strategies for Devising Prompts
→ Introduction to Embeddings and Tokenization
→ Byte-Pair Encoding (BPE) Tokenization
→ Computation and Application of Sentence Embeddings
→ Retrieval-Augmented Generation (RAG)

→ Overview of Computer Vision
→ Color Pixel and Image Representation
→ Edge Detection
→ Kernels
→ Padding, Strides, and Pooling
→ Flattening to a 1D Array
→ ANN vs CNN
→ CNN Architecture
→ Introduction to Transfer Learning
→ Common CNN Architectures

→ Introduction to Model Deployment
→ Serialization
→ Deployment Using Streamlit
→ Introduction to Containerization
→ Docker
→ Deployment Using Flask

→ Introduction to Databases and SQL
→ Fetching Data
→ Filtering Data
→ Aggregating Data
→ In-built Functions (Numeric, Datetime, Strings)
→ Joins
→ Window Functions
→ Subqueries
→ Order of Query Execution

→ Experiments, Events, and Definition of Probability
→ Introduction to Inferential Statistics
→ Introduction to Probability Distributions (Random Variable, Discrete and Continuous Random Variables, Probability Distributions)
→ Binomial Distribution
→ Normal Distribution
→ Sampling
→ Central Limit Theorem
→ Estimation to Hypothesis Testing
→ Hypothesis Formulation and Performing a Hypothesis Test
→ One-tailed and Two-tailed Tests
→ Confidence Intervals and Hypothesis Testing
→ Test for One Mean
→ Test for Equality of Means
→ Chi-square Test of Independence
→ One-way ANOVA

Certificate

Secure your career with an industry-recognized certificate that validates your expertise in Generative AI, you will also be certified by Microsoft which will further boost your credibility, enhance your resume, and open the doors to limitless career opportunities

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Professionals at Your Service

Vikas Singh
Cyber Security expert

Vivek Singh has exposure to many Indian tech companies like HCL, Tech-Mahendra, and many more, He has handled many Network security and cybersecurity projects.

Ravi Singh
Performance Marketer

Ravi has got more than 5 years of experience in Performance marketing he has handled more than 50cr budget for various realestate and education brands.

Amar P
Sr. Performance Marketer

Having more than 14 years of experience in Digital marketing, have managed agencies and have handled domestic and international brands with budget capacity as high as 100 million US dollars.

Ankush P
AI engineer

Ankush is an AI engineer, IIIT graduate, and GATE-qualified professional currently working in an AI firm. he has expertise in Lang Chain, Llama Index framework, and agentic.

Divya Bansal
Research Analyst

Divya has extensive work experience of more than 8 years in the research industries like IPSOS, AC Nielsen, and many more, She has handled research work for many organizations.

A Vasisht
Senior Data Scientist

A. Vasisht is a seasoned Senior Data Scientist with over 10 years of experience, having worked with leading organizations such as Qatar Development Bank, Ernst & Young, Optum, and several other multinational firms.

FAQ’s

This course is designed for aspiring entrepreneurs, small business owners, and Students looking to start or scale their ventures.

No, the course is beginner-friendly and covers everything from idea generation to scaling a business, making it suitable for individuals with no prior experience.

You’ll learn how to shortlist ideas, discover your passion, find co-founders, split equity, raise funds, create an MVP, set product pricing, acquire customers, and manage ROI.

The course is highly practical, with actionable insights, real-world examples, and strategies that you can immediately apply to your business.

Yes, you will receive a recognized certificate upon successfully completing the course, which can enhance your credibility further.

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