Basic Statistics:
A strong foundation in statistics is essential for any data science or machine learning practitioner. This module will cover:
Descriptive Statistics: Mean, median, mode, variance, and standard deviation.
Probability theory and distributions: Normal distribution, binomial distribution, and others.
Inferential Statistics: Hypothesis testing, confidence intervals, and p-values.
Correlation and Regression analysis: Understanding relationships between variables.
Data visualization techniques: Histograms, box plots, scatter plots, and more to gain insights from data.
Data Science Fundamentals:
Data science is the backbone of machine learning. This section dives into the methods and processes used to extract knowledge and insights from data. Topics include:
Data Cleaning: Handling missing data, removing outliers, and normalizing data.
Exploratory Data Analysis (EDA): Discovering patterns, anomalies, and key variables.
Feature Engineering: Creating new features and transforming data to improve model performance.
Data Wrangling: Techniques for working with large datasets, using tools like Pandas and NumPy.
Python Programming:
Python is one of the most popular languages in the world of data science and machine learning. This module will cover:
Python fundamentals: Data structures (lists, dictionaries, tuples), control flow (if, loops), functions, and modules.
Python for data analysis: Libraries such as Pandas for data manipulation, NumPy for numerical computations, and Matplotlib/Seaborn for visualization.
Advanced Python: List comprehensions, lambda functions, object-oriented programming, and error handling.
Working with external data sources: Loading data from CSV, Excel, databases, or APIs.
Machine Learning:
This is the core of the course. You will learn how to build machine learning models to make predictions or classifications. Key concepts include:
Supervised Learning: Regression (Linear, Polynomial) and Classification (Logistic Regression, Decision Trees, SVM).
Unsupervised Learning: Clustering (K-means, Hierarchical) and Dimensionality Reduction (PCA).
Model Evaluation: Metrics such as accuracy, precision, recall, F1-score, and ROC-AUC.
Model Optimization: Cross-validation, hyperparameter tuning (GridSearch, RandomSearch), and dealing with overfitting/underfitting.
Hands-on projects using libraries such as Scikit-learn and H2O Automl deep learning.
By the end of this course, you will be able to:
This course provides a solid foundation for learners seeking to enter the machine learning and data science industry, combining theoretical knowledge with practical implementation.
A strong foundation in statistics is essential for any data science or machine learning practitioner. This module will cover:
Descriptive Statistics: Mean, median, mode, variance, and standard deviation.
Probability theory and distributions: Normal distribution, binomial distribution, and others.
Inferential Statistics: Hypothesis testing, confidence intervals, and p-values.
Correlation and Regression analysis: Understanding relationships between variables.
Data visualization techniques: Histograms, box plots, scatter plots, and more to gain insights from data.
Data science is the backbone of machine learning. This section dives into the methods and processes used to extract knowledge and insights from data.
Topics include:
Data Cleaning: Handling missing data, removing outliers, and normalizing data.
Exploratory Data Analysis (EDA): Discovering patterns, anomalies, and key variables.
Feature Engineering: Creating new features and transforming data to improve model performance.
Data Wrangling: Techniques for working with large datasets, using tools like Pandas and NumPy.
Python is one of the most popular languages in the world of data science and machine learning. This module will cover:
Python fundamentals: Data structures (lists, dictionaries, tuples), control flow (if, loops), functions, and modules.
Python for data analysis: Libraries such as Pandas for data manipulation, NumPy for numerical computations, and Matplotlib/Seaborn for visualization.
Advanced Python: List comprehensions, lambda functions, object-oriented programming, and error handling.
Working with external data sources: Loading data from CSV, Excel, databases, or APIs.
This is the core of the course. You will learn how to build machine learning models to make predictions or classifications. Key concepts include:
Supervised Learning: Regression (Linear, Polynomial) and Classification (Logistic Regression, Decision Trees, SVM).
Unsupervised Learning: Clustering (K-means, Hierarchical) and Dimensionality Reduction (PCA).
Model Evaluation: Metrics such as accuracy, precision, recall, F1-score, and ROC-AUC.
Model Optimization: Cross-validation, hyperparameter tuning (GridSearch, RandomSearch), and dealing with overfitting/underfitting.
Hands-on projects using libraries such as Scikit-learn and H2O Automl deep learning.
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