Curriculam

• Overview of Machine Learning
• Types of Machine Learning algorithms
• Applications of Machine Learning in various domains
• Introduction to Python and popular ML libraries
• NumPy, Pandas, and scikit-learn

• Data cleaning and handling missing values
• Data transformation techniques (scaling, normalization)
• Feature selection and engineering
• Exploratory Data Analysis (EDA) using visualizations

• Linear Regression
• Logistic Regression
• Decision Trees and Random Forests
• Support Vector Machines (SVM)
• Naive Bayes Classifier

• K-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)
• Association Rule Learning (Apriori algorithm)
• Anomaly detection

• Training and testing datasets
• Cross-validation techniques
• Evaluation metrics (accuracy, precision, recall, F1-score)
• Confusion matrix and ROC curves
• Bias-Variance tradeoff

• Bagging and Random Forests
• Boosting and AdaBoost
• Stacking and Blending
• Hyperparameter tuning
• Model selection techniques

• Introduction to Neural Networks
• Activation functions
• Backpropagation and Gradient Descent
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)

• Image Classification
• Object Detection and Localization
• Natural Language Processing (NLP)
• Sequence-to-Sequence Models
• Generative Adversarial Networks (GANs)

• Markov Decision Processes (MDPs)
• Q-Learning and Value Iteration
• Policy Gradient Methods
• Deep Q-Networks (DQN)
• Applications of Reinforcement Learning

• Transfer Learning
• Explainable AI and Interpretability
• Time Series Analysis
• Autoencoders and Variational Autoencoders
• Model Deployment and serving with cloud platforms