AI Engineer Associate Certificate Course

Explore feature engineering in machine learning by transforming raw data into meaningful inputs, applying scaling, encoding, transformation, and feature selection to boost accuracy, reduce overfitting, and improve interpretability.

Master data scaling and normalization to boost model performance. Explore min-max scaling and z-score standardization in iris data with scikit-learn, and compare KNN, SVM, and gradient-descent models.

Explore feature selection techniques, including filter, wrapper, and embedded methods, using correlation and mutual information, and apply a random forest to identify key diabetes dataset features.

Learn to create and transform features from date-time data, using day of the week, month, year, interactions, and polynomial transformations to improve model performance on a bike sharing dataset.

Learn core regression and classification evaluation metrics, including mae, mse, rmse, r squared, accuracy, precision, recall, f1, and roc AUC. Practice with iris classification and California housing regression datasets.

Explore cross-validation techniques and hyperparameter tuning to optimize model performance, using grid search on a Titanic dataset with feature engineering, scaling, encoding, and model evaluation.

Explore ensemble learning, a technique that combines multiple models through bagging, boosting, and stacking to improve accuracy, reduce variance and bias, and boost robustness.

Explore bagging, bootstrap aggregating, and random forests as ensemble techniques that reduce variance, improve robustness, and handle both regression and classification with feature randomness and bootstrap sampling.

Explore boosting and gradient boosting, an ensemble method that sequentially corrects weak learners to reduce bias, with hands-on comparison to random forests.

Discover XGBoost, an extreme gradient boosting framework that speeds training with parallel processing, handles missing data, applies L1 and L2 regularization, and enables hyperparameter tuning via grid search.

Compare LightGBM, CatBoost, and XGBoost on large datasets and categorical features; implement hands-on experiments with Titanic data, highlighting speed, accuracy, and encoding differences.

Learn how imbalanced data affects classification, cover oversampling, undersampling, SMOTE, and algorithmic solutions, and evaluate models with F1, ROC AUC, and precision-recall for fraud detection and medical diagnosis.

Compare bagging and boosting ensemble methods on a real churn dataset. Balance imbalanced data with SMOTE and evaluate models using ROC AUC.

Explore deep learning and neural networks, auto feature extraction, forward propagation, back propagation; compare with machine learning, and review applications in vision, NLP, healthcare, and speech with TensorFlow and PyTorch.

Learn forward propagation through neural networks and how activation functions shape outputs. Explore sigmoid, tanh, ReLU, and softmax with hands-on Python exercises and visualizations.

Explore loss functions and backpropagation, including mean squared error and binary cross entropy loss, and learn how gradients and gradient descent drive weight updates through forward and backward passes.

Explore gradient descent and optimization techniques, from batch, stochastic, and mini-batch variants to adaptive optimizers like Adagrad, RMSprop, and Adam, with hands-on TensorFlow and PyTorch exercises.

Learn to build, train, evaluate, and save neural networks with TensorFlow and Keras for MNIST digit classification, using conv2d, pooling, dropout, and dense layers.

Build and train neural networks with PyTorch, using tensors, Autograd, and the nn module; preprocess the mnist data, define cross-entropy loss, optimize with Adam, and evaluate and save models.

Build and optimize a neural network for CIFAR ten image classification. Preprocess data, apply convolutional layers, tune hyperparameters, and analyze performance.

Explore supervised, unsupervised, and specialized machine learning algorithms, including regression, classification, clustering, dimensionality reduction, anomaly detection, and neural networks, with implementation insights.

Explore linear regression in Python using scikit-learn to predict house prices from size by fitting a model, splitting data with train-test, and evaluating with mean squared error.

Implement ridge and lasso regression in Python using scikit-learn, compare mean squared error on a train-test split, and understand L2 vs L1 penalties and feature selection.

Implement polynomial regression in Python by transforming features into polynomial features of degree-two and fitting a linear model, then evaluate with train/test split and mean squared error.

Implement logistic regression for binary classification in Python, using scikit-learn to train, predict, and evaluate with accuracy score and confusion matrix, including data split into training and testing sets.

Explore the k-nearest neighbors algorithm as a simple non-parametric binary classifier and implement it in Python with scikit-learn. Evaluate with train-test split, accuracy, and a confusion matrix.

Learn to implement a linear support vector machine in Python using scikit-learn, train-test split, and evaluate with accuracy and confusion matrix for classifying pass or fail.

