– Introduction to AI and ML
– Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
– Key Algorithms in Machine Learning (Linear Regression, k-NN, etc.)
– ML Pipeline and Workflow
– Beginners with no prior ML experience
– Aspiring data scientists and engineers
– Business professionals interested in ML
– Video tutorials
– Interactive ML exercises
– Code examples and walkthroughs
– Quizzes and projects
– Overview of Artificial Intelligence and Machine Learning
– Understanding Data Science and Its Connection to ML
– Introduction to key ML algorithms
– Basics of Supervised Learning and Unsupervised Learning
– Beginners interested in AI and ML
– Non-technical learners looking to get started in AI
– Professionals exploring AI
– Introductory lessons in AI and ML
– Hands-on projects
– Quizzes and tests
– Final exam and certification
– Introduction to Neural Networks
– Building and Training Deep Neural Networks
– Convolutional Neural Networks (CNNs)
– Recurrent Neural Networks (RNNs)
– Model Deployment with TensorFlow and Keras
– Learners with a basic understanding of machine learning
– Data scientists and engineers looking to specialize in deep learning
– TensorFlow and Keras hands-on examples
– Projects such as image classification and text generation
– Interactive coding assignments
– Data Preprocessing and Feature Engineering
– Building Predictive Models (Decision Trees, SVM, etc.)
– Model Evaluation Metrics
– Using ML for Data Science Applications (e.g., clustering, forecasting)
– Data scientists and analysts
– Professionals working in analytics or data-driven roles
– Students of data science
– In-depth data science projects
– Real-world datasets for practice
– Model evaluation techniques
– Case studies from industry
– Introduction to Supervised Learning: Algorithms and Techniques
– Introduction to Unsupervised Learning: Clustering, PCA, and Dimensionality Reduction
– When to use each type of learning
-learners with a basic understanding of ML
– Aspiring machine learning engineers
– Data analysts looking to explore ML
– Video lectures on supervised and unsupervised learning
– Hands-on practice with clustering and regression tasks
– Case study projects
– Basics of Neural Networks: Structure and Working Principles
– Activation Functions
– Training Deep Neural Networks: Backpropagation and Gradient Descent
– Advanced Deep Learning Architectures
– Intermediate learners with basic ML knowledge
– Engineers and data scientists interested in deep learning
– Step-by-step deep learning projects
– Neural network implementation using Python
– Code-based assignments for neural network models
– Introduction to NLP: Tokenization, Lemmatization, POS tagging
– Text Classification using Machine Learning
– Introduction to Word Embeddings and Transformers
– Building Chatbots and Text-based Models
– Data scientists working with textual data
– Developers looking to build NLP applications
– Aspiring NLP researchers
– NLP projects using Python
– Hands-on with transformers and BERT
– Text-based tasks and chatbot development exercises
