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Fundamentals of AI

Course Bundle Overview: A Comprehensive Introduction to Data Science and Machine Learning


Introduction: This course bundle is designed to provide a comprehensive introduction to the field of Data Science and Machine Learning (DS/ML), particularly suitable for beginners. The bundle consists of four courses, each focusing on essential aspects of DS/ML, ranging from foundational Python programming skills to advanced machine learning model building and deployment strategies. By completing this course ensemble, learners will gain a solid understanding of key concepts, tools, and techniques necessary to embark on a successful journey in DS/ML.


Python Essentials: The first course in the bundle, “Python Essentials,” serves as a fundamental stepping stone for beginners in DS/ML. Learners will dive into the basics of Python programming language, covering topics such as data types, control structures, functions, and more. This course lays a strong foundation for subsequent courses by equipping learners with essential Python skills required for data manipulation, analysis, and visualization.


Statistics Essentials: Building upon the Python Essentials course, “Statistics Essentials” introduces learners to fundamental statistical concepts essential for data analysis in DS/ML. Topics covered include measures of centrality and dispersion, data distribution, correlations, outlier analysis, and hypothesis testing. Understanding these statistical principles is crucial for making informed decisions during the data analysis process and building accurate predictive models.


Understanding Sense of Data: Moving forward, the “Understanding Sense of Data” course delves deeper into data preprocessing and quality assurance techniques. Learners will explore data quality checks, handling missing values, encoding categorical variables, and data scaling. These preprocessing steps are vital for preparing data before feeding it into machine learning algorithms, ensuring that the data is clean, standardized, and suitable for model training.


Machine Learning – Primer: Finally, the “Machine Learning – Primer” course completes the learning journey by focusing on machine learning model development and evaluation. Learners will gain insights into basic machine learning models, data splits for model training and evaluation, evaluation metrics for assessing model performance, and techniques for model tuning and optimization. Additionally, this course emphasizes the importance of deploying machine learning models into production environments.


Applications of Essential Statistics in DS/ML: Statistics play a pivotal role in DS/ML, serving as the backbone for data analysis and model building. The Statistics Essentials course equips learners with the statistical knowledge necessary to interpret data, identify patterns, and make informed decisions. Concepts such as correlations and hypothesis testing are particularly relevant in understanding relationships between variables and validating the effectiveness of machine learning models. By applying statistical techniques, learners can derive meaningful insights from data and build robust predictive models.


ML Model Build, Evaluation, and Deployment: An integral part of DS/ML is the development, evaluation, and deployment of machine learning models. The Machine Learning – Primer course provides learners with practical insights into building, evaluating, and optimizing machine learning models. By learning about various model evaluation metrics and techniques for model tuning, learners can develop models that generalize well to unseen data. Moreover, understanding deployment strategies ensures that machine learning solutions can be effectively integrated into real-world applications, delivering tangible value.

Course 1 : Python Essentials

Learning Basic Python

Exploring NumPy

Mastering Pandas

Visualizing Data

Accessing sklearn Datasets

Course 2 : Statistics Essentials

Centrality of Data


Data Distribution


Outlier Analysis

Hypothesis Testing

Course 3 : Understanding sense of data

Data Quality Checks

Handling Missing Values


Data scaling

Course 4 : Machine Learning – Primer

Understanding Machine Learning Basics

Exploring Basic Machine Learning Models

Data Splits for Model Training and Evaluation

Model Evaluation Metrics

Model Tuning and Optimization