Learn how to implement a decision tree classifier in Python using scikit-learn, including data preparation, training, prediction, and evaluation with accuracy and confusion matrix.

Learn how random forests blend multiple decision trees to improve accuracy and generalization. Build and evaluate a Python random forest classifier on a pass/fail dataset with hours studied and grades.

Apply gradient boosting to binary classification in python using trees, with libraries like XGBoost, LightGBM, and CatBoost, and evaluate via train-test split, accuracy, and confusion matrix.

Demonstrate Gaussian Naive Bayes for binary classification using two features (hours studied, prior grades) and evaluate with accuracy and confusion matrix in Python with scikit-learn.

Explore k-means clustering, an unsupervised algorithm that partitions data into two clusters by iteratively updating centroids to minimize within-cluster variance, demonstrated with a Python implementation using scikit-learn.

Implement hierarchical clustering in Python using Ward linkage, visualize the dendrogram with matplotlib, and learn how to cut the tree to select the number of clusters.

Explore how DBSCAN uses density to form clusters of arbitrary shapes and identify noise. Implement a Python example with scikit-learn, eps and min_samples, and observe the cluster labels.

Explore gaussian mixture models for probabilistic, soft clustering by fitting two gaussian components to 2d data with different shapes, using sklearn's GaussianMixture, and interpret per-point probabilities.

Learn to implement principal component analysis (PCA) in Python with scikit-learn to reduce a 3D dataset to 2D, revealing principal components and the explained variance ratio.

Explore t-SNE, a non-linear dimensionality reduction that visualizes data in 2D or 3D by preserving local structure, and implement the Python example with scikit-learn to reduce 3D data to 2D.

Explore autoencoders, a neural network approach for unsupervised learning, to perform dimensionality reduction by compressing five-dimensional data into two and reconstructing the input using Python, TensorFlow, and Keras.

Implement self-training in Python by training a model on labeled data, predicting unlabeled data, adding high confidence predictions to the labeled set, and iterating to improve accuracy.

Implement q-learning in python for a 4x4 grid by initializing 16x4 q-table, defining next-state function with boundary checks, and updating with an epsilon-greedy policy using alpha, gamma, and rewards.

Learn to implement deep q-networks (dqn) in Python using PyTorch, with experience replay and a target network for stable Cartpole v1 learning.

Implement a one-class SVM in Python for anomaly detection using scikit-learn, generate clustered training data and outlier test data, train the model, and predict anomalies.

Explore how isolation forest detects anomalies by randomly partitioning data to isolate outliers, then implement and evaluate the model in Python with scikit-learn and numpy.

Build and train CNNs in Python with TensorFlow on the MNIST dataset to classify handwritten digits, using convolutional, pooling, and dense layers with softmax output.

Implement a Python-based recurrent neural network to classify IMDb movie reviews, using embedding and a simple RNN, train with binary cross-entropy, and achieve about 80–81% test accuracy.

Learn to implement lstm networks for sentiment analysis on IMDb data using TensorFlow and Keras, with embedding, padding, and a binary classifier achieving around 86% test accuracy.

Implement transformers in python using self-attention for IMDb sentiment analysis, building a transformer block with multi-head attention, feed-forward networks, layer normalization, and training with TensorFlow and Keras.

Learn to build and evaluate machine learning models with TensorFlow, from basics to intermediate concepts. Explore supervised, unsupervised, and reinforcement learning and applications in healthcare, finance, retail, and transportation.

Discover TensorFlow, an open source machine learning framework developed by Google Brain, offering flexible, scalable tools from Keras high level APIs to TensorFlow Lite for mobile deployment.

Compare TensorFlow with PyTorch, Keras, scikit-learn, Theano, and Cntk across ease of use, APIs, performance, deployment, and community, highlighting TensorFlow's scalability and production tools.

Install TensorFlow using pip by setting up a Python virtual environment, choosing CPU or GPU versions, and verifying the installation across Windows, macOS, and Linux.

Explore choosing an IDE or editor for TensorFlow development, from PyCharm and VS Code to Jupyter notebooks, and learn to configure Jupyter for TensorFlow experimentation in a virtual environment.

Verify your TensorFlow installation by running a simple script that imports TensorFlow, creates a tensor with Hello TensorFlow, runs a session, and prints the result while troubleshooting common installation issues.

Explore the basics of TensorFlow by introducing tensors as the primary data structure and explain ranks, shape, dtype, and simple tensor operations.

Master tensor operations in TensorFlow to perform arithmetic, mathematical functions, reduction, matrix, indexing, slicing, and broadcasting on tensors for building and training machine learning models.

