Machine Learning · UpsSkills

Learn Machine Learning from data to deployment.

Build a strong foundation in Python, statistics and data analysis, then move through regression, classification, clustering, deep learning, computer vision, NLP, model evaluation and deployment — with UpAskAI available throughout your learning.

Python Statistics Scikit-learn Deep Learning NLP Deployment

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Learning path

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Python & Data Python, NumPy, Pandas, visualization and data exploration.

Statistics & Probability Distributions, hypothesis testing and statistical reasoning.

Core Machine Learning Regression, classification, clustering and evaluation.

Deep Learning Neural networks, CNNs and model training fundamentals.

NLP & Computer Vision Text, image and transformer-based application concepts.

Model Deployment Pipelines, FastAPI, Docker and ML product deployment concepts.

The active training plan may adjust module order, examples and deployment tools.

Work with Data Clean, explore and prepare data before building models.

Understand Models Learn why algorithms work, not only how to call a library function.

Evaluate Properly Use suitable metrics, validation and tuning for model decisions.

Deploy Models Connect trained models to APIs and application workflows.

Course curriculum

Machine Learning syllabus

This keeps the 10-module structure from your current course, but presents it in a cleaner, student-friendly sequence.

Python syntax, OOP and file handling

NumPy and Pandas

Matplotlib and Seaborn

Practice: exploratory data analysis

Probability distributions

Bayes theorem

Hypothesis testing and p-values

Confidence intervals

Practice: statistical analysis of data

Linear, ridge, lasso and polynomial regression

Loss functions and gradient descent concepts

Overfitting and regularization

Project practice: forecasting / prediction workflow

Logistic regression, SVM, KNN and Naive Bayes

Decision trees and random forests

Boosting concepts including XGBoost

Precision, recall, confusion matrix and ROC/AUC

Project practice: classification system

K-Means, DBSCAN and hierarchical clustering

PCA and dimensionality reduction

t-SNE concepts

Practice: customer segmentation

Perceptrons and neural network structure

Backpropagation and activation functions

PyTorch and TensorFlow/Keras foundations

Dropout, batch normalization and regularization

Project practice: neural network classifier

CNN architecture fundamentals

ResNet, VGG and EfficientNet concepts

Transfer learning and data augmentation

Project practice: image classification app

Tokenization, TF-IDF and word embeddings

Sentiment analysis and text classification

Transformer and Hugging Face fundamentals

Project practice: sentiment analysis API

Cross-validation

GridSearch and tuning concepts

Feature engineering

Scikit-learn pipelines

Practice: complete model pipeline

FastAPI model-serving concepts

Docker fundamentals

Deployment concepts for cloud / hosted platforms

Build and deploy an ML-enabled application

Learning experience

Learn the full ML workflow

Students should understand the complete path from raw data to model evaluation and application deployment, not just individual algorithms.

Use UpAskAI while learning

Ask course-aware questions about Python, statistics, machine learning, deep learning, NLP and related topics available in UpAskAI.

Understand model decisions

Learn why a model, metric or preprocessing step is chosen and how each decision changes the result.

Move from notebook to application

Practice connecting trained models to APIs and application workflows instead of stopping at model.fit().

Technology stack

Tools in the Machine Learning path

Python

NumPy / Pandas

Matplotlib / Seaborn

Scikit-learn

XGBoost

PyTorch

TensorFlow

Hugging Face

FastAPI

Docker

Course enquiry

Interested in Machine Learning?

Contact UpsSkills for the current batch schedule, training format, fee and course-access details.

What is included

The active batch can vary in schedule and examples. The course is centered around the ML curriculum, guided practice, project work and UpAskAI support.

Structured Machine Learning curriculum

Python, statistics and data foundations

Regression, classification and clustering

Deep learning, NLP and computer vision concepts

Project-oriented model practice

UpAskAI course-aware learning support

Questions

Before you join

The learning path begins with Python and data foundations before moving into statistics, machine learning algorithms, deep learning and deployment.

The course includes statistics and probability foundations needed for the ML topics in the learning path.

Yes. Use Try UpAskAI for the free learning trial, or contact UpsSkills through WhatsApp for a Machine Learning course demo.

Please contact UpsSkills at 79959 14429 for current batch and fee information.

See how Machine Learning is taught in UpsSkills.

Try UpAskAI first or speak with us about the Machine Learning course. Understand the learning experience before choosing the program.

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