Artificial Intelligence And Machine Learning Online Course
Unlock the power of Artificial Intelligence and Machine Learning with our comprehensive online program. Learn how to build intelligent systems, train ML models, and apply AI solutions across industries like healthcare, finance, and e-commerce. With hands-on projects, live case studies, and practical tool training, you’ll transform data into real-world solutions. This course is designed to take you from fundamentals to advanced AI applications, helping you stand out in the fastest-growing tech field.
6 Months
.Duration
Virtual Live
Classes
100% Job
Assurance
Languages
English | Hindi
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Course Highlights
Top AI Tools & Frameworks
Master Python, TensorFlow, PyTorch & cloud deployment.
Industry-Relevant Curriculum
Stay ahead with Generative AI, speech recognition & modern ML workflows.
Career Growth Ready
Learn how to embed AI tools, APIs (like ChatGPT, Gemini, Claude), and ML models into web applications.
AI & ML Mastery
Learn algorithms, deep learning, NLP & computer vision step by step.
Master these Tools
Skills Covered
Deep Learning & Neural Networks
.
Computer Vision
.
Big Data & Cloud AI
.
MLOps & Deployment
.
Natural Language Processing (NLP)
.
Data Preprocessing & Visualization
.
Course Curriculum
The Artificial Intelligence & Machine Learning Mastery Course is designed to take you from the basics of Python and data handling to advanced topics like predictive modeling, neural networks, natural language processing, and computer vision. You’ll also explore big data technologies, cloud-based AI services, and modern MLOps practices. Through interactive projects, real-world case studies, and guided mentorship, you’ll build the expertise to solve complex problems and launch a successful career in AI & ML.
Module 1: Foundations of AI & ML
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. Introduction to AI, ML, DL & Data Science
. History of AI & Modern Applications
. Types of ML (Supervised, Unsupervised, Semi-Supervised, Reinforcement Learning)
. Differences between AI, ML, DL & Data Science
. Machine Learning Workflow & Lifecycle
. Problem Formulation & Business Understanding
. Key Math Foundations (Linear Algebra, Probability, Statistics, Calculus)
. Gradient Descent & Optimization Basics
. Overfitting, Underfitting & Bias-Variance Tradeoff
. Introduction to Model Evaluation Metrics (Accuracy, Precision, Recall, F1, AUC)
Module 2: Python for AI/ML
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. Python Basics Refresher
. Advanced Python Concepts (OOP, Generators, Decorators, Context Managers)
. NumPy for Numerical Computing
. Pandas for Data Analysis
. Data Visualization (Matplotlib, Seaborn, Plotly)
. Scikit-learn Fundamentals
. Exploratory Data Analysis (EDA)
. Handling Missing Data & Outliers
. Working with JSON, CSV, Excel & Databases
. Python for Big Data (Dask, PySpark)
Module 3: Data Preprocessing & Feature Engineering
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. Data Cleaning & Transformation
. Handling Missing Values & Outliers
. Feature Encoding (One-Hot, Label Encoding, Target Encoding, Embeddings)
. Feature Scaling (Standardization, Normalization, Robust Scaler)
. Feature Selection (Filter, Wrapper, Embedded Methods)
. Dimensionality Reduction (PCA, LDA, t-SNE, UMAP)
. Handling Imbalanced Data (SMOTE, ADASYN)
. Feature Extraction from Images, Text & Audio
. Automated Feature Engineering Tools (FeatureTools, AutoFeat)
Module 4: Core Machine Learning
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. Linear Regression & Logistic Regression
. Decision Trees & Random Forests
. Support Vector Machines (SVM)
. Naive Bayes Classifier
. k-Nearest Neighbors (k-NN)
. Clustering Techniques (K-Means, DBSCAN, Hierarchical)
. Ensemble Methods (Bagging, Boosting, Stacking)
. Model Evaluation & Cross-Validation
. Regularization (Ridge, Lasso, ElasticNet)
. Hyperparameter Tuning (GridSearchCV, RandomSearchCV, Optuna, Bayesian Optimization)
Module 5: Advanced ML Techniques
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. Gradient Boosting (XGBoost, LightGBM, CatBoost)
. Advanced Ensemble Techniques
. Semi-Supervised Learning
. Active Learning
. Self-Supervised Learning
. Anomaly Detection (Isolation Forest, One-Class SVM)
. Recommender Systems (Collaborative Filtering, Content-Based, Hybrid)
. Time Series Forecasting (ARIMA, SARIMA, Prophet)
. Advanced Optimization Algorithms (Adam, RMSProp, AdaGrad, Nadam)
Module 6: Deep Learning & Neural Networks
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. Introduction to Neural Networks
