Aadhrose Infotech

Artificial Intelligence and Machine Learning Essentials

Course Descriptions

This AI and Machine Learning course offers a structured, hands-on introduction to the core principles and tools used to build intelligent systems. Starting from foundational concepts, you’ll explore the differences between AI, machine learning, and deep learning, along with real-world applications across industries. The curriculum includes essential math and statistics—such as linear algebra, calculus, probability, and hypothesis testing—ensuring a solid theoretical base.

Through practical exercises, learners will gain proficiency in Python programming and key libraries like NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch. The course covers supervised and unsupervised learning techniques, deep learning architectures (CNNs, RNNs, LSTMs), and NLP with modern models such as BERT. You’ll also work with big data tools (Hadoop, Spark), model deployment techniques, and cloud platforms like AWS and Azure.

Capstone projects—including image classification, sentiment analysis, and predictive maintenance—allow learners to apply their skills in realistic scenarios. The course concludes with modules on AI ethics, model fairness, and soft skills like communication, resume building, and interview preparation, helping you become job-ready for AI/ML roles across industries.

Requirements for The Course

Educational Background :

Bachelor’s Degree in:

BE / B.Tech (from any branch)
BBA / B.Com / BSc (in any branch)

Master’s Degree in:

M.Tech / MBA / MCA / M.Sc. / M.Com (in any Branch)

AI & ML tools :

TensorFlow / PyTorch / Scikit-learn / Keras / XGBoost / CatBoost / OpenCV Hugging Face Transformers / Fast.ai / Apache MXNet / MLflow / DVC / Airflow / Kubeflow

Artificial Intelligence and Machine Learning

Foundations of AI and ML

Introduction to Artificial Intelligence and Machine Learning
Differences between AI, ML, and Deep Learning
Applications of AI and ML in Industry

Mathematics and Statistics for AI/ML

Linear Algebra
- Vectors and Matrices
- Matrix Operations
- Eigenvalues and Eigenvectors
Calculus
- Derivatives and Integrals
- Partial Derivatives
- Gradient and Optimization
Probability and Statistics
- Basic Probability Theory
- Probability Distributions (Normal, Binomial, Poisson)
- Hypothesis Testing
- Bayesian Inference

Programming and Implementation Skills

Python Programming
- Syntax and Control Structures
- Functions, Lambda Expressions
- Object-Oriented Programming
Libraries and Frameworks
- NumPy: Numerical operations
- Pandas: Data manipulation and cleaning
- Scikit-learn: ML algorithms and model evaluation
- TensorFlow & Keras: Deep learning models
- PyTorch: Neural networks and deep learning
Data Handling and Preprocessing
- Data Cleaning and Transformation
- Handling Missing Values and Outliers
- Data Encoding and Normalization
- Feature Engineering and Extraction

Machine Learning Algorithms

Supervised Learning
- Regression Techniques:
  - Linear Regression
  - Polynomial Regression
  - Ridge and Lasso Regression
- Classification Algorithms:
  - Logistic Regression
  - Decision Trees and Random Forests
  - Support Vector Machines (SVM)
  - Naive Bayes
  - k-Nearest Neighbors (KNN)
Unsupervised Learning
- Clustering Techniques:
  - K-Means Clustering
  - Hierarchical Clustering
  - DBSCAN
- Dimensionality Reduction:
  - Principal Component Analysis (PCA)
  - t-SNE
Reinforcement Learning
- Markov Decision Processes (MDP)
- Q-Learning
- Deep Q-Networks (DQNs)
- Policy Gradient Methods

Deep Learning and Neural Networks

Text Preprocessing
- Tokenization, Lemmatization, Stemming
Text Representation
- Bag of Words (BoW)
- TF-IDF
- Word Embeddings (Word2Vec, GloVe)
Deep Learning for NLP
- Transformers (BERT, GPT)
- Sequence-to-Sequence Models (Seq2Seq)
- Attention Mechanisms
Applications
- Sentiment Analysis
- Named Entity Recognition (NER)
- Machine Translation
- Text Summarization

Natural Language Processing (NLP)

Text Preprocessing
- Tokenization
- Lemmatization
- Stemming
Text Representation
- Bag of Words (BoW)
- TF-IDF (Term Frequency-Inverse Document Frequency)
- Word Embeddings
  - Word2Vec
  - GloVe
Deep Learning for NLP
- Transformers (BERT, GPT)
- Sequence-to-Sequence Models (Seq2Seq)
- Attention Mechanisms
Applications
- Sentiment Analysis
- Named Entity Recognition (NER)
- Machine Translation
- Text Summarization

Model Evaluation and Optimization

Model Performance Metrics
- Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC
- Regression: MSE, RMSE, MAE, R²
Cross-Validation Techniques
- K-Fold Cross-Validation
- Leave-One-Out Cross-Validation
Hyperparameter Tuning
- Grid Search
- Random Search
- Bayesian Optimization

AI and ML System Design

Data Pipeline and Workflow Management
MLOps and CI/CD for ML Models
Model Versioning and Experiment Tracking (MLflow)
Model Deployment:
- RESTful APIs using Flask or FastAPI
- Model Serving with TensorFlow Serving or TorchServe
- Containerization using Docker and Kubernetes

Big Data and Distributed Computing

Distributed Data Processing:
- Hadoop and MapReduce
- Apache Spark for Big Data Analytics
Data Storage Solutions:
- SQL and NoSQL Databases
- Data Lakes and Data Warehousing

Cloud Platforms and Deployment

Cloud AI and ML Services:
- AWS SageMaker
- Google AI Platform
- Azure Machine Learning
Model Deployment on Cloud
Edge AI and On-Device Learning

AI Ethics and Fairness

Bias and Fairness in Machine Learning Models
Explainable AI and Accountability
Data Privacy and GDPR Compliance

Projects

Project 1: Image Classification with CNNs (Keras)
Project 2: Sentiment Analysis with Transformers (BERT)
Project 3: Predictive Maintenance with Time Series Analysis
Project 4: Fraud Detection with Anomaly Detection Techniques

Soft Skills and Interview Preparation

Problem-Solving and Critical Thinking
Communication Skills for Technical and Non-Technical Audiences
Resume Building and Portfolio Creation
Mock Interviews and Coding Challenges
Building a GitHub Portfolio with Real-World Projects

Course includes:

Level:
Expert
Lessons:
18
Mode:
Online / Offline
Certifications:
Yes
Language:
English & Tamil

Online Fees: ₹‎ 55K

Offline Fees: ₹‎ 65K

Join This course

K.M.IQBAL

Machine Learning Engineer | AI Consultant

K.M.IQBAL is a senior data scientist with expertise in machine learning, AI, and analytics. She has led projects across finance, healthcare, and retail, and specializes in teaching real-world data skills with a focus on job readiness.

Experience: 10 Years

188 Students

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