Machine Learning Scikit Tensorflow in Sector 69 Tulip Chowk, Gurgaon

Master Machine Learning Scikit Tensorflow with industry experts, live capstone projects, and comprehensive career assistance.

Looking for professional Machine Learning Lib - Scikit TensorFlow & training in Gurugram Our comprehensive course at Blazingminds Learning is designed for students, working professionals, and business owners who want to gain practical expertise in Machine learning; Model training; AI fundamentals. The training covers industry-relevant concepts, hands-on projects, real-world case studies, and certification preparation to help learners build job-ready skills.

With expert trainers, flexible learning schedules, and placement assistance, our Machine Learning Lib - Scikit TensorFlow course in Gurugram helps participants stay competitive in today's job market. Whether you are a beginner or an experienced professional looking to upskill, this program provides the knowledge and practical experience needed to succeed.

Enroll today in the leading Machine Learning Lib - Scikit TensorFlow training institute in Gurugram and take the next step toward your career goals.

Industry Recognized Course

What You'll Learn

Build ML models; Understand ML concepts

Course Curriculum

16 Modules • 181 Lessons

Python ecosystem for AI/ML

10 min Preview

Understand the roles of Python libraries in AI/ML, set up development environments, and choose the right framework for specific tasks.

Why Scikit-learn, TensorFlow and PyTorch?

10 min

Evaluate framework capabilities across Scikit-learn, TensorFlow, and PyTorch to select optimal tools for classical ML vs deep learning projects.

Classical ML vs Deep Learning

10 min

Distinguish between feature-engineered classical ML algorithms and representation-learning deep neural networks based on data scale and complexity.

ML development lifecycle

10 min

Design and execute structured end-to-end machine learning workflows covering data ingestion, preprocessing, training, validation, and deployment.

Dataset → preprocessing → model → training → evaluation → deployment

10 min

Design and execute structured end-to-end machine learning workflows covering data ingestion, preprocessing, training, validation, and deployment.

Training, validation and test datasets

10 min

Build custom PyTorch Dataset and DataLoader classes to handle batching, shuffling, and efficient parallel memory loading.

CPU vs GPU

10 min

Master the core concepts and practical implementation of CPU vs GPU in Python, demonstrating proficiency in introduction to python ml libraries & ml workflow.

Framework selection

10 min

Master the core concepts and practical implementation of Framework selection in Python, demonstrating proficiency in introduction to python ml libraries & ml workflow.

Introduction to Scikit-learn

10 min Preview

Master the core concepts and practical implementation of Introduction to Scikit-learn in Python, demonstrating proficiency in scikit-learn fundamentals.

Scikit-learn architecture

10 min

Master the core concepts and practical implementation of Scikit-learn architecture in Python, demonstrating proficiency in scikit-learn fundamentals.

Estimators

10 min

Master the unified Scikit-learn API pattern, correctly implementing fit(), transform(), and predict() methods across dataset splits.

Transformers

10 min

Master the unified Scikit-learn API pattern, correctly implementing fit(), transform(), and predict() methods across dataset splits.

Predictors

10 min

Master the unified Scikit-learn API pattern, correctly implementing fit(), transform(), and predict() methods across dataset splits.

fit(), transform(), fit_transform(), predict()

10 min

Master the unified Scikit-learn API pattern, correctly implementing fit(), transform(), and predict() methods across dataset splits.

Dataset loading

10 min

Build custom PyTorch Dataset and DataLoader classes to handle batching, shuffling, and efficient parallel memory loading.

Feature matrix and target vector

10 min

Master the core concepts and practical implementation of Feature matrix and target vector in Python, demonstrating proficiency in scikit-learn fundamentals.

Train-test split

10 min

Master the core concepts and practical implementation of Train-test split in Python, demonstrating proficiency in scikit-learn fundamentals.

Missing-value handling

10 min Preview

Master the core concepts and practical implementation of Missing-value handling in Python, demonstrating proficiency in data preprocessing with scikit-learn.

