Python for Data Science and Machine Learning Bootcamp

(3 customer reviews)

₦102,524.00

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Description

The “Python for Data Science and Machine Learning Bootcamp” is an intensive, hands-on course designed to provide participants with the essential skills and knowledge needed to analyze data, build predictive models, and implement machine learning algorithms using Python. This course covers the entire data science pipeline, from data collection and cleaning to advanced machine learning techniques and model evaluation.

What you'll learn

  • Use Python for Data Science and Machine Learning
  • Use Spark for Big Data Analysis
  • Implement Machine Learning Algorithms
  • Learn to use NumPy for Numerical Data
  • Learn to use Pandas for Data Analysis
  • Learn to use Matplotlib for Python Plotting
  • Learn to use Seaborn for statistical plots
  • Use Plotly for interactive dynamic visualizations
  • Use SciKit-Learn for Machine Learning Tasks
  • K-Means Clustering
  • Logistic Regression
  • Linear Regression
  • Random Forests and Decision Trees
  • Natural Language Processing and Spam Filters
  • Neural Networks
  • Support Vector Machines

Course Content

Module 1: Introduction to Python
  • Lesson 1.1: Python Basics
    • Python syntax and basic programming concepts.
    • Variables, data types, and operators.
  • Lesson 1.2: Control Structures and Functions
    • Conditional statements and loops.
    • Defining and invoking functions.
  • Lesson 1.3: Working with Data Structures
    • Lists, tuples, dictionaries, and sets.
    • List comprehensions and lambda functions.
Module 2: Data Manipulation with Pandas
  • Lesson 2.1: Introduction to Pandas
    • Series and DataFrame objects.
    • Importing and exporting data (CSV, Excel, SQL).
  • Lesson 2.2: Data Cleaning and Preparation
    • Handling missing data.
    • Data transformation and normalization.
  • Lesson 2.3: Data Aggregation and Grouping
    • GroupBy operations.
    • Pivot tables and cross-tabulations.
Module 3: Data Visualization
  • Lesson 3.1: Introduction to Matplotlib
    • Basic plotting functions.
    • Customizing plots (labels, titles, legends).
  • Lesson 3.2: Advanced Visualization with Seaborn
    • Statistical plots (histograms, KDE plots, pair plots).
    • Customizing Seaborn plots.
Module 4: Statistical Analysis
  • Lesson 4.1: Descriptive Statistics
    • Measures of central tendency and dispersion.
    • Correlation and covariance.
  • Lesson 4.2: Inferential Statistics
    • Probability distributions.
    • Hypothesis testing and p-values.
  • Lesson 4.3: Regression Analysis
    • Simple and multiple linear regression.
    • Evaluating regression models.
Module 5: Introduction to Machine Learning
  • Lesson 5.1: Machine Learning Basics
    • Overview of machine learning and its applications.
    • Supervised vs. unsupervised learning.
  • Lesson 5.2: Data Preprocessing for Machine Learning
    • Feature scaling and encoding.
    • Train-test split and cross-validation.
Module 6: Supervised Learning Techniques
  • Lesson 6.1: Classification Algorithms
    • Logistic regression, K-Nearest Neighbors, Decision Trees.
    • Evaluating classification models (confusion matrix, ROC curve).
  • Lesson 6.2: Regression Algorithms
    • Linear regression, Ridge and Lasso regression.
    • Evaluating regression models (RMSE, R-squared).
  • Lesson 6.3: Ensemble Methods
    • Random Forest, Gradient Boosting, AdaBoost.
    • Hyperparameter tuning with Grid Search and Random Search.
Module 7: Unsupervised Learning Techniques
  • Lesson 7.1: Clustering Algorithms
    • K-Means clustering, Hierarchical clustering.
    • Evaluating clustering results.
  • Lesson 7.2: Dimensionality Reduction
    • Principal Component Analysis (PCA).
    • t-Distributed Stochastic Neighbor Embedding (t-SNE).
Module 8: Advanced Topics and Techniques
  • Lesson 8.1: Natural Language Processing (NLP)
    • Text preprocessing and feature extraction.
    • Sentiment analysis and topic modeling.
  • Lesson 8.2: Deep Learning Introduction
    • Basics of neural networks.
    • Implementing neural networks with TensorFlow and Keras.
Module 9: Real-World Projects and Case Studies
  • Lesson 9.1: Capstone Project
    • Comprehensive project covering the entire data science pipeline.
    • Applying learned skills to solve a real-world problem.
  • Lesson 9.2: Case Studies
    • Analysis of successful data science and machine learning projects.
    • Learning from industry practices and methodologies.

3 reviews for Python for Data Science and Machine Learning Bootcamp

  1. Olubunmi –

    “The ‘Python for Data Science and Machine Learning Bootcamp’ was an exceptional learning experience. The well-structured curriculum and engaging instructors provided a comprehensive foundation in Python programming, data analysis, and machine learning algorithms. The hands-on projects and assignments allowed me to apply my knowledge immediately, fostering practical understanding and confidence. I highly recommend this bootcamp to anyone seeking to advance their skills in these fields.”

  2. Damilola –

    “I highly recommend the ‘Python for Data Science and Machine Learning Bootcamp’ online course. The course was well-structured and provided a comprehensive understanding of the subject matter. The instructors were knowledgeable and passionate, and they were always willing to help with any questions. The course materials were top-notch and included interactive exercises that reinforced the lessons. Overall, this course was an invaluable resource for building my skills in data science and machine learning.”

  3. Malam –

    “I highly recommend the ‘Python for Data Science and Machine Learning Bootcamp’. The course provides a comprehensive and well-structured curriculum that covers both theoretical concepts and practical applications. The instructor’s expertise and ability to convey complex topics in an engaging way made learning a breeze. The practical projects and hands-on exercises allowed me to apply my knowledge and build a strong foundation in data science and machine learning. Overall, this bootcamp has significantly enhanced my skills and given me the confidence to pursue further advancements in this field.”

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