. Perceptrons & Multilayer Perceptrons (MLPs)
. Backpropagation & Gradient Descent
. Activation Functions (ReLU, Sigmoid, Tanh, GELU, Swish)
. Convolutional Neural Networks (CNNs)
. Recurrent Neural Networks (RNNs, LSTM, GRU)
. Transformers & Attention Mechanisms
. Transfer Learning & Fine-Tuning Pre-trained Models
. Regularization (Dropout, BatchNorm, LayerNorm)
. Advanced Optimizers (AdamW, Ranger, Lookahead)
Module 7: Natural Language Processing (NLP)
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. Text Preprocessing (Tokenization, Stemming, Lemmatization, Stopwords Removal)
. Bag of Words, TF-IDF, Word Embeddings
. Word2Vec, GloVe, FastText
. RNNs & LSTMs for Text
. Seq2Seq Models for Machine Translation
. Transformers for NLP (BERT, RoBERTa, DistilBERT, GPT)
. Named Entity Recognition (NER)
. Sentiment Analysis & Text Classification
. Question Answering Systems
. Prompt Engineering for LLMs
. Conversational AI & Chatbots
Module 8: Computer Vision
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. Image Preprocessing & Augmentation
. CNN Architectures (VGG, ResNet, Inception, DenseNet)
. Object Detection (YOLO, SSD, Faster R-CNN)
. Image Segmentation (U-Net, Mask R-CNN, DeepLab)
. Face Detection & Recognition
. OCR (Optical Character Recognition)
. GANs for Image Generation
. Vision Transformers (ViT, CLIP, SAM)
. Generative Vision Models (Stable Diffusion, MidJourney, DALL·E)
. Multimodal Models (Image + Text AI)
Module 9: Generative AI & LLMs
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. Generative Adversarial Networks (GANs, DCGAN, CycleGAN, StyleGAN)
. Variational Autoencoders (VAEs)
. Diffusion Models (Stable Diffusion, Imagen, DALL·E)
. Large Language Models (GPT, LLaMA, Falcon, Mistral)
. Instruction Tuning & Fine-tuning LLMs
. Parameter Efficient Fine-Tuning (LoRA, PEFT, RLHF)
. Building AI Agents with LangChain
. Vector Databases (FAISS, Pinecone, Weaviate, Milvus)
. Retrieval-Augmented Generation (RAG)
. AI for Code (Codex, Code Llama, Copilot)
Module 10: Big Data & Cloud AI
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. Introduction to Big Data Ecosystem
. Hadoop & HDFS Basics
. Apache Spark & Spark MLlib
. Data Streaming with Kafka & Flink
. TensorFlow Extended (TFX) Pipelines
. Cloud ML Platforms (AWS SageMaker, GCP Vertex AI, Azure ML)
. AutoML Tools (H2O.ai, Google AutoML, AutoKeras)
. Scaling ML Models with Distributed Computing
. Serverless AI Architectures
. Edge AI & IoT Analytics
Module 11: MLOps & Model Deployment
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. Model Packaging with Docker
. Orchestration with Kubernetes
. MLflow for Experiment Tracking
. DVC for Data Versioning
. Continuous Integration/Deployment (CI/CD) for ML
. Model Serving (FastAPI, Flask, TorchServe, TensorFlow Serving)
. Model Monitoring & Retraining
. A/B Testing & Shadow Deployments
. Edge AI Deployment (Raspberry Pi, TensorRT, ONNX Runtime)
Module 12: Responsible & Explainable AI
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. Fairness, Accountability & Transparency in AI
. Bias Detection & Mitigation Techniques
. Explainable AI (SHAP, LIME, Integrated Gradients)
. Adversarial Attacks & Defenses
. Privacy-Preserving AI (Federated Learning, Differential Privacy)
. Energy Efficiency in AI Models (Green AI)
. AI Ethics & Governance
. Legal & Regulatory Frameworks (GDPR, EU AI Act)
Module 13: Capstone Projects & Kaggle Challenges
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. End-to-End ML Project Lifecycle
. Kaggle Competition Participation
. AI for Healthcare (Disease Prediction, Medical Imaging)
. AI for Finance (Fraud Detection, Stock Prediction)
. AI for Retail & E-commerce (Recommendation Engines)
. AI for Autonomous Systems (Robotics, Self-Driving Cars)
. AI in Generative Media (Music, Art, Video)
. Collaboration & Team Projects
Module 14: Career Preparation
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. Building an AI/ML Portfolio
. Publishing Projects on GitHub
. Contributing to Open Source AI Libraries
. Resume & LinkedIn Optimization
. Preparing for AI/ML Interviews (Coding + Theory)
. AI Engineer vs Data Scientist vs MLOps Engineer Career Paths
. Freelancing & Startup Opportunities in AI
. Continuous Learning & Research in AI
About Artificial Intelligence And Machine Learning Course
Our AI & Machine Learning Program is designed to equip you with the skills needed to build intelligent, data-driven applications. You’ll learn core AI concepts, machine learning, deep learning, natural language processing, computer vision, and generative AI, empowering you to solve real-world problems, boost your career, or start your freelancing journey.
