Data Science and Artificial Intelligence with Python

What you'll learn

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Analyze and manipulate datasets using Python, NumPy & Pandas to perform numerical operations, data querying, sorting, grouping, aggregation and data transform
Prepare and explore real-world datasets by handling missing values, filtering data, reshaping datasets, pivoting, sorting and applying essential data preprocess
Create meaningful data visualizations using Matplotlib, including line charts, scatter plots, histograms, bar charts, and heatmaps to communicate patterns
Apply fundamental Artificial Intelligence and Machine Learning concepts, including supervised learning, unsupervised learning, classification, regression
Explain and apply foundational Neural Network concepts, including network types, weights and biases, how neural networks work
Build & evaluate a Machine Learning model using Python from problem definition and data preparation to model selection and evaluation using Accuracy, Precision
Requirements

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No prior experience in Data Science, Artificial Intelligence, or Machine Learning is required.
Basic computer skills and familiarity with using a computer, files, folders, and web browsers will be helpful.
Basic knowledge of Python programming is helpful but not mandatory, as learners will work with Python-based Data Science and AI/ML tools throughout the course.
A Windows, macOS, or Linux computer capable of installing and running Python, Anaconda, and Jupyter Notebook is recommended.
Learners should have internet access for downloading required software, Python packages, datasets, and course resources.
A basic understanding of school-level mathematics will be useful. The course introduces relevant concepts such as vectors, probability, statistics, sets, and mathematical foundations for AI/ML.
Most importantly, learners should have an interest in working with data and exploring Data Science, AI, and Machine Learning using Python.
Description

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This course contains the use of artificial intelligence.
Step into the world ofData Science, Artificial Intelligence (AI), and Machine Learning (ML) with Python through this comprehensive foundation course designed to help learners build essential knowledge and practical skills in these rapidly growing fields.
The course begins with thefundamentals of Data Science, including the role of a Data Scientist, the Data Science pipeline, commonly used tools, and practical environments such asAnaconda, Jupyter Notebook, PyPI, and Pip. Learners will also explore real-world applications and case studies to understand how Data Science is used across different industries.
You will develop an understanding of importantmathematical, statistical, and probability concepts required for Data Science and AI/ML. The course then introducesNumPy and Pandas, enabling you to work with arrays, datasets, DataFrames, data manipulation, querying, sorting, grouping, aggregation, and data from multiple sources.
As you progress, you will learn essentialdata preprocessing and exploratory data analysis (EDA) techniques, including handling missing values, selecting and reshaping data, pivoting, sorting, and preparing datasets for analysis. You will also useMatplotlib to create meaningful visualizations such as line charts, scatter plots, histograms, bar charts, and heatmaps.
The second part of the course introduces the foundations ofArtificial Intelligence and Machine Learning, including AI concepts, history and applications, Natural Language Processing, ethical and societal implications, and the relationship between AI and other technologies. You will explore important Python libraries used in AI/ML, includingScikit-Learn, TensorFlow, Keras, PyTorch, NLTK, XGBoost, CatBoost, and OpenCV.
You will then exploresupervised and unsupervised learning, classification, regression, Naive Bayes, Linear Regression, Logistic Regression, Support Vector Machines, K-Nearest Neighbors, clustering, dimensionality reduction, and Principal Component Analysis. The course also introducesNeural Networks, including their types, weights and biases, working principles, applications, and their relationship with Deep Learning.
Finally, you will learn the process ofbuilding a Machine Learning model using Python, from defining the problem and preparing the data to selecting and building a model. You will also understand how ML models are evaluated using important measures such asAccuracy, Precision, Recall, F1 Score, and Confusion Matrix.
By the end of this course, you will have developed a strong foundation inData Science, Python-based data analysis, Artificial Intelligence, and Machine Learning, preparing you to continue toward more advanced learning and practical applications in Data Science and AI/ML.
Who this course is for

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Beginners who want to build a strong foundation in Data Science, Artificial Intelligence, and Machine Learning using Python.
Students and recent graduates who want to develop practical skills in data analysis, data visualization, AI, and Machine Learning for academic or career development.
Aspiring Data Analysts and Data Scientists who want to learn how to work with data using NumPy, Pandas, Matplotlib, data preprocessing, and Exploratory Data Analysis (EDA).
Aspiring AI and Machine Learning professionals who want to understand supervised learning, unsupervised learning, classification, regression, clustering, and Neural Networks.
Python learners who want to move beyond basic programming and explore how Python and its libraries are applied to Data Science and AI/ML.
Working professionals and technology enthusiasts who want to gain foundational knowledge of Data Science and AI and understand their practical applications.
Learners interested in hands-on Machine Learning who want to understand the process of preparing data, selecting a model, building an ML model with Python, and evaluating its performance.
Homepage

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