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Artificial Intelligence & Machine Learning Engineering Program
👉 Enroll Today & Become a Job-Ready AIML Engineer!
📞 Contact: 8208900517
Starting Soon
Starting Soon
Starting Soon
RS. 25,000 Only
This course includes
- 6 Months Training Program
- Live Online Sessions
- Basic to advance
- 3+ Projects Implementations
- Resume & Interview Preparation
- Hindi Language
- Lifetime Validity
- Chance to win Gifts from us
About Course
The Artificial Intelligence & Machine Learning Engineering Program is an advanced career-oriented course designed to prepare students for the rapidly evolving world of intelligent technologies. This program focuses on building strong foundations in Artificial Intelligence (AI) and Machine Learning (ML) while providing practical experience in developing smart systems and data-driven applications.
In this course, students will learn the core concepts of Artificial Intelligence, Machine Learning algorithms, and data analysis techniques used in modern technology solutions. The program covers topics such as programming for AI, data preprocessing, supervised and unsupervised learning, model training, and real-world AI applications. Students will also gain an understanding of how AI is transforming industries such as healthcare, finance, education, and business automation.
Course Content
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
