Machine Learning with Python Course in Jaipur
Build a practical machine-learning foundation in Python by preparing data, training models, evaluating predictions and documenting useful applications.
- Practical learning
- Project practice
- Jaipur
Machine Learning with Python
Build your foundation in Machine Learning with Python
Build a practical machine-learning foundation in Python by preparing data, training models, evaluating predictions and documenting useful applications.
Begin with Python, NumPy and Pandas, then prepare and explore datasets using statistics, Matplotlib and Seaborn. Use Scikit-learn to build regression, classification, ensemble and clustering models.
Practise feature preparation, dimensionality reduction, cross validation, evaluation metrics and hyperparameter tuning. The learning path also introduces TensorFlow and Keras, NLP and computer vision basics, prediction APIs and deployment concepts.
Who can join?
Python/data learners Data analytics students moving into ML Students interested in predictive modelling Learners building a machine-learning portfolio
Skills and tools
Course curriculum
Explore the published learning outline below. Confirm the detailed syllabus and module coverage for your chosen batch with the course team.
Introduction to Machine Learning
Understand learning types, real-world applications and the machine-learning workflow.
- Introduction to Machine Learning
- Machine Learning vs Artificial Intelligence
- Machine Learning vs Data Science
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning Overview
- Real-World Machine Learning Applications
- Machine Learning Workflow
- Setting Up the Machine Learning Environment
Python for Machine Learning
Use Python, NumPy and Pandas to load and work with tabular data.
- Python Fundamentals for Machine Learning
- Variables and Data Types
- Conditional Statements and Loops
- Functions and Modules
- Lists, Tuples and Dictionaries
- Introduction to NumPy
- NumPy Arrays and Operations
- Introduction to Pandas
- Pandas DataFrames
- Working with CSV and Excel Data
Data Preprocessing
Prepare datasets with missing-value handling, encoding, scaling, outlier review and data splitting.
- Introduction to Data Preprocessing
- Understanding Dataset Structure
- Handling Missing Values
- Removing Duplicate Data
- Encoding Categorical Data
- Feature Scaling
- Normalization and Standardization
- Detecting and Handling Outliers
- Data Splitting
- Data Preprocessing Project
Exploratory Data Analysis
Explore distributions and relationships with statistics, Matplotlib and Seaborn.
- Introduction to Exploratory Data Analysis
- Descriptive Statistics
- Data Distribution Analysis
- Correlation Analysis
- Feature Relationships
- Matplotlib Fundamentals
- Seaborn Fundamentals
- Creating Business and ML Visualizations
- Identifying Patterns in Data
- Exploratory Data Analysis Project
Machine Learning with Scikit-learn
Use Scikit-learn to train, evaluate, save and load machine-learning models.
- Introduction to Scikit-learn
- Understanding the Scikit-learn Workflow
- Loading Datasets
- Feature and Target Variables
- Training Machine Learning Models
- Making Predictions
- Train-Test Split
- Cross Validation
- Saving and Loading Models
- Scikit-learn Practice Project
Regression Algorithms
Build linear and regularized regression models and compare their prediction errors.
- Introduction to Regression
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Ridge Regression
- Lasso Regression
- Regression Model Evaluation
- Mean Absolute Error
- Mean Squared Error and RMSE
- Regression Project
Classification Algorithms
Explore classification algorithms and review results with suitable metrics and reports.
- Introduction to Classification
- Logistic Regression
- K-Nearest Neighbors
- Naive Bayes
- Support Vector Machines
- Decision Tree Classification
- Random Forest Classification
- Classification Model Evaluation
- Confusion Matrix and Classification Report
- Classification Project
Ensemble Learning
Compare bagging, boosting and forest models, then practise hyperparameter tuning.
- Introduction to Ensemble Learning
- Bagging
- Boosting
- Random Forest
- AdaBoost
- Gradient Boosting
- XGBoost Concepts
- Comparing Ensemble Models
- Hyperparameter Tuning
- Ensemble Learning Project
Unsupervised Learning
Create and evaluate customer segments with clustering methods.
- Introduction to Unsupervised Learning
- Clustering Concepts
- K-Means Clustering
- Choosing the Optimal Number of Clusters
- Hierarchical Clustering
- DBSCAN Overview
- Customer Segmentation
- Cluster Evaluation
- Practical Clustering Applications
- Unsupervised Learning Project
Dimensionality Reduction
Study feature selection, extraction and PCA for high-dimensional datasets.
