Build AI and ML that works in the real world
A 6-month career-focused program. Start with Python, then learn machine learning, deep learning and Generative AI, and finish by deploying your own model with a web interface.
Machine learning, from Python to deployment
Artificial Intelligence and Machine Learning help us find patterns, make predictions, understand language and support decisions. This course takes you from Python programming to building and deploying AI and ML applications.
You work with real datasets, write code, compare model results, build neural networks, explore Generative AI and complete an end-to-end project.
What you will build
- Data analysis and preparation workflows
- Regression, classification and segmentation models
- Neural network and image classification projects
- Text classification and sentiment analysis tools
- A document chatbot or simple RAG application
- A deployed model with a web interface
who want to build AI and ML skills.
moving into machine learning.
seeking practical AI skills.
interested in prediction and automation.
Six steps from first line of code to live model
Machine Learning syllabus in Karnal
Python Programming Foundation
About 2 to 2.5 months · Learn to write, test and understand code first
M1Programming Foundations
- What programming is; Python setup, IDEs, notebooks and Google Colab
- Syntax, variables, constants and naming rules
- Data types, type conversion, input and output
- Arithmetic, comparison, logical and membership operators
M2Control Flow and Collections
- if, elif, else and nested conditions
- for and while loops, range, break and continue
- Strings: indexing, slicing, methods and formatting
- Lists, tuples, sets and dictionaries
M3Functions and Code Structure
- Defining and calling functions, parameters and return values
- Default and keyword arguments, lambda and basic recursion
- Modules, packages, imports and the standard library
- Breaking a problem into smaller reusable steps
M4Files, Errors and OOP
- Read and write text and CSV files
- Exceptions: try, except, else and finally
- Classes, objects, constructors and methods
- Inheritance, encapsulation, polymorphism and debugging
M5Python for Data Work
- NumPy arrays, shapes, slicing and vector operations
- Pandas Series and DataFrames: filter, sort, group and merge
- Matplotlib and Seaborn charts
- Missing values, duplicates, summary statistics and EDA
Machine Learning
Data preparation, model training and performance evaluation
M6AI and Machine Learning Foundations
- AI, ML and deep learning, with real-world use cases
- Supervised, unsupervised, semi-supervised and reinforcement learning
- Training, validation and test datasets
- Bias, variance, overfitting, underfitting and data leakage
M7Data Preprocessing and Features
- Cleaning data, missing values and outlier detection
- Label and one-hot encoding
- Standardization and normalization
- Feature selection, PCA, train-test split and cross-validation
M8Supervised Learning: Regression
- Simple, multiple and polynomial regression
- Ridge, Lasso and Elastic Net
- MAE, MSE, RMSE and R-squared
- Practical: house price or salary prediction
M9Supervised Learning: Classification
- Logistic Regression, KNN, Decision Tree, Random Forest, SVM and Naive Bayes
- Gradient Boosting and model comparison
- Confusion matrix, precision, recall, F1, ROC and AUC
- Project: diabetes, heart disease or breast cancer classification
M10Unsupervised Learning
- K-Means, hierarchical clustering and DBSCAN
- Elbow Method and Silhouette Score
- PCA visualization and cluster interpretation
- Mini project: customer segmentation
Deep Learning to Deployment
Modern AI skills, practical projects and model delivery
M11Neural Networks and Deep Learning
- Neurons, perceptron and network architecture
- Sigmoid, ReLU, Tanh and Softmax
- Forward and backpropagation, loss and gradient descent
- TensorFlow and Keras: build and train networks, dropout and early stopping
M12CNN, RNN and Sequence Models
- Convolution, filters, pooling and CNN layers
- Image classification with MNIST, CIFAR-10 or plant disease data
- RNN, the vanishing gradient problem, LSTM and GRU
- Text and time-series practical work
M13Natural Language Processing
- Tokenization, stop-words, stemming and lemmatization
- Bag of Words, TF-IDF and word embeddings
- Sentiment analysis and text classification
- Practical: spam detection, news classification and movie review sentiment
M14Generative AI and Modern AI
- Large Language Models and Transformers
- Attention mechanism, prompt engineering and multimodal AI
- RAG, document question answering and an introduction to AI agents
- Responsible AI: hallucinations, bias, privacy and security
M15Deployment and MLOps
- ML pipelines, model serialization and REST APIs
- Build an interface with Streamlit, Flask or FastAPI
- Git and GitHub, model monitoring and an MLOps introduction
- Deploy a trained model and connect user input to prediction output
The tools professionals use
By the end you can collect, clean, analyze, preprocess, train, evaluate, compare, optimize, deploy and explain an AI or ML model using Python and suitable libraries.
Pick one and build it end to end
From problem definition and dataset selection to cleaning, model building, testing, evaluation and deployment.
Total 100 marks
Get certified in Machine Learning
Complete the program, your labs and the major project to receive the TechBooz Certificate of Completion.
- ISO 9001:2015 certified institute
- Verifiable certificate on techbooz.com
- A deployed project to show along with it
Roles you can aim for
Machine Learning course questions
Do I need to know programming before joining?
No. Python is taught first, over about 2 to 2.5 months, so you can write and understand code before moving to machine learning.
How long is the course?
6 months, with theory and hands-on lab work. Our counsellor shares the class timings on a call.
Is the course available online?
Yes. You can join live online classes from anywhere in India or attend offline at our Karnal institute.
Will I build real projects?
Yes. You work with real datasets, build several mini projects and complete one major end-to-end project that you deploy with a web interface.
Does the course cover Generative AI?
Yes. You learn Large Language Models, prompt engineering, RAG and document question answering, an introduction to AI agents, and responsible AI.
Will I get a certificate?
Yes. You receive a TechBooz Certificate of Completion that can be verified on our website.
Do you provide placement support?
We help with your portfolio, resume and interview preparation. We cannot promise a job, because that depends on your skills and effort.
What is the fee and when does the next batch start?
Our counsellor shares fee and batch details on a short call. Use the Request a Call Back button and we will contact you.
Start your AI and ML career in Karnal
Sit in a demo class, see a live lab and talk to our team about the right plan for you.