Data Science and Machine Learning Basics

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About Course

Unlock the power of data and machine learning with this comprehensive beginner-to-intermediate course. “Data Science and Machine Learning Basics” is designed to provide a solid foundation in data science concepts, tools, and practical applications, even if you are completely new to the field.

Throughout this course, you will:

  • Understand the fundamentals of data science, its applications, and career scope.

  • Gain proficiency in Python programming and essential libraries such as NumPy, Pandas, Matplotlib, and Seaborn.

  • Learn data handling, preprocessing, and exploratory data analysis (EDA) techniques.

  • Master statistics, probability, and hypothesis testing for data-driven decision-making.

  • Explore supervised and unsupervised machine learning algorithms, including regression, classification, clustering, and dimensionality reduction.

  • Get hands-on experience with real-world datasets and complete practical projects like predicting house prices, Titanic survival prediction, and customer segmentation.

  • Understand feature engineering, model evaluation, hyperparameter tuning, and ensemble learning to improve model performance.

  • Learn the basics of neural networks and deep learning, and build simple neural network models using TensorFlow/Keras.

  • Discover model deployment with Flask, Streamlit, and cloud platforms like Heroku and AWS, making your projects interactive and production-ready.

  • Understand the ethical considerations, bias, data privacy, and explainable AI (XAI) essential for responsible data science.

  • Explore career paths, industry trends, and future opportunities in data science and machine learning.

By the end of this course, you will have practical skills and confidence to work with data, build machine learning models, and take the first step toward a career in data science, analytics, or AI.

Who Should Enroll:

  • Beginners looking to start a career in data science and machine learning.

  • Students and professionals wanting to understand data-driven decision-making.

  • Anyone aiming to gain hands-on experience with real-world datasets and projects.

✅ Key Takeaways:

  • Master Python and data science libraries for analysis and visualization.

  • Gain practical experience in ML algorithms and real-world projects.

  • Learn deployment, ethical AI, and industry best practices.

  • Prepare for career opportunities in data science, AI, and analytics.

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What Will You Learn?

  • Understand core concepts of data science and machine learning.
  • Write Python code for data analysis, visualization, and preprocessing.
  • Work with NumPy, Pandas, Matplotlib, and Seaborn for real-world datasets.
  • Perform data cleaning, feature engineering, and exploratory data analysis (EDA).
  • Apply supervised and unsupervised learning algorithms (regression, classification, clustering).
  • Build and evaluate machine learning models using real datasets.
  • Deploy ML models using Flask, Streamlit, and cloud platforms like Heroku/AWS.
  • Understand neural networks, deep learning basics, and model improvement techniques.
  • Practice ethical AI, explainable AI (XAI), and data privacy considerations.
  • Explore career opportunities and growth paths in data science and ML.

Course Content

Module 1: Introduction to Data Science
This module introduced Data Science basics, its applications and career scope. We learned the role of a Data Scientist, their skills & responsibilities. The workflow (collection → cleaning → analysis → modeling → deployment) was explained. We also saw common tools (Python, R, SQL, Jupyter) and the difference between Data Science, AI, ML & Deep Learning.

  • Lesson 1.1: What is Data Science? – Definition, Applications & Career Scope
  • Lesson 1.2: Role of Data Scientist – Skills & Responsibilities
  • Lesson 1.3: Data Science Workflow – Data Collection → Cleaning → Analysis → Modeling → Deployment
  • Lesson 1.4: Tools and Technologies Used in Data Science (Python, R, Jupyter, SQL, etc.)
  • Lesson 1.5: Difference between Data Science, AI, ML, and Deep Learning

Module 2: Python for Data Science
In this module, you learned the fundamentals of Python programming tailored for Data Science. You explored Python basics, control structures, functions, and built-in data structures. You also mastered file handling, exception handling, and essential data science libraries such as NumPy (arrays & computations), Pandas (data manipulation & cleaning), and Matplotlib/Seaborn (data visualization). 👉 After completing this module, you are now ready to analyze, clean, and visualize real-world datasets using Python.