Numerical feature scaling

10 min

Master the core concepts and practical implementation of Numerical feature scaling in Python, demonstrating proficiency in data preprocessing with scikit-learn.

StandardScaler

10 min

Apply feature scaling transformations to prevent magnitude bias and improve convergence speed in gradient-based ML algorithms.

MinMaxScaler

10 min

Apply feature scaling transformations to prevent magnitude bias and improve convergence speed in gradient-based ML algorithms.

Categorical encoding

10 min

Encode nominal and ordinal categorical variables into machine-readable numeric formats using OneHotEncoder and LabelEncoder without data leakage.

OneHotEncoder

10 min

Encode nominal and ordinal categorical variables into machine-readable numeric formats using OneHotEncoder and LabelEncoder without data leakage.

Label encoding

10 min

Encode nominal and ordinal categorical variables into machine-readable numeric formats using OneHotEncoder and LabelEncoder without data leakage.

Feature transformation

10 min

Master the core concepts and practical implementation of Feature transformation in Python, demonstrating proficiency in data preprocessing with scikit-learn.

Feature selection

10 min

Master the core concepts and practical implementation of Feature selection in Python, demonstrating proficiency in data preprocessing with scikit-learn.

ColumnTransformer

10 min

Construct modular Scikit-learn Pipelines and ColumnTransformers to automate preprocessing workflows and ensure repeatable inference.

Pipeline

10 min

Design and execute structured end-to-end machine learning workflows covering data ingestion, preprocessing, training, validation, and deployment.

Preventing data leakage

10 min

Master the core concepts and practical implementation of Preventing data leakage in Python, demonstrating proficiency in data preprocessing with scikit-learn.

Regression

10 min Preview

Implement and evaluate Regression algorithms to predict continuous outputs while managing regularization and complexity.

Linear Regression

10 min

Implement and evaluate Linear Regression algorithms to predict continuous outputs while managing regularization and complexity.

Ridge Regression

10 min

Implement and evaluate Ridge Regression algorithms to predict continuous outputs while managing regularization and complexity.

Lasso Regression

10 min

Implement and evaluate Lasso Regression algorithms to predict continuous outputs while managing regularization and complexity.

Polynomial Regression

10 min

Implement and evaluate Polynomial Regression algorithms to predict continuous outputs while managing regularization and complexity.

Classification

10 min

Build, train, and fine-tune Classification models to classify structured dataset samples into accurate target categories.

Logistic Regression

10 min

Implement and evaluate Logistic Regression algorithms to predict continuous outputs while managing regularization and complexity.

K-Nearest Neighbors

10 min

Master the core concepts and practical implementation of K-Nearest Neighbors in Python, demonstrating proficiency in supervised learning with scikit-learn.

Decision Tree

10 min

Master the core concepts and practical implementation of Decision Tree in Python, demonstrating proficiency in supervised learning with scikit-learn.

Random Forest

10 min

Master the core concepts and practical implementation of Random Forest in Python, demonstrating proficiency in supervised learning with scikit-learn.

Gradient Boosting

10 min

Master the core concepts and practical implementation of Gradient Boosting in Python, demonstrating proficiency in supervised learning with scikit-learn.

Support Vector Machine

10 min

Master the core concepts and practical implementation of Support Vector Machine in Python, demonstrating proficiency in supervised learning with scikit-learn.

Topics

10 min

Master the core concepts and practical implementation of Topics in Python, demonstrating proficiency in supervised learning with scikit-learn.

Hyperparameters

10 min

Implement systematically tuned Hyperparameters experiments using cross-validation and hyperparameter search grids to optimize model performance.

Model selection

10 min

Master the core concepts and practical implementation of Model selection in Python, demonstrating proficiency in supervised learning with scikit-learn.

Prediction

10 min

Master the core concepts and practical implementation of Prediction in Python, demonstrating proficiency in supervised learning with scikit-learn.

Feature importance

10 min

Master the core concepts and practical implementation of Feature importance in Python, demonstrating proficiency in supervised learning with scikit-learn.

