AI & Machine Learning Internship - 1 Month Training Program
Join 1 Month AI & Machine Learning Internship at NextVision Infotech. Master Python, NumPy, Pandas, Supervised Learning, Unsupervised Learning, Model Evaluation. ₹399 only with certificate.
Program Description
Skills You'll Learn
Projects You'll Work On
Complete AI & Machine Learning Project (1 Month)
Students will complete an ML project including data preprocessing, exploratory data analysis, model building (supervised and unsupervised), model evaluation, hyperparameter tuning, and complete project documentation.
Program Modules
W1: Week 1: Python for ML, NumPy, Pandas & Data Visualization
Introduction to AI and Machine Learning, AI vs ML vs DL, types of ML - supervised, unsupervised, reinforcement learning, Python fundamentals for ML, Python syntax, data types, control flow, functions, lists, dictionaries, NumPy fundamentals, NumPy arrays, indexing, slicing, reshaping, broadcasting, mathematical operations, Pandas fundamentals, Series, DataFrame, data loading - CSV, Excel, JSON, data inspection, data selection, filtering, sorting, grouping, aggregation, merging, joining, data visualization - Matplotlib, line plots, bar plots, histograms, scatter plots, box plots, heatmaps, Seaborn, statistical data visualization.
W2: Week 2: Data Preprocessing, Feature Engineering & Supervised Learning
Data cleaning, handling missing values, imputation techniques, outlier detection, feature scaling - normalization, standardization, encoding categorical variables - label encoding, one-hot encoding, feature engineering, feature selection, train-test split, cross-validation, supervised learning fundamentals, regression - linear regression, multiple regression, polynomial regression, regularization - Ridge, Lasso, classification - logistic regression, K-Nearest Neighbors (KNN), decision trees, random forests, support vector machines (SVM), Naive Bayes, confusion matrix, precision, recall, F1-score, ROC-AUC, scikit-learn, Jupyter Notebook, Google Colab.
W3: Week 3: Unsupervised Learning, Model Evaluation & Hyperparameter Tuning
Unsupervised learning fundamentals, clustering - K-Means, hierarchical clustering, DBSCAN, dimensionality reduction - PCA (Principal Component Analysis), t-SNE, association rules - Apriori, model evaluation techniques, regression metrics - MSE, RMSE, MAE, R-squared, classification metrics - accuracy, precision, recall, F1-score, ROC curve, AUC, cross-validation techniques - K-Fold, Stratified K-Fold, Leave-One-Out, hyperparameter tuning - GridSearchCV, RandomizedSearchCV, bias-variance tradeoff, overfitting and underfitting, regularization techniques.
W4: Week 4: Introduction to Deep Learning, Live Projects & Certification
Introduction to deep learning, neural networks basics, perceptron, multilayer perceptron, activation functions - ReLU, sigmoid, tanh, loss functions, backpropagation, gradient descent, optimization algorithms - SGD, Adam, TensorFlow, Keras, PyTorch basics, live projects - house price prediction, customer churn prediction, iris flower classification, sales forecasting, complete documentation, resume building, interview preparation, viva-voce examination, 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./B.Com/MBA in any discipline
- Basic knowledge of Python programming
- Basic knowledge of mathematics and statistics
- Interest in AI and Machine Learning
Who Can Enroll ?
- B.Tech Computer Science students (1st to 8th semester)
- BCA/MCA students
- B.Sc. students
- AI/ML aspirants
- Data science aspirants
- Working professionals wanting to upskill
- Students from all over India seeking practical training
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