- Introduction to Dimensionality Reduction
- High-Dimensional Data
- Feature Selection
- Feature Extraction
- Principal Component Analysis
- PCA Implementation
- Explained Variance
- Dimensionality Reduction for Visualization
- Improving Model Performance
- PCA Project
Model Evaluation and Optimization
Evaluate predictions, validation results, overfitting and optimization choices.
- Introduction to Model Evaluation
- Accuracy and Error Analysis
- Precision and Recall
- F1 Score
- ROC Curve and AUC
- Cross Validation Techniques
- Overfitting and Underfitting
- Bias and Variance
- Hyperparameter Tuning
- Grid Search and Random Search
Deep Learning Fundamentals
Introduce neural networks, TensorFlow, Keras and a practical deep-learning project.
- Introduction to Deep Learning
- Artificial Neural Networks
- Neurons and Activation Functions
- Forward and Backpropagation
- Loss Functions
- Optimizers
- Introduction to TensorFlow
- Introduction to Keras
- Building a Neural Network
- Deep Learning Project
NLP and Computer Vision Basics
Explore text preprocessing, sentiment analysis, image data and introductory vision workflows.
- Introduction to Natural Language Processing
- Text Preprocessing
- Tokenization and Stop Words
- Text Vectorization
- Sentiment Analysis
- Introduction to Computer Vision
- Image Data Fundamentals
- Image Classification Concepts
- Machine Learning Applications in NLP and Vision
- NLP or Computer Vision Mini Project
Machine Learning Deployment
Save models, create prediction APIs with Flask and validate model inputs and outputs.
- Introduction to Model Deployment
- Saving Trained Models
- Pickle and Joblib
- Creating Prediction APIs
- Introduction to Flask
- Integrating Machine Learning Models with APIs
- Model Input and Output Validation
- Basic Deployment Workflow
- Introduction to Cloud Deployment Concepts
- Machine Learning Deployment Project
Live Projects and Capstone Project
Apply the curriculum through prediction, clustering, text-classification and deployment projects.
- House Price Prediction Project
- Sales Forecasting Project
- Customer Churn Prediction
- Customer Segmentation Project
- Loan Prediction Project
- Spam or Sentiment Classification
- Recommendation System Concepts
- Machine Learning Model Deployment Project
- End-to-End Machine Learning Project
- Final Machine Learning Capstone Project
Project practice ideas
Use these examples to discuss suitable practice work with your trainer.
Regression prediction project
Build a house-price or sales prediction model and compare its prediction errors.
Classification and churn project
Train a customer-churn or loan-prediction classifier and review metrics and the confusion matrix.
Customer segmentation study
Use clustering to identify customer segments and evaluate the resulting groups.
Text classification project
Prepare text data and build a spam or sentiment-classification workflow.
Model-evaluation comparison
Compare models using suitable validation and evaluation methods, and document their limitations.
Machine-learning deployment capstone
Combine data preparation and modelling with a prediction API, input validation and a documented end-to-end workflow.
Frequently asked questions
Do I need Python first?
Basic Python and data-handling skills are recommended. The source curriculum also begins with Python and data fundamentals before moving to modelling.
Is mathematics required?
Basic statistics and algebra help; concepts can be introduced alongside practical modelling.
Are real datasets used?
The practical path includes dataset preparation, exploratory analysis, modelling and evaluation projects.
What should I learn before deep learning?
A solid foundation in Python, data analysis, statistics and classical machine learning is useful first.
Which technologies are covered?
The curriculum includes Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, Keras and introductory Flask deployment workflows.
Does the course include model deployment?
Yes. The source outline includes saving models with Pickle and Joblib, creating prediction APIs, validating model inputs and outputs and introductory cloud deployment concepts.
Who is this course for?
Python/data learners Data analytics students moving into ML Students interested in predictive modelling Learners building a machine-learning portfolio
How can I confirm fees, duration and batch timings?
Contact Groot Academy for the current syllabus, fees, duration and available learning modes before enrolling. These details depend on the selected course and batch.
Ready to discuss Machine Learning with Python?
Ask the course team about syllabus coverage, current batch timings, fees, duration and learning modes before you decide.