Supervised vs unsupervised learning

10 min Preview

Master the core concepts and practical implementation of Supervised vs unsupervised learning in Python, demonstrating proficiency in unsupervised learning with scikit-learn.

Clustering

10 min

Apply Clustering algorithms to cluster high-dimensional data, determine optimal cluster counts, and interpret grouped distributions.

K-Means

10 min

Apply K-Means algorithms to cluster high-dimensional data, determine optimal cluster counts, and interpret grouped distributions.

Hierarchical clustering

10 min

Apply Hierarchical clustering algorithms to cluster high-dimensional data, determine optimal cluster counts, and interpret grouped distributions.

DBSCAN

10 min

Apply DBSCAN algorithms to cluster high-dimensional data, determine optimal cluster counts, and interpret grouped distributions.

Dimensionality reduction

10 min

Perform Principal Component Analysis (PCA) to compress feature dimensions, visualize clusters, and reduce computational overhead.

PCA

10 min

Perform Principal Component Analysis (PCA) to compress feature dimensions, visualize clusters, and reduce computational overhead.

Feature visualization

10 min

Master the core concepts and practical implementation of Feature visualization in Python, demonstrating proficiency in unsupervised learning with scikit-learn.

Regression Metrics

10 min Preview

Implement and evaluate Regression Metrics algorithms to predict continuous outputs while managing regularization and complexity.

MAE

10 min

Master the core concepts and practical implementation of MAE in Python, demonstrating proficiency in model evaluation & optimization.

MSE

10 min

Master the core concepts and practical implementation of MSE in Python, demonstrating proficiency in model evaluation & optimization.

RMSE

10 min

Master the core concepts and practical implementation of RMSE in Python, demonstrating proficiency in model evaluation & optimization.

R²

10 min

Master the core concepts and practical implementation of R² in Python, demonstrating proficiency in model evaluation & optimization.

Classification Metrics

10 min

Build, train, and fine-tune Classification Metrics models to classify structured dataset samples into accurate target categories.

Accuracy

10 min

Master the core concepts and practical implementation of Accuracy in Python, demonstrating proficiency in model evaluation & optimization.

Precision

10 min

Master the core concepts and practical implementation of Precision in Python, demonstrating proficiency in model evaluation & optimization.

Recall

10 min

Master the core concepts and practical implementation of Recall in Python, demonstrating proficiency in model evaluation & optimization.

F1-score

10 min

Master the core concepts and practical implementation of F1-score in Python, demonstrating proficiency in model evaluation & optimization.

Confusion matrix

10 min

Master the core concepts and practical implementation of Confusion matrix in Python, demonstrating proficiency in model evaluation & optimization.

ROC-AUC

10 min

Master the core concepts and practical implementation of ROC-AUC in Python, demonstrating proficiency in model evaluation & optimization.

Advanced Topics

10 min

Master the core concepts and practical implementation of Advanced Topics in Python, demonstrating proficiency in model evaluation & optimization.

Cross-validation

10 min

Implement systematically tuned Cross-validation experiments using cross-validation and hyperparameter search grids to optimize model performance.

K-Fold CV

10 min

Master the core concepts and practical implementation of K-Fold CV in Python, demonstrating proficiency in model evaluation & optimization.

Stratified K-Fold

10 min

Master the core concepts and practical implementation of Stratified K-Fold in Python, demonstrating proficiency in model evaluation & optimization.

GridSearchCV

10 min

Implement systematically tuned GridSearchCV experiments using cross-validation and hyperparameter search grids to optimize model performance.

RandomizedSearchCV

10 min

Master the core concepts and practical implementation of RandomizedSearchCV in Python, demonstrating proficiency in model evaluation & optimization.

Hyperparameter optimization

10 min

Implement systematically tuned Hyperparameter optimization experiments using cross-validation and hyperparameter search grids to optimize model performance.

