What Will You Learn?
- Python programming fundamentals for AI and machine learning.
- Introduction to artificial intelligence and machine learning concepts.
- Data collection, preprocessing, and exploratory data analysis.
- NumPy, Pandas, and data visualization using Python.
- Basic statistics and mathematics for machine learning.
- Supervised and unsupervised machine learning algorithms.
- Model training, testing, evaluation, and prediction.
- Introduction to deep learning and neural networks.
- Basics of Natural Language Processing (NLP) and Generative AI.
- Practical AI and machine learning projects.
By the end of the internship, you will have a better understanding of AI and ML workflows and the foundational skills needed to develop data-driven applications.
AI & Machine Learning Internship Training Curriculum
Our AI & ML Internship curriculum introduces essential programming and data concepts before progressing toward machine learning algorithms and practical AI applications.
1. Python Programming Fundamentals
- Introduction to Python programming.
- Variables, data types, operators, and expressions.
- Conditional statements and loops.
- Functions, modules, and packages.
- Lists, tuples, dictionaries, and sets.
- Object-oriented programming fundamentals.
- Exception handling and file operations.
2. Introduction to Artificial Intelligence and Machine Learning
- Understanding Artificial Intelligence and its applications.
- Introduction to Machine Learning and its types.
- Differences between AI, ML, and Deep Learning.
- Understanding datasets, features, and target variables.
- Introduction to machine learning workflows.
- Real-world applications of AI and ML.
- Understanding responsible AI and data privacy.
3. Data Analysis and Visualization
- Introduction to data analysis using Python.
- Working with NumPy arrays and numerical operations.
- Data manipulation using Pandas.
- Loading and processing CSV and structured datasets.
- Handling missing values and duplicate records.
- Exploratory data analysis.
- Data visualization using Matplotlib and Seaborn.
- Identifying patterns, trends, and relationships in data.
4. Mathematics and Statistics for Machine Learning
- Introduction to descriptive statistics.
- Mean, median, mode, and standard deviation.
- Understanding probability fundamentals.
- Introduction to vectors and matrices.
- Understanding correlation and data distributions.
- Basic concepts of linear algebra.
- Understanding how mathematical concepts support ML algorithms.
5. Supervised Machine Learning
- Introduction to supervised learning.
- Understanding regression and classification.
- Linear regression and prediction.
- Logistic regression fundamentals.
- Decision trees and random forests.
- Introduction to K-Nearest Neighbors (KNN).
- Training and testing datasets.
- Evaluating model performance using suitable metrics.
6. Unsupervised Machine Learning
- Introduction to unsupervised learning.
- Understanding clustering techniques.
- K-Means clustering.
- Introduction to dimensionality reduction.
- Understanding patterns in unlabeled datasets.
- Customer segmentation and similar use cases.
- Comparing and interpreting clustering results.
7. Machine Learning Model Development
- Preparing datasets for model training.
- Feature selection and feature engineering basics.
- Splitting data into training and testing sets.
- Model training and prediction.
- Evaluating model accuracy and performance.
- Understanding overfitting and underfitting.
- Introduction to model optimization.
- Saving and reusing trained models.
8. Introduction to Deep Learning and Neural Networks
- Understanding deep learning concepts.
- Introduction to artificial neural networks.
- Understanding neurons, layers, and activation functions.
- Introduction to TensorFlow or PyTorch.
- Understanding basic training workflows.
- Introduction to image and text processing applications.
- Exploring practical deep learning use cases.
9. Natural Language Processing and Generative AI
- Introduction to Natural Language Processing (NLP).
- Understanding text preprocessing and tokenization.
- Basic text classification and sentiment analysis.
- Introduction to large language models (LLMs).
- Understanding Generative AI applications.
- Introduction to prompt engineering.
- Exploring AI-powered chatbots and text generation.
- Understanding AI limitations, privacy, and responsible use.
10. Practical AI and Machine Learning Project Development
- Identifying a problem suitable for an AI or ML solution.
- Collecting and preparing a suitable dataset.
- Performing exploratory data analysis.
- Selecting an appropriate machine learning algorithm.
- Training and evaluating a model.
- Generating predictions and interpreting results.
- Presenting project findings and documenting the workflow.
- The curriculum may be adjusted according to your current knowledge, learning objectives, and the internship program available at Akash Technolabs.