AI and ML
- 300+ Hours of training
- 450+ Hiring Partners
- Classroom & Live Online Training Available
- Hands-on training with assignments and capstone projects
- Next batch starts 10 Nov 2026 · Cohort Kanchenjunga
DedicatedPlacement Assistance
About the AI and ML course
A comprehensive grounding in AI and machine learning, covering supervised and unsupervised learning, model evaluation and tuning, and the fundamentals of neural networks. You'll work through the full modeling lifecycle — cleaning and preparing data, choosing and training the right algorithm, measuring performance honestly, and iterating — using industry-standard Python libraries throughout. Projects are designed to mirror real industry problems in areas like classification, regression, and clustering, so you graduate with practical, defensible experience rather than just theory, and a clear sense of how ML fits into a larger product or business.
Learn From The Real Forward Deployed Engineers And AI Experts
Guest lectures and mentorship from Forward Deployed Engineers working at leading companies.
Companies Hiring In This Field
Companies that recruit for roles in this field.
Amazon
IBMWho Should Do The AI and ML?
Who this program is built for.
- Working professionals looking to upskill or switch domains01
- M.Tech / M.E. graduates looking to specialize or pivot into a new tech track02
- B.Tech / B.E. graduates or final-year students in CS, IT, ECE, or related branches03
- BCA, MCA, or B.Sc / M.Sc (Computer Science, IT, or related) graduates04
- 05M.Com, B.Com, MBA, or BBA graduates
- 06Diploma holders in Computer Science, IT, or Engineering
- 07No prior coding experience required for beginner-track courses
- 08Final-year students and recent graduates from any stream
AI and ML Learning Path
Learn → Build → Deploy → Solve Real Problems → Get Placed
Tech Stack
Technologies & Tools
Placement Path
From your first class to your first offer letter, here is how we take you there.
Career Opportunities with AI and ML
Tap or hover a card to see what the role looks like day to day. Pay ranges are indicative market figures, not a guarantee of salary or placement.
ML Research AssociateIndicative market pay: ₹6 – 10 LPA
ML Research Associate
A role you can aim for after AI and ML. We help you prepare with projects, mock interviews and resume reviews.
Machine Learning EngineerIndicative market pay: ₹6 – 10 LPA
Machine Learning Engineer
A role you can aim for after AI and ML. We help you prepare with projects, mock interviews and resume reviews.
AI EngineerIndicative market pay: ₹7 – 12 LPA
AI Engineer
A role you can aim for after AI and ML. We help you prepare with projects, mock interviews and resume reviews.
Data ScientistIndicative market pay: ₹8 – 13 LPA
Data Scientist
A role you can aim for after AI and ML. We help you prepare with projects, mock interviews and resume reviews.
Applied ML EngineerIndicative market pay: ₹9 – 14 LPA
Applied ML Engineer
A role you can aim for after AI and ML. We help you prepare with projects, mock interviews and resume reviews.
Senior Data ScientistIndicative market pay: ₹14 – 20 LPA
Senior Data Scientist
A role you can aim for after AI and ML. We help you prepare with projects, mock interviews and resume reviews.
Everything That You Get by Enrolling in AI and ML
We've built this program with one goal — to guide, support, and uplift you at every stage of your career journey. From the first class to your first placement and beyond, every component has been created with your growth, confidence, and success in mind.
EMI Options Available
Placement Assistance
Collaborative Learning through Teamwork
Course Completion Certificate
Expert Career Guidance
Flexible Learning
ISO 9001:2015 Certified Institute
Hands on Project
Corporate-Ready Curriculum
Industry Expert Sessions
Internship programs
Mock Interview
Placement Mentorship
Resume Preparation Guidance
Recognised worldwide
ISO
IAF
MSME
DPIIT
Accreditation and Recognition from Top Industry Bodies
Our programs and certifications are affiliated with, and recognised by, globally respected standards and skill-development bodies.
AI and ML Curriculum
Comprehensive modules designed to build AI and ML skills step by step.
Build your AI/ML toolkit: Python, data manipulation and AI-assisted coding.
Key Topics
- Dev environment: Jupyter, VS Code, PyCharm, Anaconda, Google Colab
- Data types, operators, strings, lists, tuples, sets & dictionaries
- Control flow, match-case, loops & comprehensions
- Functions, *args/**kwargs, lambda, modules & packages
- Iterators, generators & decorators
- File handling, logging, error & exception handling
- OOP: classes, inheritance, access modifiers, polymorphism, abstraction
- Frameworks for UI & deployment: FastAPI, Streamlit, Flask
- Git & version control; AI-assisted coding with ChatGPT, Claude, Copilot
Industry Relevance
Automate repetitive analysis tasksWrite clean, reusable codeDebug and log real programsUse AI coding assistants responsiblyMaster data wrangling, visualization and statistical reasoning for confident, data-driven decisions.
