AI/ML Engineer Training Program

Here's what you'll learn:

• Database Fundamentals & Management Systems (DBMS):
  • Explore different types of databases (relational, NoSQL) and understand data organization, storage, and retrieval.
  • Learn about Database Management Systems (DBMS) like SQL Server and MySQL, their roles, and how they manage data.
  • Understand data models, including conceptual, logical, and physical data models.
  • Get hands-on by installing and configuring a chosen database environment (e.g., Microsoft SQL Server/SSMS) to practice throughout the module.
• SQL Programming Essentials:
  • Master writing powerful SQL queries, progressing from basics to advanced scenarios.
  • Joins: Inner, Left, Right, Full, and Self-Joins for combining data from multiple tables.
  • Subqueries: Utilize nested queries for complex data retrieval.
  • Aggregations: Apply functions like COUNT, SUM, AVG, MIN, MAX with GROUP BY and HAVING clauses.
  • Window Functions: Perform calculations across related rows (e.g., ROW_NUMBER, RANK, LEAD, LAG).
• Database Design & Optimization Principles:
  • Learn to design efficient and well-structured databases for practical applications.
  • Entity-Relationship (ER) Modeling: Create ER diagrams to visually represent database structures.
  • Normalization (1NF, 2NF, 3NF, BCNF): Understand how to reduce data redundancy and improve data integrity.
  • Indexing Strategies: Optimize query performance and data retrieval speed.
  • Views & Stored Procedures: Create virtual tables and pre-compiled SQL code for enhanced security, reusability, and efficiency.
• Leveraging AI for Developer Productivity & Learning:
  • Learn to effectively utilize modern AI tools, including AI chatbots like ChatGPT and Gemini, to accelerate research, understand complex technical concepts, and streamline problem-solving.
  • Additionally, explore AI coding assistants such as GitHub Copilot and Gemini, understanding how they can significantly boost your efficiency in writing, debugging, and understanding code (including SQL scripts), thereby enhancing your overall development workflow and learning process from day one.

Here's what you'll learn:

• Python Programming Fundamentals:
  • Build a strong foundation in Python, understanding its syntax, how to write logical code, use different data structures (like lists, dictionaries, tuples, sets), control flow (if/else, loops), functions, and create reusable scripts.
  • Learn to work with Python libraries and packages, including how to install and manage them using pip.
  • Understand how to read and write files in Python, including text files and CSV files.
  • Get familiar with Python's object-oriented programming (OOP) concepts, including classes, objects, inheritance, and polymorphism.
• Data Manipulation with Pandas & NumPy: Master powerful Python libraries for efficient data handling and manipulation:
  • NumPy: Learn about ndarray for numerical operations, array broadcasting, and vectorized computations for performance.
  • Pandas: Master DataFrames and Series for data manipulation, including reading/writing data from/to various formats (CSV, Excel, JSON).
  • Data Transformation: Merging, joining, reshaping, and aggregating data for analysis.
• Creating Data Visualizations: Learn to make impactful charts and graphs, effectively communicating your findings:
  • Matplotlib: Fundamentals of creating static, interactive, and animated visualizations, including line plots, scatter plots, bar charts, histograms, and subplots.
  • Seaborn: Build aesthetically pleasing statistical graphics like heatmaps, box plots, violin plots, pair plots, and distribution plots for exploring relationships and distributions.
  • Plotly/Dash : An introduction to creating interactive web-based visualizations and simple dashboards.
• Gathering Web Data & Using APIs:
  • Web Scraping: Using libraries like BeautifulSoup and Requests to extract data from HTML web pages.
  • API Integration: Understanding how Application Programming Interfaces (APIs) work and using Python's requests library to fetch data from public APIs (e.g., weather data, social media data, government data portals).
• Exploratory Data Analysis (EDA) & Basic Statistics: Perform in-depth exploration of datasets and grasp fundamental statistical concepts to draw meaningful, data-backed conclusions:
  • Descriptive Statistics: Measures of central tendency (mean, median, mode), spread (variance, standard deviation, quartiles), and shape (skewness, kurtosis).
  • Data Distribution Analysis: Histograms, KDE plots, and QQ plots.
  • Hypothesis Testing: Basic concepts like p-value, t-tests, and chi-squared tests.

