Machine Learning
This comprehensive Data Science and Machine Learning course delves into the world of data analysis, statistics, and predictive modeling. You'll gain a solid understanding of the principles and techniques used in extracting insights from data and building intelligent systems.Through this course, you'll explore key topics such as data preprocessing, exploratory data analysis, feature engineering, and model evaluation. You'll learn how to apply statistical techniques to uncover patterns and relationships within data sets. Additionally, you'll delve into machine learning algorithms, understanding their working principles and applications. You'll gain practical experience in using popular libraries and tools such as Python, Pandas,and scikit-learn for data analysis and machine learning tasks.By the end of the course, you'll have the skills to perform data analysis, build and evaluate machine learning models, and extract meaningful insights from complex data sets.Join us on this exciting journey into the world of Data Science and Machine Learning, and unlock the potential of data-driven decision-making and intelligent systems.
Duration
4 months (6 hrs/w)
Level
Beginner
Price
55000AMD/month
Requirements
Databases, Python
Format
Offline/Online/Hybrid
Introduction to Data Science
- -Lecture 1: Introduction to Data Science: Overview, applications, and ethical considerations.
- -Lecture 2: Python Basics for Data Science: Python syntax, variables, data structures (lists, tuples, dictionaries), control flow (if statements, loops), and functions.
- -Hands-on Session: Setting up the data science environment, Python installation, Jupyter Notebook and Google Colab introduction.
Data Manipulation and Data Visualization
- -Lecture 1: Data Manipulation with Pandas: Data loading, cleaning, transformation, and aggregation.
- -Lecture 2: Data Visualization with Matplotlib and Seaborn: Creating meaningful visualizations.
- -Hands-on Session: Data cleaning and exploration using Pandas and visualization techniques.
SQL Fundamentals for Data Science
- -Lecture 1: SQL Fundamentals for Data Science: Querying databases and manipulating data.
- -Lecture 2: Advanced SQL Techniques: Joins, subqueries, and optimization.
- -Hands-on Session: Applying SQL queries to retrieve and manipulate data from various databases.
Data Cleaning and Preprocessing, Visualization and Exploratory
- -Lecture 1: Data Cleaning and Preprocessing: Handling missing data, data imputation techniques, outlier detection and treatment, feature scaling, and normalization.
- -Lecture 2: Data Visualization and Exploratory Data Analysis (EDA): Descriptive statistics, data distributions, correlation analysis.
- -Hands-on Session: Data cleaning, preprocessing, and visualization exercises.
Probability and Descriptive Statistics
- -Lecture 1: Probability and Descriptive Statistics: Basic concepts of probability: sample space, events, and probability rules.Probability distributions: discrete and continuous distributions. Descriptive statistics: measures of central tendency and variability.
- -Lecture 2: Hypothesis Testing and Inference: Introduction to hypothesis testing framework and types of errors.Confidence intervals and inference methods.
- -Hands-on Session: Solving statistical problems and performing calculations by hand.
Advanced Statistics and Statistical Distributions
- -Lecture 1: Advanced Statistics and Statistical Distributions:Review of key probability distributions.Transforming random variables and the central limit theorem.
- -Lecture 2: Non-Parametric Methods and Multivariate Analysis:Introduction to non-parametric statistical methods.Multivariate analysis techniques: covariance, correlation, and PCA.
- -Hands-on Session: Applying advanced statistical concepts and techniques.
Statistical Analysis and Regression Modeling
- -Lecture 1: Statistical Analysis in Python: Hypothesis testing, confidence intervals.
- -Lecture 2: Regression Analysis: Simple linear regression, multiple linear regression, model evaluation.
- -Hands-on Session: Statistical analysis and regression modeling using Python libraries.
Logistic Regression and kNN
- -Lecture 1: Logistic Regression: Introduction to logistic regression, binary and multi-class classification, model interpretation, and evaluation.
- -Lecture 2: k-Nearest Neighbors (kNN): Introduction to kNN algorithm, distance metrics, selecting k value, model evaluation.
- -Hands-on Session: Implementing logistic regression and kNN algorithms, training classification models, and evaluating their performance.
SVM and Naive Bayes
- -Lecture 1: Support Vector Machines (SVM): Introduction to SVM, linear and non-linear kernels, tuning hyperparameters, model evaluation.
- -Lecture 2: Naive Bayes: Bayesian classification, conditional probability, Laplace smoothing, model evaluation.
- -Hands-on Session: Implementing SVM and Naive Bayes algorithms, training classification models, and evaluating their performance.
Dimensionality Reduction and Model Evaluation
- -Lecture 1: Dimensionality Reduction: Principal Component Analysis (PCA) and t-SNE.
- -Lecture 2: Model Evaluation and Selection: Feature selection, cross-validation, evaluation metrics, addressing overfitting and underfitting.
- -Hands-on Session: Implementing dimensionality reduction techniques and evaluating models.
Clustering Techniques and Time Series Analysis
- -Lecture 1: Clustering Techniques: Unsupervised learning, k-means clustering, hierarchical clustering.
- -Lecture 2: Time Series Analysis: Concepts, trend analysis, seasonal decomposition, forecasting techniques (ARIMA, SARIMA), time series visualization.
- -Hands-on Session: Implementing clustering algorithms and analyzing time series data.
NLP and Sentiment Analysis, Decision Trees and Ensemble Methods
- -Lecture 1: Natural Language Processing (NLP) and Sentiment Analysis: Text preprocessing, sentiment scoring.
- -Lecture 2: Decision Trees and Ensemble Methods: Tree-based models, random forests, gradient boosting, parameter tuning (grid search, Bayesian optimization).
- -Hands-on Session: NLP techniques and tree-based model implementation.
Advanced Feature Engineering and Hyperparameter Tuning
- -Lecture 1: Advanced techniques in feature engineering: Feature transformations, feature interactions, feature encoding, feature selection
- -Lecture 2 : Advanced hyperparameter tuning: Random search, grid search, Bayesyan optimization, automated hyperparameter tuning
- -Hands-on Session: Advanced Feature Engineering and Hyperparameter Tuning: Implementing advanced feature engineering techniques, including transformations, interactions, and encoding methods, to create informative features from raw data. Selecting relevant features using various feature selection techniques. Exploring different hyperparameter tuning approaches, such as grid search, random search, or Bayesian optimization, to find the optimal hyperparameter configuration.
Ensemble Methods and Anomaly Detection
- -Lecture 1: Ensemble Methods: Bagging, boosting, and stacking techniques, model combination and aggregation, model evaluation.
- -Lecture 2: Anomaly Detection: Introduction to anomaly detection, outlier detection methods (e.g., Isolation Forest, Local Outlier Factor), model evaluation.
- -Hands-on Session: Implementing ensemble methods and anomaly detection algorithms, training models, and evaluating their performance.
Introduction to Neural Networks
- -Lecture 1: Introduction to Neural Networks for Tabular Data: Understanding the application of neural networks to tabular data including the advantages and challenges compared to traditional machine learning algorithms. Feedforward neural networks, activation functions, model architectures (e.g., fully connected, deep neural networks), and the backpropagation algorithm.
- -Lecture 2: Advanced Techniques for Neural Networks in Tabular Data: Exploring advanced techniques and strategies for improving the performance of neural networks on tabular data. Regularization techniques (e.g., dropout, L1/L2 regularization), batch normalization, learning rate scheduling, and optimization algorithms (e.g., Adam, RMSprop).
- -Hands-on Session: Implementing Neural Networks for Tabular Data in Python using TensorFlow.
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