Key Topics
- NumPy & Pandas: arrays, groupby, pivot, merges, missing-data handling (MCAR/MAR/MNAR)
- Matplotlib & Seaborn: subplots, pair plots, heat maps, catplots
- EDA: univariate, bivariate, multivariate analysis, outliers & missing values
- Unstructured data: regular expressions, regex with Pandas
- Descriptive vs inferential statistics, central tendency, dispersion, skewness, covariance & correlation
- Probability & distributions: binomial, Poisson, normal, Bernoulli, uniform
- Hypothesis testing: t-test vs z-test, chi-square, ANOVA, CLT, confidence intervals, p-values
- A/B testing: methodology, sample size, statistical significance
Industry Relevance
Clean and reshape messy datasetsExplain patterns with clear visualsTest business hypotheses with evidenceDesign and read A/B experimentsBuild, evaluate and explain supervised and unsupervised models.
Key Topics
- ML foundations: lifecycle, bias-variance, overfitting, train/validation/test splits
- Regression: linear, polynomial, Ridge/Lasso/ElasticNet, logistic
- Classification: Naive Bayes, decision tree, random forest, SVM, KNN, XGBoost, AdaBoost & bagging
- Imbalanced data (SMOTE), cross-validation, GridSearchCV/RandomizedSearchCV, sklearn pipelines
- Unsupervised: K-Means, hierarchical, DBSCAN, PCA/t-SNE/UMAP, anomaly detection, Apriori/FP-Growth
- AutoML, recommendation systems & time-series forecasting (ARIMA, Prophet, LSTM)
- Explainability: SHAP, LIME, partial dependence plots, ELI5
- Responsible AI: fairness metrics, model cards, GDPR & EU AI Act
- Projects: airline passenger referral, credit card fraud, customer churn, loan approval
Industry Relevance
Predict churn, fraud and credit riskExplain model decisions to stakeholdersTune and compare models rigorouslySpot bias before shippingDesign, train and deploy neural networks for vision and sequence problems.
Key Topics
- Neural network fundamentals: perceptron, forward/backpropagation, vanishing gradients
- Activation functions, optimizers (SGD, RMSProp, Adam), loss functions, dropout & batch norm
- ANNs with Keras Sequential & Functional API
- Computer vision: OpenCV, CNNs, data augmentation, YOLO/SSD object detection, segmentation basics
- Transfer learning: LeNet, AlexNet, VGG, ResNet, MobileNet, EfficientNet, Hugging Face models
- Sequence models: RNN, LSTM, GRU, bidirectional LSTM, seq2seq
- Generative models: autoencoders, VAE, GAN basics
- Attention, self-attention, Transformer & Vision Transformer (ViT) overview
- Libraries & deployment: TensorFlow, Keras, PyTorch, JAX, ONNX, TensorFlow Serving, TorchServe
- Projects: fake news detection, house price prediction, brain tumor classification, plant disease detection
Industry Relevance
Build image classifiers and detectorsFine-tune pre-trained modelsTrain on GPUs in the cloudServe models behind an APIBuild text classification, sentiment, chatbot and translation applications.
Key Topics
- Text preprocessing: tokenization, stemming, lemmatization, POS tagging, parsing
- Text representation: Bag of Words, TF-IDF, Word2Vec, GloVe, FastText, sentence embeddings
- NLP libraries: NLTK, spaCy, Gensim, TextBlob, Hugging Face Tokenizers
- Deep learning for NLP: RNN/LSTM/GRU, NER, sentiment analysis, text classification
- Advanced NLP: summarization, machine translation, question answering, chatbots, topic modeling (LDA)
- Transformers: BERT, GPT family, RoBERTa, DistilBERT, T5, Hugging Face pipelines, fine-tuning
- Evaluation: BLEU, ROUGE, perplexity, BERTScore
- Projects: language translation, customer support chatbot, sentiment analysis, spam email classifier
Industry Relevance
Classify and summarize text at scaleBuild customer support chatbotsFine-tune BERT on custom dataEvaluate language models properlyApply LLMs, prompt engineering and RAG to build generative AI applications.