✅ Practical, Project-Based Learning
Through hands-on projects, capstone assignments, and Kaggle-style challenges, you’ll build a strong portfolio showcasing your AI/ML skills, making you job-ready for high-demand roles in tech and data science.
✅ Expert Mentorship & Career Support
Learn directly from industry experts, receive personalized career guidance, interview preparation, and earn a recognized certification to stand out to employers or clients in the competitive AI/ML market.
✅ For Everyone Ready to Master AI
Whether you’re a student, working professional, or entrepreneur, this program will help you master AI/ML techniques, Python programming, data preprocessing, model deployment, and responsible AI practices, preparing you for smarter, faster, and scalable AI solutions.
👥 Who Should Enroll?
✔ Students & graduates aiming to start a career in AI/ML or Data Science
✔ Working professionals looking to upskill or switch to AI/ML roles
✔ Entrepreneurs & business owners wanting to integrate AI into their products or services
Earn Industry Recognised Certificates from:
Career Opportunities After the Course
List potential roles students can pursue after completing the course, such as
- Machine Learning Engineer
- AI Engineer
- Deep Learning Specialist
- NLP Engineer
- Data Analyst
- Business Intelligence Developer with AI/ML
With 100% job assurance, our course prepares you for a lucrative career in AI/ML.
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Live Virtual Classes
Batch Timings
📅 Weekday Batches
🕖 Monday to Friday | 7:00PM – 9:00PM
📅 Weekend Special Batches
🕖 Saturday | 4:00 PM – 9:00 PM
🕖 Sunday | 10:00 AM – 3:00 PM
Total Course Duration : 6 Months
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Alumni's Testimonies
The live interactive sessions made learning AI & ML so much easier. I could ask doubts instantly and work on projects with classmates. It felt like a real classroom, but from the comfort of home
Megh Sanjay Gade
ML Engineer
“What I loved most was the practical approach — every module had hands-on projects. By the end of the course, I had a complete portfolio, which helped me land multiple interview calls.”
Pooja Vinay Shukla
AI Assurance Engineer
Frequently Asked Questions
This course helps you master the core concepts of Artificial Intelligence and Machine Learning. You’ll learn how to train models, analyze data, and build smart systems that make predictions and automate tasks.
It’s ideal for students, graduates, engineers, data enthusiasts, and professionals who want to start a career in AI or enhance their existing technical skills.
The course runs for 6 months, combining conceptual learning with hands-on projects, assignments, and interactive sessions.
The total course fee is ₹18,000, which includes all study materials, project evaluations, and certification.
You’ll learn Python for AI, data preprocessing, supervised and unsupervised learning, neural networks, deep learning, natural language processing (NLP), and model deployment.
Basic programming knowledge helps, but the course starts from fundamentals, so beginners can also easily follow along.
You’ll work on real-world projects such as predictive modeling, image classification, chatbot creation, and data-driven analytics.
After successful completion, you’ll receive an industry-recognized AI & ML Certificate from HITMI that validates your technical and project-based skills
Yes, we provide 100% placement assistance, including resume preparation, interview guidance, and connections with leading tech and creative companies.
You can enroll directly through our official website or visit our nearest HITMI training center. Our counselors will guide you through the registration and payment process.