Machine Learning Specilization
Covering fundamental concepts, data preprocessing, and feature engineering, it equips learners with the skills to harness the potential of machine learning for diverse applications. As the demand for expertise in this field surges, a specialized education in machine learning becomes essential for those seeking to navigate and contribute to the rapidly evolving landscape of AI.
Skills You Will Gain
NumPy
Pandas
Scikit-Learn
Matplotlib
Seaborn
SciPy
Statsmodels
TensorFlow
PyTorch
Keras
This course includes
- 1 : 1 Session
- 100% Placement Assistance
- 24 Weeks
- Real TIme Project Training
Syllabus Overview
Advanced Machine Learning Specialization
A machine learning specialization delves into the core principles of artificial intelligence, focusing on algorithms and models that empower computers to learn and predict from data.
- Course Overview
- Goals, structure, and key outcomes of the course.
- History of Machine Learning
- Milestones in the development of machine learning.
- Types of Machine Learning
- Supervised vs. unsupervised vs. reinforcement learning.
- Probability and Statistics
- Basic concepts, distributions, and statistical significance.
- Bayesian Thinking
- Introduction to Bayesian inference and decision making.
- Linear Algebra
- Vectors, matrices, operations, eigenvalues, and eigenvectors.
- Calculus
- Derivatives, integrals, and their applications in machine learning.
- Visualization Techniques
- Using graphs and plots to explore data relationships.
- Descriptive Statistics
- Measures of central tendency, dispersion, and correlation.
- Data Preprocessing
- Cleaning, normalization, encoding, and handling missing values.
- Linear Regression
- Simple and multiple linear regression models.
- Polynomial Regression
- Addressing non-linearity in data relationships.
- Regularization Methods
- Ridge, Lasso, and Elastic Net techniques.
- Logistic Regression
- Binary and multiclass classification.
- K-Nearest Neighbors (KNN)
- Non-parametric method for classification and regression.
- Support Vector Machines (SVM)
- Linear and non-linear classification.
- Decision Trees
- Building and pruning for classification and regression.
- Random Forests
- Ensemble learning method for classification and regression.
- Boosting Algorithms
- AdaBoost, Gradient Boosting, XGBoost, and LightGBM.
- Cross-Validation Techniques
- k-fold and stratified k-fold cross-validation.
- Performance Metrics
- Accuracy, precision, recall, F1-score, and ROC curves.
- Model Optimization with Hyperparameter Tuning
- Grid search, random search, and Bayesian optimization with Optuna.
- Feature Selection and Dimensionality Reduction
- Techniques like forward selection, backward elimination, and PCA.
- Handling Imbalanced Data
- Techniques like SMOTE, Near Miss for undersampling and oversampling.
- Clustering Algorithms
- K-means, hierarchical clustering, and DBSCAN.
- Association Rule Learning
- Market basket analysis using Apriori and FP-Growth algorithms.
- Introduction to Neural Networks
- Basic concepts, architecture, and backpropagation.
- Introduction to Deep Learning
- Brief overview of CNNs and RNNs for context.
- Capstone Project
- Students will apply their learned skills to tackle a real-world data problem.
- Weekly Workshops
- Practical sessions focusing on applying machine learning techniques with real datasets.
- Industry Guest Lectures
- Talks by industry experts on current trends and future directions in machine learning.
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