Bias vs variance

10 min

Diagnose model underfitting and overfitting using learning curves, applying regularization and data strategies to improve generalization.

Overfitting

10 min

Diagnose model underfitting and overfitting using learning curves, applying regularization and data strategies to improve generalization.

Underfitting

10 min

Diagnose model underfitting and overfitting using learning curves, applying regularization and data strategies to improve generalization.

Project - End-to-End Machine Learning Prediction System

10 min Preview

Execute a real-world end-to-end Machine Learning project from data ingestion to evaluation, producing clean, documented code.

Introduction to TensorFlow

10 min Preview

Manipulate multi-dimensional tensors, control shape transformations, and utilize automatic differentiation engines for gradient updates.

TensorFlow architecture

10 min

Manipulate multi-dimensional tensors, control shape transformations, and utilize automatic differentiation engines for gradient updates.

Tensor fundamentals

10 min

Manipulate multi-dimensional tensors, control shape transformations, and utilize automatic differentiation engines for gradient updates.

Tensor shapes

10 min

Manipulate multi-dimensional tensors, control shape transformations, and utilize automatic differentiation engines for gradient updates.

Tensor operations

10 min

Manipulate multi-dimensional tensors, control shape transformations, and utilize automatic differentiation engines for gradient updates.

CPU vs GPU execution

10 min

Master the core concepts and practical implementation of CPU vs GPU execution in Python, demonstrating proficiency in tensorflow & keras fundamentals.

TensorFlow vs Keras

10 min

Manipulate multi-dimensional tensors, control shape transformations, and utilize automatic differentiation engines for gradient updates.

Sequential API

10 min

Design deep neural network architectures using TensorFlow Keras Sequential API, defining custom layers, activations, and connectivity graphs.

Functional API

10 min

Design deep neural network architectures using TensorFlow Keras Functional API, defining custom layers, activations, and connectivity graphs.

Model lifecycle

10 min

Design and execute structured end-to-end machine learning workflows covering data ingestion, preprocessing, training, validation, and deployment.

Artificial neurons

10 min Preview

Master the core concepts and practical implementation of Artificial neurons in Python, demonstrating proficiency in neural networks with tensorflow.

Dense layers

10 min

Design deep neural network architectures using TensorFlow Keras Dense layers, defining custom layers, activations, and connectivity graphs.

Activation functions

10 min

Select and apply Activation functions to introduce non-linearity into neural networks, managing vanishing and exploding gradient challenges.

ReLU

10 min

Select and apply ReLU to introduce non-linearity into neural networks, managing vanishing and exploding gradient challenges.

Sigmoid

10 min

Select and apply Sigmoid to introduce non-linearity into neural networks, managing vanishing and exploding gradient challenges.

Softmax

10 min

Select and apply Softmax to introduce non-linearity into neural networks, managing vanishing and exploding gradient challenges.

Forward propagation

10 min

Master the core concepts and practical implementation of Forward propagation in Python, demonstrating proficiency in neural networks with tensorflow.

Loss functions

10 min

Configure loss functions and gradient descent optimizers, understanding backpropagation mechanics during neural network training.

Optimizers

10 min

Configure loss functions and gradient descent optimizers, understanding backpropagation mechanics during neural network training.

Gradient descent

10 min

Master the core concepts and practical implementation of Gradient descent in Python, demonstrating proficiency in neural networks with tensorflow.

Backpropagation

10 min

Configure loss functions and gradient descent optimizers, understanding backpropagation mechanics during neural network training.

Learning rate

10 min

Master the core concepts and practical implementation of Learning rate in Python, demonstrating proficiency in neural networks with tensorflow.

Batch size

10 min

Master the core concepts and practical implementation of Batch size in Python, demonstrating proficiency in neural networks with tensorflow.

Epochs

10 min

Master the core concepts and practical implementation of Epochs in Python, demonstrating proficiency in neural networks with tensorflow.

