TaskHiveSea is a skilled training provider with over 7 years of expertise in Artificial Intelligence, Machine Learning, and Data Analytics. With a focus on practical, hands-on learning, TaskHiveSea equips learners with the ability to build AI models, perform data analysis, and apply advanced analytics for real-world problem solving. Through industry-relevant training, TaskHiveSea helps students and professionals gain job-ready skills in AI, ML, and Data Science to accelerate their careers in technology.

Course Content

ML Topics

1. Introduction to ML

Ø  What is Machine Learning?

Ø  Difference between AI, ML, and Deep Learning

Ø  Types of ML:

Ø  Supervised (Regression, Classification)

Ø  Unsupervised (Clustering, Dimensionality Reduction)

Ø  Reinforcement Learning (basic overview)

Ø  Real-world applications

2. Data Preprocessing

Ø  Data collection & loading

Ø  Handling missing values (mean/median imputation, drop)

Ø  Handling outliers

3. Supervised Learning

Ø  Regression Models

Ø  Linear Regression

Ø  Polynomial Regression

Ø  Regularization (Ridge, Lasso)

Ø  Classification Models

Ø  Logistic Regression

Ø  k-Nearest Neighbors (kNN)

Ø  Decision Trees, Random Forests

Ø  Model evaluation:

Ø  Regression: MSE, RMSE, R²

Ø  Classification: Accuracy, Precision, Recall

4. Unsupervised Learning

Ø  Clustering

Ø  K-Means Clustering

Ø  Hierarchical Clustering

Ø  DBSCAN (Density-Based Clustering)

Ø  Dimensionality Reduction

Ø  Principal Component Analysis (PCA)

5. Introduction to Neural Networks

Ø  Artificial Neuron model (inputs, weights, activation function)

Ø  Perceptron model (working + limitations)

Ø  Multi-Layer Perceptron (MLP)

Ø  Activation functions

Ø  Backpropagation & Gradient Descent (basic explanation)

Ø  Simple implementation using Python

6. Advanced ML Concepts

Ø  Ensemble Learning (Bagging, Boosting, Stacking)

Ø  Feature importance

Ø  Hyperparameter tuning (Grid Search, Random Search)

Ø  Bias-Variance Tradeoff

Ø  Overfitting & Underfitting handling

Artificial Intelligence (AI) Topics

1. Introduction to AI

Ø  History of AI

Ø  Applications of AI in industry (Healthcare, Finance, Retail, Manufacturing)

2. Intelligent Agents & Problem Solving

Ø  Definition of agents, environment, sensors, actuators

Ø  Types of agents: Simple Reflex, Goal-based, Utility-based

Ø  Search strategies: BFS, DFS, A* (conceptual only)

Ø  Constraint satisfaction problems (Sudoku, Scheduling examples)

3. Knowledge Representation & Reasoning

Ø  Logic: Propositional and Predicate Logic

Ø  Rule-based systems (if-then rules, expert systems)

Ø  Ontologies & semantic networks (basic introduction)

4. Natural Language Processing (NLP)

Ø  Text preprocessing: tokenization, stopword removal, stemming, lemmatization

Ø  Bag of Words & TF-IDF representations

Ø  Word embeddings: Word2Vec, GloVe (concepts)

Ø  Transformers and LLMs (BERT, GPT – high level)

Ø  Applications: Chatbots, Sentiment Analysis

5. Computer Vision (CV)

Ø  Image representation (pixels, channels)

Ø  Convolutional Neural Networks (CNN basics)

Ø  Image classification using pretrained models

6. Generative AI

Ø  Introduction to Generative AI

Ø  GANs (Generative Adversarial Networks – basic idea)

Ø  Diffusion models (overview)

Ø  Retrieval-Augmented Generation (RAG)

Ø  Applications: Text-to-image, AI chatbots, content generation

8. AI  Case studies

Ø  AI adoption strategies

Ø  Business use cases in different domains

Ø  Challenges in AI implementation