Key Topics
- Generative vs discriminative models: GANs, VAEs, diffusion, autoregressive
- LLM families (GPT, Claude, Llama, Gemini, Mistral), tokenization, context windows
- Fine-tuning vs in-context learning, LoRA & QLoRA, model limitations (hallucination, bias, cost)
- Prompt engineering: zero/few-shot, chain-of-thought, structured output, system prompts, ReAct
- Prompt injection & safety
- RAG: embeddings, vector databases (FAISS, Pinecone, ChromaDB), chunking, re-ranking, hybrid search
- LLM agents: tool & function calling, LangChain, LlamaIndex, multi-agent basics, memory
- Tools: OpenAI API, Claude API, Hugging Face Hub, Stable Diffusion, Ollama
- Projects: knowledge-base assistant, AI content generator, document Q&A bot
Industry Relevance
Build RAG assistants over company documentsWrite reliable promptsCall LLM APIs safelyPrototype agents with toolsDeploy, serve and monitor machine learning models in production on AWS.
Key Topics
- AWS fundamentals: EC2, S3, IAM, VPC, Lambda, ECR
- Amazon SageMaker: training jobs, endpoints, pipelines; Bedrock & AWS AI services
- MLOps: ML pipelines, experiment tracking (MLflow, W&B), feature stores, model & data versioning (DVC)
- Serving: real-time vs batch, REST APIs with FastAPI/Flask, drift monitoring with Evidently AI
- CI/CD for ML: GitHub Actions, automated model tests, continuous training triggers
- Docker & Kubernetes: Dockerfile, docker-compose, pods, services, Helm, scaling inference
- Auto-scaling (ECS, EKS), spot instances, cost optimization, model compression
- Projects: deploy an ML model as a REST API on AWS; containerized serving with Docker & Kubernetes
Industry Relevance
Ship models as production APIsMonitor drift and performanceAutomate ML pipelinesControl cloud spendBuild, deploy and showcase a complete production AI system.
Key Topics
- Problem definition & data collection
- Full ML/DL pipeline build & training
- AWS production deployment & CloudWatch monitoring dashboard
- Portfolio curation: GitHub, LinkedIn & Kaggle
- Demo day & mock interviews
Industry Relevance
Own a project end to endPresent results to a review panelBuild a recruiter-ready portfolioShow production-grade engineering
The L&L AI Solutions Placement Mentorship Program
At L&L AI Solutions, we treat every learner as a professional in progress. Our Placement Mentorship Program pairs you with dedicated mentors who guide, support, and push you through every stage of your journey — from your first module to your final interview — so you leave not just trained, but genuinely ready to build a career in AI and ML.
Stage 01
Sharpen Your Fundamentals & Interview Confidence
You'll work closely with mentors who pressure-test what you know and flag exactly where to focus next. Through regular check-ins, simulated interviews, and guided practice, you'll build the technical clarity and communication confidence to hold your own in any interview — technical or HR.
Skill Gap Assessments
Confidence Coaching
Technical Interview Simulations
Weekly Progress Scorecards
HR Round Simulations
Concept Refresher Sessions
Communication Skill Coaching
Key Support
Stage 02
Map Your Career Path & Build Your Professional Profile
Get complete clarity on how placements actually work — the roles open to you, what employers expect, and where you're the strongest fit. With a mentor by your side, you'll rebuild your resume and LinkedIn profile around the roles you're targeting, so your profile reads as capable as you actually are.
Career Path Walkthroughs
What to Expect from Placements
Resume Rebuild Support
LinkedIn Profile Makeover
Role Eligibility Reviews
Personal Branding Guidance
Extra Upskilling Support
Key Support
Stage 03
Apply, Interview & Land the Role
This is where preparation turns into real opportunity. Our placement team matches your profile to live openings, preps you for each specific interview, coordinates directly with employers, and stays with you — feedback and all — until an offer is in hand.
Live Job Opportunities
Profile-to-Role Matching
Role Briefings
Application Support
1:1 Interview Prep
Interview Scheduling Help
Post-Offer Support
Key Support
AI and ML — FAQs
Everything you need to know about the course and admissions.
Classes are held live online and at our campus: 5th Floor, Draper Startup House Building, Rajiv Gandhi Nagar, Gachibowli, Hyderabad 500032.
Core machine learning concepts, models, and practical implementation.
Eligibility depends on your background. See the "Who Should Do The AI and ML?" section on this page for the details.
The program runs for 6 Months.
The next cohort starts on 10 Nov 2026. Contact our admissions team to confirm seats and timings.
Yes. You receive a certificate of completion once you finish the program requirements.
Yes. We provide placement assistance, including mentorship, resume support and mock interviews. Placement assistance is not a guarantee of a job.
Yes. The course is built around hands-on practice and projects, ending with a capstone project you can add to your portfolio.
Yes. Mentors review your work and clear doubts throughout the program, not only during the capstone.
A laptop with a stable internet connection. Setup for the tools used is covered at the start of the course.
Our refund and cancellation terms are published on the Refund Policy page of this website. Please read them before you enrol, or ask our admissions team.