Training

10 min

Master the core concepts and practical implementation of Training in Python, demonstrating proficiency in neural networks with tensorflow.

compile()

10 min

Master the core concepts and practical implementation of compile() in Python, demonstrating proficiency in neural networks with tensorflow.

fit()

10 min

Master the core concepts and practical implementation of fit() in Python, demonstrating proficiency in neural networks with tensorflow.

evaluate()

10 min

Master the core concepts and practical implementation of evaluate() in Python, demonstrating proficiency in neural networks with tensorflow.

predict()

10 min

Master the core concepts and practical implementation of predict() in Python, demonstrating proficiency in neural networks with tensorflow.

Callbacks

10 min

Implement training callbacks such as Callbacks to monitor validation loss, prevent overfitting, and save optimal model weights.

EarlyStopping

10 min

Implement training callbacks such as EarlyStopping to monitor validation loss, prevent overfitting, and save optimal model weights.

ModelCheckpoint

10 min

Implement training callbacks such as ModelCheckpoint to monitor validation loss, prevent overfitting, and save optimal model weights.

ReduceLROnPlateau

10 min

Master the core concepts and practical implementation of ReduceLROnPlateau in Python, demonstrating proficiency in neural networks with tensorflow.

Image data representation

10 min Preview

Master the core concepts and practical implementation of Image data representation in Python, demonstrating proficiency in cnn & deep learning with tensorflow.

Convolution

10 min

Construct Convolutional Neural Networks (CNNs) utilizing Convolution to extract spatial features and classify visual image data.

Filters

10 min

Master the core concepts and practical implementation of Filters in Python, demonstrating proficiency in cnn & deep learning with tensorflow.

Feature maps

10 min

Master the core concepts and practical implementation of Feature maps in Python, demonstrating proficiency in cnn & deep learning with tensorflow.

Pooling

10 min

Construct Convolutional Neural Networks (CNNs) utilizing Pooling to extract spatial features and classify visual image data.

Flattening

10 min

Master the core concepts and practical implementation of Flattening in Python, demonstrating proficiency in cnn & deep learning with tensorflow.

CNN architecture

10 min

Construct Convolutional Neural Networks (CNNs) utilizing CNN architecture to extract spatial features and classify visual image data.

Data augmentation

10 min

Master the core concepts and practical implementation of Data augmentation in Python, demonstrating proficiency in cnn & deep learning with tensorflow.

Image classification

10 min

Build, train, and fine-tune Image classification models to classify structured dataset samples into accurate target categories.

Model evaluation

10 min Preview

Master the core concepts and practical implementation of Model evaluation in Python, demonstrating proficiency in cnn & deep learning with tensorflow.

TensorBoard

10 min

Manipulate multi-dimensional tensors, control shape transformations, and utilize automatic differentiation engines for gradient updates.

Model saving/loading

10 min

Master the core concepts and practical implementation of Model saving/loading in Python, demonstrating proficiency in tensorflow training, evaluation & deployment.

Checkpoints

10 min

Master the core concepts and practical implementation of Checkpoints in Python, demonstrating proficiency in tensorflow training, evaluation & deployment.

Inference

10 min

Master the core concepts and practical implementation of Inference in Python, demonstrating proficiency in tensorflow training, evaluation & deployment.

Batch prediction

10 min

Master the core concepts and practical implementation of Batch prediction in Python, demonstrating proficiency in tensorflow training, evaluation & deployment.

TensorFlow SavedModel

10 min

Manipulate multi-dimensional tensors, control shape transformations, and utilize automatic differentiation engines for gradient updates.

Basic deployment concepts

10 min

Export trained deep learning models to production formats using Basic deployment concepts for fast inference and web application integration.

TensorFlow Lite overview

10 min

Manipulate multi-dimensional tensors, control shape transformations, and utilize automatic differentiation engines for gradient updates.

Why PyTorch?

10 min Preview

Master the core concepts and practical implementation of Why PyTorch? in Python, demonstrating proficiency in pytorch fundamentals.

PyTorch architecture

10 min

Master the core concepts and practical implementation of PyTorch architecture in Python, demonstrating proficiency in pytorch fundamentals.

