Machine Learning with Python - 45 Days Internship Training Program
Join 45 Days Machine Learning with Python Internship Training Program at NextVision Infotech. Learn Python, NumPy, Pandas, Scikit-learn, ML algorithms. ₹649 only with certificate.
Program Description
Skills You'll Learn
Projects You'll Work On
Complete Machine Learning Project
Students will complete a full machine learning project including data collection, preprocessing, exploratory data analysis, feature engineering, model building, hyperparameter tuning, model evaluation, deployment, and complete project documentation as per industry standards.
Program Modules
W1: Week 1: Python for Data Science and NumPy
Introduction to machine learning, types of machine learning - supervised, unsupervised, reinforcement learning, Python for data science, Python data types, control flow, functions, object-oriented programming, NumPy fundamentals, NumPy arrays, array creation, indexing, slicing, reshaping, broadcasting, mathematical operations, statistical operations, linear algebra operations, random number generation, performance optimization, Jupyter Notebook, Google Colab, Anaconda, virtual environments.
W2: Week 2: Pandas and Data Visualization
Pandas fundamentals, Series, DataFrame, data loading - CSV, Excel, JSON, SQL, data inspection, data selection, indexing, filtering, sorting, grouping, aggregation, merging, joining, concatenation, pivot tables, cross-tabulation, time series analysis, date and time handling, data cleaning, handling missing values, data transformation, Matplotlib fundamentals, line plots, bar plots, histograms, scatter plots, pie charts, box plots, heatmaps, Seaborn, statistical data visualization, interactive visualization with Plotly.
W3: Week 3: Data Preprocessing and Exploratory Data Analysis
Data preprocessing, data cleaning, handling missing values - imputation techniques, outlier detection and treatment, feature scaling - normalization, standardization, robust scaling, encoding categorical variables - label encoding, one-hot encoding, ordinal encoding, target encoding, feature engineering, feature creation, feature transformation, feature selection - filter methods, wrapper methods, embedded methods, dimensionality reduction - PCA, t-SNE, LDA, exploratory data analysis (EDA), univariate analysis, bivariate analysis, multivariate analysis, correlation analysis, statistical hypothesis testing, data profiling.
W4: Week 4: Supervised Learning Algorithms
Supervised learning fundamentals, regression - linear regression, polynomial regression, ridge regression, lasso regression, elastic net, logistic regression, classification - k-nearest neighbors (KNN), decision trees, random forests, support vector machines (SVM), naive Bayes, gradient boosting, XGBoost, LightGBM, CatBoost, ensemble methods - bagging, boosting, stacking, voting, model evaluation metrics - regression metrics (MSE, RMSE, MAE, R²), classification metrics (accuracy, precision, recall, F1-score, ROC-AUC, confusion matrix), cross-validation, k-fold cross-validation, stratified k-fold, leave-one-out, hyperparameter tuning - grid search, random search, Bayesian optimization.
W5: Week 5: Unsupervised Learning, Model Evaluation, and Live Projects
Unsupervised learning fundamentals, clustering - k-means, hierarchical clustering, DBSCAN, Gaussian mixture models, association rule mining - Apriori, FP-Growth, market basket analysis, anomaly detection, isolation forest, one-class SVM, model selection, bias-variance tradeoff, underfitting, overfitting, regularization - L1, L2, dropout, early stopping, model interpretability - SHAP, LIME, feature importance, live projects - house price prediction, customer churn prediction, credit card fraud detection, customer segmentation, recommendation system, complete documentation.
W6: Week 6: Model Deployment, Documentation, Viva, and Certification
Model deployment, model serialization - pickle, joblib, ONNX, Flask API, FastAPI, Streamlit, Gradio, cloud deployment - AWS, GCP, Azure, Heroku, Docker basics, MLOps fundamentals, model monitoring, version control with Git and GitHub, project documentation, resume building, interview preparation, viva-voce examination, feedback session, certificate distribution.
Key Features
What You'll Learn
Examination Details
| Exam Name | Full Marks | Pass Marks |
|---|---|---|
| Module-wise Assessment | 100 | 40 |
| Project Evaluation | 100 | 50 |
| Final Presentation | 50 | 25 |
| Viva-Voce | 50 | 25 |
Internship Details
Prerequisites
- Pursuing or completed B.Tech/B.E./BCA/MCA/B.Sc. in Computer Science, IT, or related field
- Basic knowledge of programming concepts
- Basic mathematics and statistics knowledge
- Interest in machine learning
Who Can Enroll ?
- B.Tech Computer Science students (1st to 8th semester)
- B.E. IT students
- BCA students (1st to 6th semester)
- MCA students
- B.Sc. Computer Science students
- Data science aspirants
- Working professionals wanting to upskill
- Students from all over India seeking practical training
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