NIHAL SOFTWARE
Developers & Expoters of Quality Software
Python, AI & Machine Learning
Objective
The primary objective of this 6-month program is to train learners in Python programming, data handling, machine learning, deep learning, NLP, and applied AI development. The course aims to build strong programming foundations, develop practical skills in data analysis, teach core machine learning algorithms, introduce neural networks and transformer models, and provide real-world experience through deployment and capstone projects.
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Target Audience
Students (Grade 10+) - Beginners with little programming experience - Hobbyists and STEM enthusiasts
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Prerequisites
Experience in Basic Python, AI concepts and Machine Learning Algorithms.​
Cost - ₹99,000
Duration - 6 Months
Syllabus
Month 1
The first month focuses on developing a strong foundation in Python programming. Students begin with Python basics, including variables, data types, operators, input/output, and simple scripting.
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During the second week, they learn about essential data structures such as lists, tuples, sets, dictionaries, and string manipulation, followed by extensive coding exercises.
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The third week covers control flow, loops, conditional statements, functions, lambda expressions, recursion, and error handling. Students apply this knowledge in a mini console-based project.
The fourth week introduces object-oriented programming, covering classes, objects, inheritance, polymorphism, and file handling with CSV and JSON files. The month concludes with a practical project such as an Employee or Student Management System
Month 2
The second month trains learners in the core data analysis libraries used in the data science ecosystem. The fifth week covers NumPy, including arrays, indexing, slicing, vectorization, and mathematical operations.
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In the sixth week, students learn Pandas for data cleaning, preprocessing, handling missing values, merging datasets, and performing Data-Frame operations.
The seventh week focuses on data visualization using Matplotlib, Seaborn, and an introduction to interactive dashboards using Plotly.
In the eighth week, students perform complete exploratory data analysis (EDA) on a real dataset (sales, health, finance, or similar). They study distributions, outliers, correlations, trends, and insights. A full EDA project is completed at the end of this month.
Month 3
The third month introduces core machine learning. The ninth week explains the machine learning workflow, dataset splitting, overfitting, underfitting, and evaluation metrics such as accuracy, F1-score, and RMSE.
In the tenth week, students learn supervised learning algorithms including linear regression, logistic regression, support vector machines, and k-nearest neighbors. During this period, they complete a machine learning project such as house price prediction.
The eleventh week covers tree-based and ensemble models, including decision trees, random forest, XGBoost, and hyperparameter tuning methods such as GridSearch and RandomSearch.
The twelfth week introduces unsupervised learning, including K-Means clustering, hierarchical clustering, and PCA for dimensionality reduction. A customer segmentation project is completed to apply the concepts learned of this month.
Month 4
The fourth month focuses on neural networks and deep learning. The thirteenth week covers perceptrons, feedforward neural networks, activation functions, gradient descent, and backpropagation.
In the fourteenth week, students work with TensorFlow and Keras to build neural network models, understand optimizers, loss functions, callbacks, and model training workflows.
The fifteenth week focuses on computer vision using convolutional neural networks (CNNs). Students learn about convolution layers, pooling layers, image preprocessing, augmentation, and build a CNN image classification project.
The sixteenth week covers sequence models including RNN, LSTM, and GRU. Students apply these models to text-based tasks such as sentiment analysis and time-series forecasting.
Month 5
The fifth month introduces modern natural language processing and generative AI. The seventeenth week covers the fundamentals of NLP including tokenization, stemming, lemmatization, bag-of-words, TF-IDF, and text classification.
The eighteenth week introduces transformer architectures, BERT, embeddings, and the basics of fine-tuning transformer models using Hugging Face.
In the nineteenth week, students explore generative AI and large language models including GPT, Gemini, and Llama. They learn prompt engineering, text generation, embeddings, and retrieval-based systems.
The twentieth week involves building practical NLP and AI projects such as document question answering systems, resume analyzers, chatbots, FAQ assistants, or simple voice assistants.
Month 6
The sixth month prepares learners for production-level AI engineering. The twenty-first week covers MLOps fundamentals including model lifecycle, version control, pipelines, and model monitoring concepts.
The twenty-second week trains students in deploying machine learning models using FastAPI to build REST APIs. They also learn cloud deployment on platforms such as Render, Railway, and Hugging Face Spaces, with optional AWS deployment.
In the twenty-third week, students begin working on their major capstone project, selecting topics such as an AI voice assistant, PDF chatbot, face recognition system, retail prediction engine, or a multi-agent AI system.
The final week includes project presentation, GitHub portfolio building, LinkedIn optimization, resume writing for AI/ML roles, and mock interview preparation.