Tensors

10 min

Manipulate multi-dimensional tensors, control shape transformations, and utilize automatic differentiation engines for gradient updates.

Tensor operations

10 min

Manipulate multi-dimensional tensors, control shape transformations, and utilize automatic differentiation engines for gradient updates.

Tensor shapes

10 min

Manipulate multi-dimensional tensors, control shape transformations, and utilize automatic differentiation engines for gradient updates.

CPU/GPU

10 min

Master the core concepts and practical implementation of CPU/GPU in Python, demonstrating proficiency in pytorch fundamentals.

Device management

10 min

Master the core concepts and practical implementation of Device management in Python, demonstrating proficiency in pytorch fundamentals.

Autograd

10 min

Manipulate multi-dimensional tensors, control shape transformations, and utilize automatic differentiation engines for gradient updates.

Computational graphs

10 min

Master the core concepts and practical implementation of Computational graphs in Python, demonstrating proficiency in pytorch fundamentals.

PyTorch Core APIs

10 min

Master the core concepts and practical implementation of PyTorch Core APIs in Python, demonstrating proficiency in pytorch fundamentals.

torch

10 min

Master the core concepts and practical implementation of torch in Python, demonstrating proficiency in pytorch fundamentals.

torch.nn

10 min

Master the core concepts and practical implementation of torch.nn in Python, demonstrating proficiency in pytorch fundamentals.

torch.optim

10 min

Master the core concepts and practical implementation of torch.optim in Python, demonstrating proficiency in pytorch fundamentals.

torch.utils.data

10 min

Master the core concepts and practical implementation of torch.utils.data in Python, demonstrating proficiency in pytorch fundamentals.

Neural network Development : nn.Module

10 min Preview

Develop custom PyTorch neural network modules inheriting from nn.Module, managing forward propagation and parameter updates.

Layers

10 min

Master the core concepts and practical implementation of Layers in Python, demonstrating proficiency in neural network development with pytorch.

Activation functions

10 min

Select and apply Activation functions to introduce non-linearity into neural networks, managing vanishing and exploding gradient challenges.

Loss functions

10 min

Configure loss functions and gradient descent optimizers, understanding backpropagation mechanics during neural network training.

Optimizers

10 min

Configure loss functions and gradient descent optimizers, understanding backpropagation mechanics during neural network training.

Forward pass

10 min

Develop custom PyTorch neural network modules inheriting from nn.Module, managing forward propagation and parameter updates.

Backward pass

10 min

Develop custom PyTorch neural network modules inheriting from nn.Module, managing forward propagation and parameter updates.

Gradient calculation

10 min

Master the core concepts and practical implementation of Gradient calculation in Python, demonstrating proficiency in neural network development with pytorch.

optimizer.zero_grad()

10 min

Master the core concepts and practical implementation of optimizer.zero_grad() in Python, demonstrating proficiency in neural network development with pytorch.

loss.backward()

10 min

Master the core concepts and practical implementation of loss.backward() in Python, demonstrating proficiency in neural network development with pytorch.

optimizer.step()

10 min

Master the core concepts and practical implementation of optimizer.step() in Python, demonstrating proficiency in neural network development with pytorch.

Data Handling

10 min

Master the core concepts and practical implementation of Data Handling in Python, demonstrating proficiency in neural network development with pytorch.

Dataset

10 min

Build custom PyTorch Dataset and DataLoader classes to handle batching, shuffling, and efficient parallel memory loading.

DataLoader

10 min

Build custom PyTorch Dataset and DataLoader classes to handle batching, shuffling, and efficient parallel memory loading.

Batch processing

10 min

Master the core concepts and practical implementation of Batch processing in Python, demonstrating proficiency in neural network development with pytorch.

Shuffling

10 min

Master the core concepts and practical implementation of Shuffling in Python, demonstrating proficiency in neural network development with pytorch.