Explore constants, variables, and placeholders in TensorFlow 2.x, learning how immutable constants and mutable variables define and update model parameters, while placeholders become unnecessary with eager execution.

Explore how TensorFlow builds and executes a computational graph by defining operations, creating tensors, and running sessions to flow data and visualize with TensorBoard.

Create and run a TensorFlow session to execute operations and compute tensor outputs, and compare graph mode with eager execution across TF 1.x and 2.x.

Learn to create and manage multiple graphs and the default graph, and run operations in TensorFlow sessions to build and train models efficiently.

Learn to build a simple feed forward neural network in TensorFlow by defining architecture, input and output layers, hidden layers, activations, loss, optimizer, training loop, and evaluation.

Explore activation functions that introduce non-linearity in neural networks, including linear, sigmoid, tanh, ReLU, leaky ReLU, and softmax, for regression, binary classification, and multi-class tasks.

Learn how loss functions measure prediction error and how optimizers minimize it, covering MSE, categorical cross entropy, sparse categorical cross entropy, custom losses and optimizers, and SGD, Adam, RMSprop, Adagrad.

Discover the Keras API, a high-level neural network interface for building, training, and deploying models with backends like TensorFlow, Theano, or CNTK.

Explore building complex models with Keras by combining layers, activation functions, loss functions, and optimizers for task-specific neural networks using the sequential or functional API, including multi-input designs.

Prepare, load, and pre-processing mnist data with TensorFlow and Keras; split into training, validation, and test sets; define, train, evaluate a convolutional neural network; visualize history and make predictions.

Explore convolutional neural networks, their convolutional and pooling layers, activation functions, and fully connected layers, and learn how CNN architectures like VGG and ResNet enable image classification and object detection.

Build and train convolutional neural networks with TensorFlow and Keras by stacking conv2d, max pooling, and dense layers, then compile, train on MNIST, and evaluate accuracy.

Learn transfer learning with pre-trained CNNs that leverage ImageNet features to improve performance on small datasets through feature extraction or fine tuning, implemented with TensorFlow Keras.

Explore recurrent neural networks, including LSTM and GRU, that address vanishing gradients via backpropagation through time with gradient clipping and dropout, for NLP, speech recognition, and time series.

Build and train recurrent neural networks with TensorFlow and Keras, using simple RNN, LSTM, or GRU layers. Compile, fit with data, and evaluate on test data.

Explore how recurrent neural networks model sequential data for language modeling and time series prediction, enabling text generation, machine translation, speech recognition, stock market forecasting, weather forecasting, and healthcare forecasting.

Explore saving and loading TensorFlow models for deployment, including saving architectures, weights, and optimizer state, using h5, json, yaml, or hdf5, and loading for inference in production.

Deploy TensorFlow models in production with TensorFlow Serving, enabling model versioning, monitoring via TensorBoard, scalability, and serving over HTTP or gRPC, using SavedModel export and Docker deployment.

Learn how to convert TensorFlow models to tensorFlow lite format, deploy on mobile and embedded devices, and optimize with quantization, pruning, and operator fusion for efficient inference.

Explore how distributed TensorFlow coordinates worker nodes, parameter servers, and cluster specs to scale training and inference across devices using data, model, and pipeline parallelism, and distributed optimizers.

Explore TensorFlow's distributed execution framework for scaling machine learning tasks across multiple devices, covering distributed sessions, distributed optimizers, fault tolerance, and various training strategies like data, model, and pipeline parallelism.

Scale TensorFlow models by containerizing with Docker, deploying on Kubernetes, and using TensorFlow Serving for versioned, scalable serving via gRPC, HTTP, or REST.

Learn how TensorFlow extended TFX enables end-to-end automation and scalable deployment of ML pipelines, from data ingestion to monitoring, with components like Examplegen, Transform, Trainer, Evaluator, and Pusher.

Define and orchestrate end-to-end ml pipelines with TFX, covering data ingestion, pre-processing, statisticsgen and schemagen, trainer and evaluator, deployment with pusher, and monitoring with governance.

Explore how TFX orchestrates model validation with the evaluator, data transformation with the transform component, and model serving via the pusher for production pipelines.

Explore real world applications of image classification across healthcare, automotive, retail, agriculture, and security. Develop hands-on projects in medical imaging, autonomous driving, product recognition, and crop monitoring.

