• Overview of Machine Learning: Understanding the basics of machine learning and its importance in solving real-world problems.
• Types of Machine Learning:

➤ Supervised Learning: Learning from labeled data to make predictions.
➤ Unsupervised Learning: Discovering hidden patterns in unlabeled data.
➤ Reinforcement Learning: Learning from interaction and feedback.

• Key Machine Learning Terminologies: Data preprocessing, feature extraction, training, testing, validation, etc.
• Applications of Machine Learning: Exploring real-world applications like recommendation systems, image recognition, natural language processing, and more.

• Introduction to Python Libraries: Overview of essential libraries for machine learning, including NumPy, Pandas, Matplotlib, and Seaborn.
• Data Preprocessing: Techniques for data cleaning, handling missing data, data scaling, encoding categorical features, and feature engineering.
• Exploratory Data Analysis (EDA): Visualizing and analyzing data distributions, correlations, and outliers.

• Linear Regression: Predicting continuous values based on linear relationships.
• Logistic Regression: Classification technique for binary and multi-class problems.
• Decision Trees: Building decision trees for classification and regression tasks.
• Random Forests: Ensemble learning method using multiple decision trees to improve accuracy.
• Support Vector Machines (SVM): Supervised learning algorithm used for classification tasks.
• K-Nearest Neighbors (KNN): Instance-based learning for classification and regression.
• Naive Bayes: Probabilistic classifier based on Bayes’ theorem.
• Performance Evaluation Metrics: Accuracy, Precision, Recall, F1 Score, Confusion Matrix, ROC Curve, and AUC.

• Clustering:

★ K-Means Clustering: Partitioning data into clusters with similar characteristics.

★ Hierarchical Clustering: Building a hierarchy of clusters and dendrogram visualization.

• Dimensionality Reduction:

★ Principal Component Analysis (PCA): Reducing the number of features while preserving variance.
★ t-SNE: Visualizing high-dimensional data in 2D or 3D.

• Association Rule Mining: Discovering interesting relationships between variables (Apriori, Eclat algorithms).

• Introduction to Neural Networks: Understanding the architecture of a neural network.
• Activation Functions: Sigmoid, ReLU, Tanh, etc.
• Backpropagation: Training neural networks using the backpropagation algorithm.
• Deep Learning Techniques:

★ Convolutional Neural Networks (CNN): Used for image processing and recognition.
★ Recurrent Neural Networks (RNN): Applied to sequential data like time series and natural language.
★ Long Short-Term Memory (LSTM): A type of RNN for better handling of long-term dependencies.

• Text Preprocessing: Tokenization, stemming, lemmatization, and stop-word removal.
• Feature Extraction: TF-IDF (Term Frequency-Inverse Document Frequency), Word2Vec, and GloVe embeddings.
• Text Classification: Building models for sentiment analysis, spam detection, and topic categorization.
• Named Entity Recognition (NER): Identifying entities like names, locations, dates, etc.
• Topic Modeling: Identifying hidden themes in large collections of text using LDA (Latent Dirichlet Allocation).