CNN with PyTorch

10 min Preview

Construct Convolutional Neural Networks (CNNs) utilizing CNN with PyTorch to extract spatial features and classify visual image data.

Convolutional layers

10 min

Construct Convolutional Neural Networks (CNNs) utilizing Convolutional layers to extract spatial features and classify visual image data.

Pooling

10 min

Construct Convolutional Neural Networks (CNNs) utilizing Pooling to extract spatial features and classify visual image data.

Batch normalization

10 min

Master the core concepts and practical implementation of Batch normalization in Python, demonstrating proficiency in cnn, transfer learning & advanced pytorch.

Dropout

10 min

Master the core concepts and practical implementation of Dropout in Python, demonstrating proficiency in cnn, transfer learning & advanced pytorch.

Transfer learning

10 min

Implement transfer learning with pretrained neural network backbones, freezing base layers and fine-tuning head layers for target domains.

Pre-trained models

10 min

Implement transfer learning with pretrained neural network backbones, freezing base layers and fine-tuning head layers for target domains.

Fine-tuning

10 min

Implement transfer learning with pretrained neural network backbones, freezing base layers and fine-tuning head layers for target domains.

Freezing/unfreezing layers

10 min

Master the core concepts and practical implementation of Freezing/unfreezing layers in Python, demonstrating proficiency in cnn, transfer learning & advanced pytorch.

Production training structure

10 min Preview

Master the core concepts and practical implementation of Production training structure in Python, demonstrating proficiency in pytorch training pipeline & model deployment.

GPU training

10 min

Master the core concepts and practical implementation of GPU training in Python, demonstrating proficiency in pytorch training pipeline & model deployment.

Checkpointing

10 min

Master the core concepts and practical implementation of Checkpointing in Python, demonstrating proficiency in pytorch training pipeline & model deployment.

Model persistence

10 min

Master the core concepts and practical implementation of Model persistence in Python, demonstrating proficiency in pytorch training pipeline & model deployment.

Training/validation loops

10 min

Master the core concepts and practical implementation of Training/validation loops in Python, demonstrating proficiency in pytorch training pipeline & model deployment.

Inference pipeline

10 min

Design and execute structured end-to-end machine learning workflows covering data ingestion, preprocessing, training, validation, and deployment.

Model optimization concepts

10 min

Master the core concepts and practical implementation of Model optimization concepts in Python, demonstrating proficiency in pytorch training pipeline & model deployment.

TorchScript / deployment concepts

10 min

Export trained deep learning models to production formats using TorchScript / deployment concepts for fast inference and web application integration.

PyTorch ecosystem overview

10 min

Master the core concepts and practical implementation of PyTorch ecosystem overview in Python, demonstrating proficiency in pytorch training pipeline & model deployment.

Students select one problem and implement an end-to-end solution.

10 min Preview

Execute a real-world end-to-end Machine Learning project from data ingestion to evaluation, producing clean, documented code.

Project 1 — Student Performance Prediction Scikit-learn: preprocessing regression/classification evaluation hyperparameter tuning

10 min

Execute a real-world end-to-end Machine Learning project from data ingestion to evaluation, producing clean, documented code.

Project 2 — Image Classification TensorFlow/PyTorch: dataset preparation CNN training validation inference

10 min

Build, train, and fine-tune Project 2 — Image Classification with TensorFlow and PyTorch models to classify structured dataset samples into accurate target categories.

Project 3 — Customer Segmentation: Scikit-learn: clustering PCA visualization business interpretation

10 min

Perform Principal Component Analysis (PCA) to compress feature dimensions, visualize clusters, and reduce computational overhead.

Project 4 — Transfer Learning Image Classifier: PyTorch: pretrained model fine-tuning evaluation inference

10 min

Implement transfer learning with pretrained neural network backbones, freezing base layers and fine-tuning head layers for target domains.

In case you are thinking of beginning your IT career, Machine Learning Lib - Scikit TensorFlow is the best course program to start and our Machine Learning Scikit Tensorflow in Sector 69 Tulip Chowk, Gurgaon is structured in such a way that you will be job ready on the first day after course completion.