Module 3: Data Handling & Preprocessing
In this module, you learned how to prepare raw data for Machine Learning models: Introduction to NumPy & Pandas → Efficient libraries for data manipulation. Importing & Exploring Data → Loading datasets, checking structure, missing values. Data Cleaning → Handling missing values, duplicates, and inconsistencies. Feature Engineering → Creating new features, scaling & normalization. Encoding Categorical Data → One-hot encoding, label encoding. Handling Outliers → Detecting and treating unusual data points. Splitting Data → Train/Test Split & Cross Validation for model evaluation. ✅ By the end of this module, you now understand how to clean, transform, and prepare datasets so that ML models can learn effectively.

Module 4: Exploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA) helps to understand data using statistics and visualizations. It identifies patterns, trends, correlations, and anomalies before model building.

Module 5: Statistics & Probability for Data Science
In this module, you will learn the fundamentals of statistics and probability that form the backbone of data science. You’ll explore how to work with population and samples, understand probability distributions like Normal, Binomial, and Poisson, and perform hypothesis testing with p-values. You will also study confidence intervals, advanced tests like ANOVA and Chi-square, and finally learn to distinguish between correlation and causation. By the end of this module, you’ll have the statistical knowledge required to analyze data rigorously and make reliable, data-driven decisions.

Module 6: Introduction to Machine Learning
This module introduces the fundamentals of Machine Learning (ML) – the science of building algorithms that learn from data. You will learn what ML is, its main types, the typical workflow of ML projects, and important concepts like bias, variance, underfitting, overfitting, and validation techniques. By the end, you’ll have a clear foundation for understanding and applying ML models.

Module 7: Supervised Learning Algorithms
This module covers Supervised Learning, where models learn from labeled data to make predictions. You will learn popular regression and classification algorithms, including Linear Regression, Logistic Regression, KNN, Decision Trees, Random Forest, SVM, and Naive Bayes. You’ll also study evaluation metrics for both regression and classification problems to measure model performance accurately. By the end of this module, you’ll be able to apply supervised learning algorithms to real-world datasets and evaluate their performance.

Module 8: Unsupervised Learning Algorithms
This module introduces Unsupervised Learning, where models learn from unlabeled data to find hidden patterns, clusters, or associations. You will explore popular clustering algorithms like K-Means, Hierarchical, and DBSCAN, understand dimensionality reduction using PCA, and learn association rule mining techniques such as Apriori for market basket analysis. By the end of this module, you’ll be able to group similar data, reduce complexity, and discover meaningful relationships in datasets.

Module 9: Feature Engineering & Model Improvement
This module focuses on enhancing model performance through feature engineering and optimization techniques. You will learn how to select important features, handle imbalanced data, apply regularization, tune hyperparameters, and use advanced ensemble learning methods like Bagging, Boosting (AdaBoost, XGBoost, LightGBM) to improve model accuracy and robustness. By the end of this module, you’ll be able to build more accurate and generalizable models for real-world datasets.

Module 10: Neural Networks & Deep Learning (Basics)
This module introduces the fundamentals of Neural Networks and Deep Learning. You will learn about neurons, perceptrons, activation functions, forward and backward propagation, and get hands-on experience with TensorFlow/Keras to build a simple neural network. By the end of this module, you’ll understand how deep learning models process data and make predictions, laying the foundation for advanced neural network architectures.

Module 11: Working with Real-World Data
This module focuses on applying data science and machine learning concepts to real-world datasets. You will explore datasets from Kaggle and UCI, and complete hands-on projects including regression (house prices), classification (Titanic survival), and clustering (customer segmentation). By the end of this module, you’ll gain practical experience in handling, analyzing, and modeling real-world data, preparing you for professional data science tasks.

Module 12: Model Deployment (Basics)
This module introduces the basics of deploying machine learning models so that they can be used in real-world applications. You will learn how to save trained models, and deploy them using Flask or Streamlit for interactive web-based applications. By the end of this module, you’ll understand how to make your ML models accessible and usable beyond local environments.

Module 13: Ethics & Future of Data Science
This module focuses on the ethical, social, and professional aspects of data science and machine learning. You will learn about data privacy, security, bias, fairness, and explainable AI (XAI). The module also provides guidance on career paths, skills, and opportunities in the data science field. By the end of this module, you’ll understand the responsible and ethical use of data and be aware of future trends and career growth.

Data Science & Machine Learning – Final Assessment
Test your knowledge and skills from all modules of this course. This assessment evaluates your understanding of Python, data handling, ML algorithms, model deployment, and ethical AI practices.

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