Artificial Intelligence & Data Science
Artificial Intelligence & Machine Learning Course
6 Months
Advanced
Certified Course
Contact Us
+918160529351
info.nodetolearn@gmail.com
Surat, Gujarat
Course Overview
AI and Machine Learning are no longer just buzzwords — they're the skills behind recommendation engines, fraud detection systems, medical diagnosis tools, and self-driving cars. This course teaches you how those systems actually work, from the math underneath to the code on top, and finally to a deployed model running live on the internet.
You'll start with the essentials: cleaning messy data, understanding how algorithms learn from examples, and building your first prediction models using Python, NumPy, Pandas, and Scikit-Learn. From there you'll move into deep learning — building neural networks using TensorFlow and Keras that can classify images and recognize patterns. Every concept gets built hands-on in Jupyter Notebook, not just explained on a slide.
Because this is 1-on-1 mentorship, your sessions move at your pace. If you need more time on a concept like backpropagation or cross-validation, you get it. If you're moving fast, you move fast. You won't be waiting for a batch to catch up or rushing because someone else is ahead. By the end, you'll have a real ML project deployed to the cloud with a working web interface showing live predictions — something you can actually show in interviews.
This course is built for people who want to understand how AI works, not just how to copy-paste a model from a tutorial. If you're looking to get into data science, machine learning engineering, or AI development as a serious career path, this is where you start.
You'll start with the essentials: cleaning messy data, understanding how algorithms learn from examples, and building your first prediction models using Python, NumPy, Pandas, and Scikit-Learn. From there you'll move into deep learning — building neural networks using TensorFlow and Keras that can classify images and recognize patterns. Every concept gets built hands-on in Jupyter Notebook, not just explained on a slide.
Because this is 1-on-1 mentorship, your sessions move at your pace. If you need more time on a concept like backpropagation or cross-validation, you get it. If you're moving fast, you move fast. You won't be waiting for a batch to catch up or rushing because someone else is ahead. By the end, you'll have a real ML project deployed to the cloud with a working web interface showing live predictions — something you can actually show in interviews.
This course is built for people who want to understand how AI works, not just how to copy-paste a model from a tutorial. If you're looking to get into data science, machine learning engineering, or AI development as a serious career path, this is where you start.
Who is this for?
This course is a good fit for BCA, MCA, and B.Tech (CS/IT) students or graduates who want to move into AI or data science roles. It also works well for backend developers who already know some Python and want to add machine learning skills to their profile. You don't need prior ML experience, but you should be comfortable with basic Python programming before joining — if you're not, ask us about our Python fundamentals course first. This is not a course for complete beginners to programming.
Career Outcomes
- Completing this Artificial Intelligence and Machine Learning course in Surat opens doors to roles like Machine Learning Engineer
- Data Scientist
- AI Developer
- Deep Learning Engineer
- and Predictive Analytics Developer. You can also pursue freelance AI project work or consulting once you have a solid deployed portfolio.
Course Curriculum
Module 1: Python for Data — NumPy, Pandas & a Simple Project Dashboard
- Setting up your workspace: Installing Anaconda, Jupyter Notebook, and the required Python libraries so everything runs correctly from day one.
- NumPy basics: Working with arrays and matrices in Python — the foundation every ML library is built on. Includes array math, slicing, and reshaping.
- Pandas for data cleaning: Loading real datasets (CSV files), fixing missing values, filtering rows, grouping data, and getting it ready for a model.
- Building a simple project dashboard: Creating a basic HTML/CSS webpage to display your model's prediction results — so your project looks presentable, not just a notebook output.
Module 2: Supervised Learning — Regression and Classification
- Linear Regression from scratch: Understanding how a model learns to predict a number (like house price or sales revenue) by minimizing error using gradient descent.
- Regularization with Lasso and Ridge: How to stop your model from overfitting (memorizing training data instead of learning from it) using L1 and L2 penalties.
- Logistic Regression for classification: Teaching a model to predict yes/no outcomes (like spam or not spam) using a sigmoid function and cross-entropy loss.
- Measuring model performance: Reading a confusion matrix, understanding Precision vs. Recall, and interpreting the AUC-ROC score to know if your model is actually good.
Module 3: Smarter Algorithms — Decision Trees, Random Forests & SVMs
- Decision Trees: How a model makes decisions by splitting data at each step using concepts like Gini impurity and entropy — and how to control tree depth to avoid overfitting.
- Random Forests: Why combining hundreds of decision trees (each trained on a random slice of data) gives far better results than a single tree.
- Support Vector Machines (SVM): How SVMs find the best boundary between two classes, including the kernel trick for data that isn't linearly separable.
- Hyperparameter tuning with Grid Search and Random Search: Automatically testing different model settings to find the combination that performs best on your data.
Module 4: Deep Learning — Neural Networks with TensorFlow & Keras
- Building an Artificial Neural Network (ANN): Stacking input, hidden, and output layers in Keras, setting weights and biases, and training the network on real data.
- Activation functions explained: What ReLU, Sigmoid, and Tanh do mathematically and why choosing the right one matters for your network's performance.
- Backpropagation and optimizers: How a neural network learns by calculating gradients and updating weights — and how to tune learning rate and use optimizers like Adam.
- Convolutional Neural Networks (CNN) for image data: Building CNNs with convolution layers, pooling layers, and fully connected layers to classify images correctly.
Module 5: Deploying Your Model & Final Project
- Saving and loading trained models: Using Python's pickle utility and Keras model-saving methods to store a trained model as a file you can reuse.
- Wrapping your model in an API: Building a lightweight backend endpoint (using Flask or FastAPI) so other applications can send data to your model and get predictions back.
- Deploying to the cloud: Hosting your trained model and its web interface on a live cloud server so it works on a real URL, not just on your laptop.
- Final project and portfolio defense: Assembling a complete end-to-end ML pipeline — from raw data to live deployed model — and walking through it in a mock interview-style review session.