Course Description

Prerequisites

Basic Programming Knowledge

Familiarity With Coding (Recommended)

Willingness To Learn And Practice

Skills You'll Gain

Machine Learning; Model Training; Ai Fundamentals

Your Instructors

Manoj Yadav

Lead Instructor

Tech Consultant

15+ Years Experience in Python,Unix,Database ,AI, ML, And Backend Systems For Developing Solutions. Total Expereince in teaching 15

⭐ 4.1 (121 Reviews)

👨‍🎓 1200 Students

📚 12 Courses

Student Reviews

4.9 (5 Rating or 4.8)

Manish Kumar

★ ★ ★ ★ ★

"Hands-on labs made Scikit-learn workflows and TensorFlow model building easy to understand. The trainer explained concepts clearly, demonstrated practical pipelines, and encouraged experimentation. Excellent exposure to model training, evaluation, tuning, and deployment practices that strengthen job readiness."

Amit Sharma

★ ★ ★ ★ ★

"A useful learning experience covering preprocessing, feature engineering, supervised learning, neural networks, and evaluation. Teaching remained structured and industry-focused. Real exercises improved confidence in developing complete machine learning solutions rather than only studying theoretical algorithms."

Dipesh Pandey

★ ★ ★ ★ ★

"The course delivers practical machine learning knowledge through meaningful coding assignments and demonstrations. Trainer guidance was approachable and technically strong. Working with Scikit-learn and TensorFlow provided valuable exposure to tools commonly expected in modern data science and AI roles."

Fateh Singh

★ ★ ★ ★ ☆

"Training combined clear explanations with practical implementation. Projects helped connect algorithms with business-oriented machine learning scenarios. The institute created a supportive environment where questions were welcomed, while hands-on TensorFlow practice added considerable value for aspiring ML professionals."

Niharika Singh

★ ★ ★ ★ ★

"Well-organized sessions provide a strong pathway from fundamentals toward practical model development. Exercises, trainer feedback, and framework exposure make the learning relevant to professional work. Particularly valuable for learners seeking confidence with Scikit-learn pipelines and TensorFlow applications."

FAQ

🔹 Is this course available online or offline?

Yes, course is available in both modes.

🔹 Certificate of completion?

Yes, shareable digital certificate.

🔹 Can i make fee payments in parts ?

Yes, Few basis , we allow candidates to pay fee in parts.

🔹 Projects included?

5 major minor portfolio projects.

🔹 Can you provide guaranteed placement after the course ?

Yes, we provide you the assured placement. we have a dedicated team for placement assistance.

Career Outcomes

Career outcomes will be added soon.

₹500 *Off on one-time payment

✨ 5-day money-back guarantee

Includes free 2-day preview session

Personalized Learning Roadmap
AI-Powered Learning Assistant
Hands-on Projects
Job-Ready Skills
Verified Certificate
24/7 Q&A/ Project Support

Course Features

51 Hrs.+ Sessions

48+ Exercises

Resources Kit

Daily Quiz-Assignments

Earn a Verified Certificate

Shareable on LinkedIn & Resume

Industry Recognized Verified by BMLC

❤️ We Care & Build Your Confidence

1-on-1 Mentorship: Dedicated professional mentors guide you through every challenge.

Peer Learning Community: Join 1k+ learners in weekly code reviews.

Confidence Booster: Project-based learning with real-world simulations.

🎯 Latest Job Matches For You

Python Developer

TechCorp • Remote

New

AI Engineer

InnovateAI • Bangalore

Urgent

Backend Architect

ScaleUp • Hybrid

Top Match

💰 Salary Earned By Developers

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Upcoming Course Events

Live & Interactive-Offline

Oct

Coding session

Agentic AI Session

Praveen Y

01:00 PM - 01:00 PM

Build Agents Quickly

45 attending

*All events are recorded and available for enrolled students

Machine Learning Lib - Scikit Tensorflow