AI/ML training teaches you to write Python, prepare data and build machine learning models that make predictions, then apply those skills to real projects. At Solitaire Infosystems in Phase 7, Mohali, students from Chandigarh, Mohali and across the Tricity can take AI/ML training as a regular course, a 6-month internship or 6-month industrial training for their degree.
This guide covers what the training includes, which projects are worth building, how an internship differs from industrial training, and how to choose the option that fits your course and goals.
Artificial Intelligence (AI) is the field of building computer systems that do tasks which normally need human judgement: recognising patterns, understanding language, making predictions and supporting decisions.
Machine Learning (ML) is a branch of AI where algorithms learn patterns from data instead of following fixed rules. Predicting house prices, flagging fraudulent transactions and recommending products are all typical ML problems.
Deep Learning uses multi-layered neural networks for complex data such as images, speech and long text. Generative AI goes a step further and creates new content — text, images or code — the way tools like ChatGPT do.
For a beginner, the order matters: machine learning fundamentals first, deep learning and Generative AI after.
A good AI/ML curriculum moves from programming basics to working models in a clear sequence. These are the five core areas.
Python is the main language for AI and data work. You will cover variables, data types, loops, conditions, functions, lists and dictionaries, file handling, exception handling and object-oriented programming. The official Python tutorial is a useful companion for practice outside class.
Don’t rush this stage. Most problems learners face later with Pandas or scikit-learn are really Python problems.
Models are only as good as the data behind them. You will study probability, mean, median, standard deviation, distributions and basic linear algebra. NumPy and Pandas are used to load, clean, filter and reshape data, while Matplotlib and Seaborn turn it into charts you can read.
In real projects, cleaning and preparing data usually takes more time than training the model, so this is where practice pays off most.
Supervised learning covers Linear Regression, Logistic Regression, Decision Trees and Random Forest. Unsupervised learning covers K-Means clustering and dimensionality reduction. Most of this is done with scikit-learn, the standard Python library for classical machine learning.
Just as important is judging a model: train/test splits, overfitting and underfitting, and metrics such as accuracy, precision, recall and F1-score. A model with 95% accuracy can still be useless if it misses the cases that matter, and learning to spot that is a big part of the training.
Once the ML basics are solid, you move to neural networks with TensorFlow, Keras or PyTorch. Natural Language Processing (NLP) handles text for chatbots, sentiment analysis and document processing. Computer vision handles images and video, for tasks such as image classification and object detection.
This area covers Large Language Models (LLMs), prompt engineering, embeddings, AI APIs, vector databases and Retrieval-Augmented Generation (RAG). The aim is practical: learning how to connect an LLM to your own data and build a working application around it.
| Learning area | Tools |
| Programming | Python |
| Data analysis | NumPy, Pandas |
| Visualisation | Matplotlib, Seaborn |
| Machine learning | Scikit-learn |
| Deep learning | TensorFlow, Keras, PyTorch |
| NLP | NLTK, spaCy |
| Databases | SQL |
| Development | Jupyter Notebook, VS Code |
| Version control | Git, GitHub |
| Generative AI | LLM APIs, embeddings, RAG tools |
You don’t need all of these on day one. Learn Python, Pandas and scikit-learn well, then add tools as your projects need them.
Projects are what prove you can use what you learned. Start simple and increase the difficulty:
A finished project includes the problem statement, data preparation, model building, evaluation and a clear README on GitHub. That last part is often skipped, yet it’s the first thing an interviewer opens.
The most common mistake is copying a notebook from the internet and changing the dataset. If you can’t explain why you chose an algorithm or what you would improve, the project won’t help you in an interview.
A six-month program gives you time to go from Python basics to complete projects at a steady pace. Solitaire Infosystems offers both a 6-month internship program and 6-month industrial training in Mohali for students of B.Tech, BCA, MCA, M.Tech, B.Sc., M.Sc. and related courses.
Over the six months, the work typically covers:
Before you join any program, ask to see the current syllabus, the projects you’ll work on, the batch timings and how mentoring works.
| Option | Main focus | Best for |
| AI/ML course | Concepts and technical fundamentals | Beginners and working professionals building a base |
| 6-month AI/ML internship | Project work and real development practice | Students and graduates who want project experience |
| 6-month AI/ML industrial training | Structured training with industry-style projects | Students who need training credits for their degree |
Start by checking your university’s rules if you need six months of training for your degree; industrial training is likely the right fit. An internship suits learners who already know the basics and want more hands-on project work. For beginners with no fixed deadline, a course offers the simplest entry point.
It depends on your starting point and how much you practice. With regular practice, Python basics come first, then data handling and statistics, and building complete machine learning projects comfortably usually takes several months. That is why six-month internship and industrial training programs suit AI/ML well: there is time to move past theory and finish real projects.
AI/ML skills lead to different roles depending on your degree, depth of skill and specialization:
A data analyst spends more time on SQL, dashboards and explaining findings; an ML engineer spends more time writing code and deploying models. Fresh graduates often start in analyst or Python developer roles and move into ML work as their projects grow.
Completing a course, internship or industrial training does not guarantee a job. Strong projects, a clean GitHub profile and the ability to explain your work are what make the difference in interviews.
Yes, if you start with Python and basic statistics before moving to algorithms. You don’t need a computer science degree, but you do need regular coding practice. Beginners who skip the fundamentals usually get stuck once projects involve messy, real-world data.
It is worth it if you spend those months building and finishing projects, not just attending sessions. Six months gives enough time to move from Python basics to complete machine learning projects you can show and explain in an interview.
Both involve practical AI/ML work. Industrial training is usually taken to meet a university’s training requirement for B.Tech, BCA or MCA, while an internship focuses more on project work and development practice. Check what your university needs before you choose.
No. Most AI/ML training starts with Python. Knowing basic programming helps you move faster, but it is not a requirement.
No. Hiring depends on your skills, projects, qualifications, interview performance and the openings available. Training and good projects improve your chances; they don’t guarantee a job.
If you are looking for AI/ML training in Chandigarh or Mohali, pick the format that matches your degree and where you are right now: a course to build the base, an internship for project experience, or industrial training to meet your university requirement. The next batch of 6-month internship and industrial training at Solitaire Infosystems starts in January 2027.
Want to know the syllabus, batch timings and how to apply? Apply for the 6-month AI/ML internship or visit us at C-110, Phase 7, Industrial Area, Mohali.
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