Explore how natural language processing uses machine learning and deep learning to analyze human language across industries, enabling text classification, sentiment analysis, machine translation, and more.

Recommender systems use machine learning to analyze user preferences, behaviors, and interactions to deliver personalized recommendations, enhancing experience, engagement, and revenue across e-commerce, streaming, social media, travel, and online education.

Explore how object detection detects and localizes objects in images or video frames using deep learning, CNNs, and real-world applications across autonomous vehicles, retail, healthcare, and industry.

Develop a sentiment analysis model with TensorFlow, starting from data collection and preprocessing to build a bidirectional LSTM and dense layers, train, evaluate, and deploy via REST APIs.

Create an image recognition system using TensorFlow, covering data collection and preprocessing, data augmentation on mnist, CNN design, training, evaluation, and deployment via TensorFlow serving or flask.

Develop a time series prediction model using TensorFlow Keras, covering data collection, preprocessing, RNNs like LSTM or GRU, training, evaluation, deployment, and ongoing improvement.

Explore building a chatbot with natural language processing and a sequence-to-sequence model. Preprocess data, tokenize, vectorize with word embeddings, design encoder-decoder architectures, train and evaluate, then enable chat interactions.

Explore generative adversarial networks, including generator and discriminator trained adversarially, with key variants like DCGAN, conditional GAN, Wasserstein GAN, and cycleGAN for image generation and translation.

Learn reinforcement learning with TensorFlow, building agents that interact with environments to maximize rewards, using Q-learning, DQN, policy gradient, actor-critic, PPO, and TF agents.

Explore TensorFlow documentation and tutorials across the official website, API docs, GitHub notebooks, and Google Colab. Learn APIs, concepts, and best practices while leveraging blogs, community contributions, and online courses.

Discover popular TensorFlow online courses and books for machine learning and deep learning, covering basics, TensorFlow 2.0, data pipelines, CNNs, NLP, deployment, and practical guides with Keras.

Join the TensorFlow community, forums, GitHub, LinkedIn group, and meetups to share knowledge, seek help, and collaborate on open source projects.

Explore tensors and their dimensions from scalars to higher dimensional arrays, computational graphs, and neural networks, then cover training, optimization, deployment, and tools like TFX and TFQ in TensorFlow.

Advance your TensorFlow journey with transfer learning, fine-tuning, pre-trained models, reinforcement learning, and quantum machine learning concepts. Explore hyperparameter tuning, model optimization, and architectures like transformers and graph neural networks.

Explore PyTorch’s dynamic graphs, pythonic syntax, and GPU-accelerated tensors to build, train, and deploy neural networks, while comparing its evolution and production readiness with frameworks like TensorFlow and Keras.

Install PyTorch via pip or conda and configure a development environment with VS Code or Jupyter. Create and run a PyTorch program showing tensor creation, matmul, and optional CUDA acceleration.

Explore tensors in PyTorch, including rank, shape, and data types, and learn to create and manipulate them from lists, numpy arrays, zeros, ones, to random initializations.

Learn autograd and dynamic computation graphs in PyTorch, mastering automatic differentiation, gradient computation via backward, and the benefits of on-the-fly graph building for flexible deep learning.

Build simple neural networks with PyTorch, exploring neurons, layers, activation functions, and the forward pass. Train models by applying loss functions, optimizers, backpropagation, and the concepts of epochs and iterations.

Learn to load and preprocess data with PyTorch tools, including torchvision datasets and custom datasets, applying normalization and resizing. Use data loaders for efficient batch processing and parallel loading.

Evaluate machine learning models using accuracy, precision, recall, and F1, apply train-test split and k-fold cross-validation, then monitor performance to prevent overfitting and underfitting.

Explore transfer learning and fine tuning with pre-trained models from Torchvision and Hugging Face transformers to adapt to domain-specific tasks, using feature extraction or full fine tuning.

Master handling complex data in deep learning across images, text, and time series. Apply image augmentation, text preprocessing and tokenization, and time-series techniques for forecasting and better generalization.

deploy deep learning models to production by saving and loading with state dictionaries, and serialize with torchscript or onnx for serving via flask, fastapi, or aws lambda.

Master debugging and troubleshooting in PyTorch, diagnose shape and CUDA errors, handle NaNs and infinities, and optimize code with batching and mixed precision.

Learn to train large models efficiently with distributed training across GPUs and nodes using PyTorch distributed data parallel (DDP), including gradient synchronization, gradient accumulation, mixed precision, and profiling.

