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Data Science Mastery: Journey into Machine Learning

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Data Science Mastery: Journey into Machine Learning, available at $59.99, has an average rating of 4.95, with 528 lectures, based on 75 reviews, and has 922 subscribers.

You will learn about Gain proficiency in using Python libraries commonly used in data science and machine learning, such as NumPy, Pandas, and Matplotlib. Learn how to clean and preprocess datasets, including handling missing data, outliers, and feature scaling. Acquire knowledge of exploratory data analysis techniques to extract insights and patterns from data. Master the fundamentals of statistical analysis and apply statistical methods to interpret and draw conclusions from data. Understand the principles of machine learning and its various algorithms, such as regression, classification, and clustering. Learn how to select appropriate machine learning models and techniques for different types of problems and datasets. Develop skills in feature engineering and selection to enhance the performance of machine learning models. This course is ideal for individuals who are Aspiring data scientists and machine learning enthusiasts who have a basic understanding of Python programming. or Learners who want to acquire comprehensive knowledge and practical skills in Python, data science, and machine learning. or The course content is tailored to provide valuable insights and hands-on experience to individuals aiming to excel in data-driven problem-solving and analysis. It is particularly useful for Aspiring data scientists and machine learning enthusiasts who have a basic understanding of Python programming. or Learners who want to acquire comprehensive knowledge and practical skills in Python, data science, and machine learning. or The course content is tailored to provide valuable insights and hands-on experience to individuals aiming to excel in data-driven problem-solving and analysis.

Enroll now: Data Science Mastery: Journey into Machine Learning

Summary

Title: Data Science Mastery: Journey into Machine Learning

Price: $59.99

Average Rating: 4.95

Number of Lectures: 528

Number of Published Lectures: 519

Number of Curriculum Items: 528

Number of Published Curriculum Objects: 519

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

  • Gain proficiency in using Python libraries commonly used in data science and machine learning, such as NumPy, Pandas, and Matplotlib.
  • Learn how to clean and preprocess datasets, including handling missing data, outliers, and feature scaling.
  • Acquire knowledge of exploratory data analysis techniques to extract insights and patterns from data.
  • Master the fundamentals of statistical analysis and apply statistical methods to interpret and draw conclusions from data.
  • Understand the principles of machine learning and its various algorithms, such as regression, classification, and clustering.
  • Learn how to select appropriate machine learning models and techniques for different types of problems and datasets.
  • Develop skills in feature engineering and selection to enhance the performance of machine learning models.

Who Should Attend

  • Aspiring data scientists and machine learning enthusiasts who have a basic understanding of Python programming.
  • Learners who want to acquire comprehensive knowledge and practical skills in Python, data science, and machine learning.
  • The course content is tailored to provide valuable insights and hands-on experience to individuals aiming to excel in data-driven problem-solving and analysis.

Target Audiences

  • Aspiring data scientists and machine learning enthusiasts who have a basic understanding of Python programming.
  • Learners who want to acquire comprehensive knowledge and practical skills in Python, data science, and machine learning.
  • The course content is tailored to provide valuable insights and hands-on experience to individuals aiming to excel in data-driven problem-solving and analysis.

The Python for Data Science and Machine Learning course is designed to equip learners with a comprehensive understanding of Python programming, data science techniques, and machine learning algorithms.

Whether you are a beginner looking to enter the field or a seasoned professional seeking to expand your skillset, this course provides the knowledge and practical experience necessary to excel in the rapidly growing field of data science.

Course Objectives:

1. Master Python Programming: Develop a strong foundation in Python programming, including syntax, data structures, control flow, and functions. Gain proficiency in using Python libraries such as NumPy, Pandas, and Matplotlib to manipulate and visualize data effectively.

2. Data Cleaning and Preprocessing: Learn how to handle missing data, outliers, and inconsistent data formats. Acquire skills in data cleaning and preprocessing techniques to ensure the quality and reliability of datasets.

3. Exploratory Data Analysis: Understand the principles and techniques of exploratory data analysis. Learn how to extract insights, discover patterns, and visualize data using statistical methods and Python libraries.

4. Statistical Analysis: Gain a solid understanding of statistical concepts and techniques. Apply statistical methods to analyze data, test hypotheses, and draw meaningful conclusions.

5. Machine Learning Fundamentals: Learn the foundations of machine learning, including supervised and unsupervised learning, regression, classification, and clustering. Understand the strengths and limitations of different machine learning algorithms.

6. Machine Learning Implementation: Gain hands-on experience in implementing machine learning models using Python libraries such as scikit-learn. Learn how to train, evaluate, and optimize machine learning models.

7. Feature Engineering and Selection: Develop skills in feature engineering to create meaningful and informative features from raw data. Learn techniques for feature selection to improve model performance and interpretability.

8. Model Evaluation and Optimization: Learn how to assess the performance of machine learning models using techniques like cross-validation and evaluation metrics. Understand the importance of hyperparameter tuning and regularization for model optimization.