Here's what you'll learn:

• Supervised & Unsupervised Learning Paradigms:
  • You'll understand the key differences between supervised and unsupervised learning, and how to apply them to real-world problems
  • Supervised Learning: Training models on labeled data to make accurate predictions (e.g., predicting house prices, classifying customer churn, spam detection).
  • Unsupervised Learning: Discovering hidden patterns, structures, and relationships in unlabeled data (e.g., customer segmentation, anomaly detection, dimensionality reduction).
• Core Machine Learning Algorithms:
  • Linear Regression: Modeling linear relationships.
  • Logistic Regression: Classification for binary and multi-class outcomes.
  • Decision Trees & Random Forests: Ensemble methods for robust predictions and insights.
  • Gradient Boosting Machines (GBM): Advanced ensemble techniques including popular implementations like XGBoost and LightGBM.
  • K-Nearest Neighbors (KNN): A simple, instance-based learning algorithm for classification and regression tasks.
  • Support Vector Machines: Powerful algorithms for classification by finding optimal hyperplanes.
  • K-Means Clustering: A popular algorithm for partitioning data into distinct clusters.
• Data Preparation for Machine Learning :
  • Feature Engineering: Creating new, more informative features from raw data (e.g., polynomial features, time-based features, combining features).
  • Feature Selection Techniques: Identifying and selecting the most relevant features to improve model efficiency, interpretability, and reduce overfitting (e.g., correlation analysis, recursive feature elimination, importance-based selection).
  • Data Preprocessing: Handling missing values, encoding categorical variables (one-hot encoding, label encoding), scaling numerical features (standardization, normalization), and dealing with outliers.
  • Data Splitting: Understanding the importance of splitting your dataset into training, validation, and test sets to ensure unbiased model evaluation and prevent overfitting.
• Model Evaluation, Validation & Hyperparameter Tuning:
  • Evaluation Metrics: Master metrics like accuracy, precision, recall, F1-score, ROC-AUC for classification, and RMSE, MAE, R-squared for regression tasks.
  • Cross-Validation Techniques: Implement robust strategies like k-fold cross-validation and stratified sampling to ensure reliable model evaluation.
  • Hyperparameter Tuning: Learn techniques like grid search and random search to optimize model performance by fine-tuning hyperparameters.
  • Model Selection: Understand how to choose the best model based on evaluation metrics and validation results.
• Machine Learning Pipelines with Scikit-learn:
  • Scikit-learn Basics: Familiarize yourself with the Scikit-learn library, its core components, and how to use it for machine learning tasks.
  • Pipeline Creation: Learn how to create end-to-end machine learning pipelines that include preprocessing, feature engineering, model training, and evaluation steps.
  • Model Persistence: Understand how to save and load trained models using joblib or pickle for future use and deployment.

Here's what you'll learn:

• Deep Learning Fundamentals & Architectures:
  • Build a solid understanding of the foundational principles and architectures that underpin advanced AI and powerful neural networks.
  • Neural Network Architectures: Learn the fundamental building blocks of deep learning, including essential concepts of Perceptrons, Multi-Layer Perceptrons (MLPs), Convolutional Neural Networks (CNNs) for visual data, and Recurrent Neural Networks (RNNs) like LSTMs and GRUs for sequential data. Understand their core principles and applications in various AI domains.
  • Core Mechanics: Grasp the essential concepts of activation functions (ReLU, Sigmoid, Tanh), loss functions (MSE, Cross-Entropy), and optimizers (SGD, Adam), which are fundamental to how all neural networks learn.
  • PyTorch Basics for Deep Learning: Get hands-on with PyTorch, a flexible deep learning framework, covering tensors, automatic differentiation (autograd), and constructing basic neural network layers (torch.nn).
• Large Language Models (LLMs) & Generative AI:
  • Dive deep into the models powering the current AI revolution and the broader generative landscape.
  • Transformer Architecture: Understand the powerful Transformer architecture, including self-attention, multi-head attention, and positional encoding, as the backbone of modern LLMs and many generative models.
  • Understanding LLMs: Explore the capabilities, limitations, and training paradigms (pre-training, fine-tuning) of foundational LLMs.
  • Prompt Engineering & Context Management: Master the critical skill of crafting effective prompts, managing conversational context, and optimizing inputs to achieve desired outputs from LLMs.
• Building AI Agents & Intelligent Systems:
  • Learn to integrate LLMs and other AI components into sophisticated, intelligent applications capable of complex reasoning and interaction.
  • Agentic Workflows & Orchestration (e.g., LangChain/LlamaIndex): Design and build AI agents that can reason, plan, execute multi-step tasks, and leverage tools to interact with the outside world.
  • Building a Knowledge-Augmented Chatbot: Create a chatbot that can answer complex questions by integrating LLMs with external data sources, demonstrating the principles of Retrieval-Augmented Generation (RAG) and agentic workflows.
  • Tool Use & Function Calling: Teach AI models how to effectively interact with external APIs, databases, and custom software tools to extend their capabilities beyond their training data.