Explore how to create custom neural network layers and loss functions using torch.nn, implement advanced activations like swish, mish, and glue, and apply regularization with dropout and weight decay.

Master research oriented techniques for reproducible machine learning, track experiments with Neptune or Weights & Biases, and tune hyperparameters with grid, random, and bayesian methods.

Explore PyTorch interoperability by integrating with OpenCV, TensorFlow, Keras, Onnx, and nlp libraries like Spacy and nltk to build versatile cross-framework models.

Contribute to PyTorch by following contribution guidelines, forking the repository, creating branches, coding and testing, and submitting pull requests while engaging with forums, mailing lists, and social media.

Discover what AI agents are, how they sense, decide, and act in dynamic environments, and how autonomy, learning, and interaction set them apart from conventional software.

Learn how autonomy, reactivity, and proactivity make AI agents behave independently, respond to real-world changes, and take initiative toward long-term goals, with examples from chatbots to self-driving cars.

Identify four core AI agent types—reactive, deliberative, hybrid, and learning—and compare their strengths, weaknesses, and use cases, with learning loops and real-world examples.

Explore supervised, reinforcement, and imitation learning as the three pillars powering AI agents to learn, adapt, and operate in dynamic environments.

Explore natural language processing for AI agents, covering intent detection, entity recognition, dialogue management, memory, and language generation powered by transformer models.

Explore how AI agents embody intelligence in robotics, bridging sensing and acting through the sensorimotor loop, perception action cycles, and real-time learning for safe, autonomous robots.

Explore LangChain, Haystack, and Open Agents—three open frameworks for AI agents—and learn how they accelerate development with memory, tool usage, and multi-agent collaboration for natural language processing.

Explore auto GPT, an autonomous agent framework that reasons, plans, and acts using tools, memory, and sequential workflows. Learn its pipeline, innovations, limitations, and future impact on goal driven intelligence.

Explore IBM Bee, a modular blueprint for enterprise AI agents that emphasizes governance, auditability, security, and compliance, with planner and worker roles, tool layers, and policy-driven deployment.

LangGraph extends Lang chain with graph-based control flows and persistent state machines. Define agents as graphs with nodes and edges to manage memory, branching, and retries across long-running tasks.

Explore Crew AI, a Python framework that coordinates teams of specialized agents. Define roles, tools, and goals, enable collaboration, and execute dynamic task flows across multi-agent workflows.

Ai agents transform business operations by automating repetitive tasks, managing workflows, and providing intelligent insights across product, operations, hr, sales, and finance, using llms and memory with automation platforms.

Explore how ai agents augment healthcare with diagnostic support, clinical trial matching, patient engagement, and workflow automation, under human oversight, privacy, and compliance.

Discover how AI agents power financial systems with automated portfolio advice, real-time fraud detection, and regulatory compliance through real-time data, LLM reasoning, and auditable risk management.

Explore how AI agents transform entertainment with dynamic NPCs, storytelling companions, and creative co-pilots, delivering immersive, emotionally resonant experiences through real-time reactions and branching narratives.

Discover how AI agents in smart homes and IoT orchestrate comfort, security, and energy efficiency, enabling personalized experiences across devices through multi-agent ecosystems, edge AI, and privacy-conscious design.

Explore the future of AI agents that autonomously learn and self-improve over long horizons, using memory, multimodal reasoning, collaboration, and ethical design.

Explore fairness, explainability, and alignment in autonomous AI agents, with safeguards across data, models, interfaces, and evaluation loops, guided by IEEE ethically aligned design and EU AI Act.

Explore data rights, liability, and international regulation shaping ai agents. Learn how gdpr, the eu ai act, and global norms govern training data, liability, and transparency across the ai stack.

Explore how AI agents reshape society and the economy through displacement and productivity gains, create new roles, and demand inclusive policy to ensure shared prosperity.

Explore how AI agents and humans collaborate to amplify intelligence, combining complementary strengths, trust, and human-in-the-loop workflows to boost outcomes and creativity.

Accelerate discovery with AI agents that simulate experiments in silico, automate labs, and generate hypotheses. Foster autonomous science through literature review and interdisciplinary collaboration while upholding ethics and reproducibility.

Explore how ai agents enhance public safety and national defense, from disaster response with autonomous drones to predictive policing and real-time surveillance, while addressing ethics and oversight.

Celebrate completing the AI engineer associate certificate course by mastering advanced machine learning and deep learning. Build models in TensorFlow and PyTorch and apply real-world evaluation techniques.