9. Deep Learning Concepts: Explore the basics of deep learning, including neural networks, activation functions, and gradient descent optimization. Gain an understanding of deep learning architectures and their applications.

10. Practical Deep Learning: Acquire practical experience in building and training neural networks using popular deep learning frameworks such as TensorFlow or PyTorch. Learn how to apply deep learning techniques to solve real-world problems.

Course Curriculum

Chapter 1: Introduction to Numpy

Lecture 1: Introduction to Numpy

Lecture 2: Numpy ndarray

Lecture 3: ndarray

Lecture 4: Data Types

Lecture 5: Arithmetic

Lecture 6: Indexing and Slicing

Lecture 7: Indexing and Slicing – 2

Lecture 8: Indexing and Slicing – 3

Lecture 9: Boolean Indexing

Lecture 10: Fancy Indexing in Numpy

Lecture 11: Transposing and Swapping

Lecture 12: Universal Functions

Lecture 13: Array Oriented Programming

Lecture 14: Expressing Conditional Logic

Lecture 15: Methods involving Math and Statistics

Lecture 16: Boolean Array Methods

Lecture 17: The Sorting

Lecture 18: Unique Set Logic

Lecture 19: Linear Algebra

Lecture 20: Pseudorandom Number Generator

Lecture 21: Random Walks (An example)

Lecture 22: Simulation of plenty of Random Walks

Chapter 2: Introduction to Pandas

Lecture 1: Introduction to Pandas

Lecture 2: Series

Lecture 3: Series – 2

Lecture 4: Series – 3

Lecture 5: DataFrame

Lecture 6: DataFrame – 2

Lecture 7: DataFrame – 3

Lecture 8: DataFrame – 4

Lecture 9: Index Objects

Lecture 10: Reindexing

Lecture 11: Reindexing – 2

Lecture 12: Axis and the Dropping of Values

Lecture 13: Indexing

Lecture 14: Indexing – 2

Lecture 15: Using loc and iloc for Selection

Lecture 16: Integer Indexes

Lecture 17: Data Alignment & Arithmetic

Lecture 18: Data Alignment & Arithmetic – 2

Lecture 19: Fill Values with Arithmetic Methods

Lecture 20: DataFrame and Series and the Operation

Lecture 21: Application and Mapping

Lecture 22: Application and Mapping – 2

Lecture 23: Ranking and Sorting

Lecture 24: Ranking and Sorting – 2

Lecture 25: Axis Indexes

Lecture 26: Computing Descriptive Statistics

Lecture 27: Computing Descriptive Statistics – 2

Lecture 28: Value Counts, Membership, Unique Values

Chapter 3: Data Preparation and Data Cleaning

Lecture 1: Lets Handle Missing Data

Lecture 2: Filtration of the Missing Data

Lecture 3: Filling of the Missing Data

Lecture 4: Duplicates Removal

Lecture 5: Function or Mapping and Transformation

Lecture 6: Function or Mapping

Lecture 7: Function or Mapping – 2

Lecture 8: Values Replacing

Lecture 9: Axis Indexes Renaming

Lecture 10: Discretization and Binning

Lecture 11: Discretization and Binning – 2

Lecture 12: Discretization and Binning – 3

Lecture 13: Filtering and Detecting the Outliers

Lecture 14: Random Sampling and Permutations

Lecture 15: Indicator Computing

Lecture 16: Indicator Computing – 2

Lecture 17: Indicator Computing – 3

Lecture 18: Indicator Computing – 4

Lecture 19: String Object Methods

Lecture 20: String Object Methods – 2

Lecture 21: Regular Expressions

Lecture 22: Regular Expressions – 2

Lecture 23: Regular Expressions – 3

Lecture 24: Vectorized String Functions

Lecture 25: Vectorized String Functions – 2

Lecture 26: Hierarchical Indexing

Lecture 27: Hierarchical Indexing – 2

Lecture 28: Hierarchical Indexing – 3

Lecture 29: Reordering and the Sorting Levels

Lecture 30: Summarizing Statistics and Indexing with DataFrames Columns

Lecture 31: DataFrame Join with Database Style

Lecture 32: DataFrame Join with Database Style – 2

Lecture 33: DataFrame Join with Database Style – 3

Lecture 34: Merging on Index

Lecture 35: Merging on Index – 2

Lecture 36: Merging on Index – 3

Lecture 37: Merging on Index – 4

Lecture 38: Concatenating Along an Axis

Lecture 39: Concatenating Along an Axis – 2

Lecture 40: Concatenating Along an Axis – 3

Lecture 41: Data Combining with the Overlap

Lecture 42: Hierarchical Indexing and Reshaping

Lecture 43: Hierarchical Indexing and Reshaping – 2

Lecture 44: pd.melt

Chapter 4: Introduction to Matplotlib

Lecture 1: Introduction

Lecture 2: Figures and Subplots

Instructors

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  • 4 stars: 4 votes
  • 5 stars: 68 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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