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Definition of Machine Learning: Understanding the concept and applications of machine learning. Supervised Learning vs. Unsupervised Learning: Differentiating between the two major types of machine learning. Supervised Learning: Basics of labeled training data. Types of problems addressed (classification, regression). Unsupervised Learning: Clustering and pattern discovery. Dimensionality reduction.
Introduction to Linear Regression: Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. The goal is to find the best-fit linear relationship that minimizes the sum of squared differences between the observed and predicted values. Linear Relationship: Understanding the concept of a linear relationship between variables. Dependent and Independent Variables: Identifying the response variable (dependent) and predictor variables (independent). Linear Equation: Expressing the linear relationship using a mathematical equation. Model Training: In linear regression, the model is trained using historical data to learn the parameters of the linear equation. The training process involves finding the coefficients that minimize the difference between the predicted and actual values. Historical Data: Using a dataset with known values of the dependent and independent variables. Loss Function: Defining a loss function to quantify the difference between predicted and actual values. Gradient Descent: Iteratively adjusting coefficients to minimize the loss function. Feature Engineering and Data Preparation: Feature Selection and Transformation: Feature engineering is the process of selecting and transforming features to improve the model's performance and interpretability. Identifying Relevant Features: Analyzing the dataset to determine which features are most relevant to the target variable. Correlation Analysis: Assessing the correlation between features and the target variable. Dimensionality Reduction: Using techniques like PCA (Principal Component Analysis) to reduce the number of features. Data Cleaning: Data cleaning involves handling missing values and outliers to ensure the dataset is suitable for training a robust linear regression model. Handling Missing Values: Strategies for imputing or removing missing data points. Outlier Detection: Identifying and addressing outliers that can skew the model. Data Normalization: Scaling features to a standard range to avoid dominance of certain variables.
Logistic Regression: Binary Logistic Regression: Modeling Binary Outcomes: Using the logistic function for binary classification. Odds Ratio: Interpreting odds ratios for predictor variables. Multi-Class Logistic Regression: Extension to Multiple Classes: Adapting logistic regression for more than two outcomes. Softmax Function: Computing class probabilities using softmax. Tree Based Methods: Decision Trees: Understanding Structures: Grasping the hierarchical structure of decision nodes. Decision Criteria: Splitting nodes based on criteria like Gini impurity or entropy. Random Forests: Ensemble Learning: Combining multiple decision trees for improved accuracy. Randomization: Introducing randomness to diversify individual trees. Gradient Boosting: Sequential Model Building: Constructing models to correct errors sequentially. Gradient Descent: Minimizing the loss function through iterative parameter adjustments.
Boosting Methods: AdaBoost: Boosting algorithm combining weak learners. Gradient Boosting: Sequential model boosting with a focus on errors. XGBoost (Extreme Gradient Boosting): Highly efficient and scalable gradient boosting. Naive Bayes Classification: Types of Naive Bayes Classifiers: Gaussian, Multinomial, Bernoulli. Applications of Naive Bayes: Text classification, spam filtering. Clustering Techniques: K-means Clustering: Partitioning data into clusters based on similarity. Hierarchical Clustering: Creating a hierarchy of clusters. DBSCAN (Density-based Spatial Clustering of Applications with Noise): Clustering based on data density.
Dimensionality Reduction: Principal Component Analysis (PCA): Reducing dimensionality while retaining important features. Manifold Learning Techniques: Exploring manifold learning algorithms. Model Deployment: Applying Machine Learning Techniques: Utilizing supervised learning techniques on preprocessed datasets. Model Fit and Evaluation: Ensuring the model is a good fit for the dataset. Deployment Strategies: Deploying models for real-world applications.
Once you have completed the course, assignments, exercise and submit the projects you will be able to generate the certificate and be eligible for placements
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