#No 1 Artificial Intelligence & Machine Learning Program
Python, Statistics, NumPy, Pandas, Data Analysis, Machine Learning, Deep Learning, TensorFlow, Keras, Computer Vision, NLP, Generative AI, LLMs, Prompt Engineering, OpenAI APIs, AI Agents, Model Deployment, Git & GitHub, Live AI Projects & Internship.
- 6 Months Training & Internship Program
- Basic to advance Training
- 3+ Projects Implementations
- Industry Specific Tools
- Unlimited Training Access
- Resume & Interview Preparation
- Internship Offer Letter
- Internship Completion Certificate
- Internship Experience Letter
Training Module
Introduction To Artificial Intelligence & Machine Learning
- Introduction to Artificial Intelligence : History and Evolution of AI, Importance of AI in Modern Technology, Applications of AI in Different Industries – Healthcare, Finance, E-commerce, Automation.
- Types of Artificial Intelligence : Narrow AI (Weak AI), General AI, Super AI, Examples of AI Systems in Real World Applications.
- AI vs Machine Learning vs Deep Learning : Understanding Relationship Between AI, Machine Learning and Deep Learning, Use Cases and Applications of Each Technology.
Python Programming for AI
- Introduction to Python : Introduction to Programming Languages, Python Installation and IDE Setup, Python Syntax and Indentation Rules, Writing First Python Program
- Variables and Data Types : Python Variables, Primitive Data Types – int, float, string, Boolean. Type Conversion and Type Casting
- Operators and Expressions : Arithmetic Operators, Relational Operators, Logical Operators, Assignment Operators
- Control Statements :Conditional Statements – If, If Else, Nested If, Looping Statements – For Loop, While Loop, Break and Continue Statements
- Functions and Modules : Defining Functions, Function Parameters and Return Values, Lambda Functions, Python Modules and Packages
- Exception Handling : Errors vs Exceptions, Try, Except, Finally Blocks, Custom Exceptions
Python for Data Science
- Introduction to NumPy : NumPy Arrays, Array Creation Methods, Array Indexing and Slicing, Mathematical Operations on Arrays, Broadcasting Concepts
- Introduction to Pandas : Pandas Series and DataFrames, Data Import and Export, Data Cleaning Techniques, Handling Missing Values, Data Aggregation and GroupBy Operations, Data Filtering and Sorting
Data Visualization
- Introduction to Data Visualization : Importance of Data Visualization, Data Visualization Best Practices, Types of Charts and Graphs
- Matplotlib : Line Charts, Bar Charts, Scatter Plots, Histograms, Plot Customization Techniques
- Seaborn : Statistical Data Visualization, Heatmaps, Distribution Plots, Pair Plots, Correlation Visualization
Mathematics for Machine Learning
- Linear Algebra : Vectors and Matrices, Matrix Operations, Matrix Multiplication, Eigenvalues and Eigenvectors Basics
- Statistics : Mean, Median, Mode, Variance, Standard Deviation, Correlation and Covariance
- Probability : Probability Basics, Probability Distributions, Conditional Probability, Bayes Theorem
Machine Learning
- Introduction to Machine Learning : Artificial Intelligence vs Machine Learning vs Deep Learning, Types of Machine Learning – Supervised, Unsupervised, Reinforcement Learning, Machine Learning Workflow
- Supervised Learning : Linear Regression, Logistic Regression, Decision Trees, Random Forest Algorithms, Support Vector Machines
- Unsupervised Learning : Clustering Concepts, K-Means Clustering, Hierarchical Clustering, Dimensionality Reduction – PCA
- Model Evaluation : Training and Testing Data, Accuracy, Precision, Recall, F1 Score, Cross Validation Techniques
Deep Learning
- Introduction to Neural Networks : Artificial Neural Networks, Activation Functions, Forward Propagation, Backpropagation Algorithm
- Deep Learning Frameworks : Introduction to TensorFlow, Introduction to PyTorch, Model Training and Optimization
- Applications of Deep Learning : Image Classification, Object Detection Basics, Pattern Recognition Systems
Natural Language Processing (NLP)
- Text Processing : Tokenization, Stop Words Removal, Stemming and Lemmatization, Text Cleaning Techniques
- Text Vectorization : Bag of Words Model, TF-IDF Vectorization
- NLP Applications : Sentiment Analysis, Text Classification, Chatbot Development Basics
Generative AI & LLMs
- Introduction to Generative AI : Generative AI Concepts, Large Language Models Overview, AI Model Capabilities and Limitations
- Prompt Engineering : Writing Effective Prompts, Prompt Optimization Techniques, Chain of Thought Prompting
- AI Application Development : Integrating AI APIs, Building AI Chat Applications, AI Automation Tools
Model Deployment
- REST API for ML Models : Introduction to APIs, Creating APIs for Machine Learning Models, Model Serialization
- Deployment Tools : FastAPI for Model Deployment, Docker Basics
- Cloud Deployment : Deploying AI Models on Cloud Platforms, Model Monitoring Basics
Industry Projects
- Machine Learning Project : Prediction Model Development using Real Dataset
- NLP Project : Sentiment Analysis Application Development
- Deep Learning Project : Image Classification System Development
- Final Capstone Project : End-to-End AI Application Development
