Data Science Mastery: Journey into Machine Learning from Udemy

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.

What's inside

Learning objectives

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.

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.

Syllabus

Introduction to Numpy

Numpy ndarray

ndarray

Data Types

Arithmetic

Indexing and Slicing

Indexing and Slicing - 2

Indexing and Slicing - 3

Boolean Indexing

Fancy Indexing in Numpy

Transposing and Swapping

Universal Functions

Array Oriented Programming

Expressing Conditional Logic

Methods involving Math and Statistics

Boolean Array Methods

The Sorting

Unique Set Logic

Linear Algebra

Pseudorandom Number Generator

Random Walks (An example)

Simulation of plenty of Random Walks

Introduction to Pandas

Ranking and Sorting - 2

Series

Series - 2

Series - 3

DataFrame

DataFrame - 2

DataFrame - 3

DataFrame - 4

Index Objects

Reindexing

Reindexing - 2

Axis and the Dropping of Values

Indexing

Indexing - 2

Using loc and iloc for Selection

Integer Indexes

Data Alignment & Arithmetic

Data Alignment & Arithmetic - 2

Fill Values with Arithmetic Methods

DataFrame and Series and the Operation

Application and Mapping

Application and Mapping - 2

Ranking and Sorting

Axis Indexes

Computing Descriptive Statistics

Computing Descriptive Statistics - 2

Value Counts, Membership, Unique Values

Data Preparation and Data Cleaning

Lets Handle Missing Data

Filtration of the Missing Data

Filling of the Missing Data

Duplicates Removal

Function or Mapping and Transformation

Function or Mapping

Function or Mapping - 2

Values Replacing

Axis Indexes Renaming

Discretization and Binning

Discretization and Binning - 2

Discretization and Binning - 3

Filtering and Detecting the Outliers

Random Sampling and Permutations

Indicator Computing

Indicator Computing - 2

Indicator Computing - 3

Indicator Computing - 4

String Object Methods

String Object Methods - 2

Regular Expressions

Regular Expressions - 2

Regular Expressions - 3

Vectorized String Functions

Vectorized String Functions - 2

Hierarchical Indexing

Hierarchical Indexing - 2

Hierarchical Indexing - 3

Reordering and the Sorting Levels

Summarizing Statistics and Indexing with DataFrames Columns

DataFrame Join with Database Style

DataFrame Join with Database Style - 2

DataFrame Join with Database Style - 3

Merging on Index

Merging on Index - 2

Merging on Index - 3

Merging on Index - 4

Concatenating Along an Axis

Concatenating Along an Axis - 2

Concatenating Along an Axis - 3

Data Combining with the Overlap

Hierarchical Indexing and Reshaping

Hierarchical Indexing and Reshaping - 2

pd.melt

Introduction to Matplotlib

Introduction

Figures and Subplots

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Science Mastery: Journey into Machine Learning with these activities:

Review Linear Algebra Fundamentals

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Reinforce your understanding of linear algebra concepts, which are crucial for understanding many machine learning algorithms.

Browse courses on Linear Algebra

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Review key concepts like vectors, matrices, and matrix operations.
Work through practice problems involving linear transformations and eigenvalue decomposition.

Read 'Python Data Science Handbook'

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Deepen your understanding of Python data science libraries and techniques.

View Python Data Science Handbook: Essential Tools... on Amazon

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Read the chapters relevant to NumPy, Pandas, and Matplotlib.
Experiment with the code examples provided in the book.

Practice Pandas Data Manipulation

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Sharpen your Pandas skills by working through data manipulation exercises.

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Download a sample dataset from Kaggle or UCI Machine Learning Repository.
Perform data cleaning, filtering, and aggregation operations using Pandas.
Practice merging and joining data from multiple sources.

Four other activities

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Create a Data Visualization Portfolio

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Showcase your data visualization skills by creating a portfolio of interactive visualizations.

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Choose several datasets and visualization techniques.
Create interactive visualizations using Matplotlib and Seaborn.
Write a short description of each visualization and its insights.

Build a Simple Regression Model

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Apply your machine learning knowledge by building a regression model from scratch.

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Choose a regression problem and find a suitable dataset.
Preprocess the data and select relevant features.
Train a linear regression model using scikit-learn.
Evaluate the model's performance using appropriate metrics.

Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'

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Expand your knowledge of machine learning algorithms and deep learning frameworks.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read the chapters relevant to the machine learning algorithms covered in the course.
Experiment with the code examples provided in the book.

Contribute to a Data Science Project

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Gain real-world experience by contributing to an open-source data science project.

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Find an open-source data science project on GitHub.
Identify a bug or feature request that you can contribute to.
Submit a pull request with your changes.

Career center

Learners who complete Data Science Mastery: Journey into Machine Learning will develop knowledge and skills that may be useful to these careers:

Data Scientist

As a Data Scientist, you'll analyze large datasets to extract meaningful insights and develop data-driven solutions. This involves data cleaning, preprocessing, statistical analysis, and machine learning. This course helps you gain proficiency in these areas. The emphasis on Python programming, coupled with the study of NumPy, Pandas, and Matplotlib, helps you manipulate and visualize data effectively. Furthermore, the skills acquired in exploratory data analysis, statistical analysis, and machine learning implementation help you draw impactful conclusions from data.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning models. This professional builds data pipelines, trains models, and integrates them into applications. The Python for Data Science and Machine Learning course equips you with a strong foundation in Python programming and key libraries like NumPy, Pandas, and scikit-learn, essential for implementing machine learning algorithms. The course's thorough coverage of exploratory data analysis, feature engineering, and model evaluation directly translates into success in this role, particularly its hands-on experience in training and optimizing models.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

A Data Analyst examines data to identify trends, patterns, and insights that help organizations make better decisions. This role involves data collection, cleaning, analysis, and visualization. The Python for Data Science and Machine Learning course helps you master the necessary skills. The course's modules on data cleaning and preprocessing, as well as exploratory data analysis, provide the tools needed to ensure data quality and extract useful information. Proficiency in Python and libraries like Pandas and Matplotlib, as provided by the course, is crucial for effective data manipulation and visualization.

See salaries and explore the career path for Data Analyst

Predictive Modeler

A Predictive Modeler develops statistical and machine learning models to predict future outcomes. The Python for Data Science and Machine Learning course helps in gaining proficiency in Python programming and machine learning algorithms. The practical experience in training, evaluating, and optimizing machine learning models using libraries like scikit-learn is directly applicable to predictive modeling. The skills in feature engineering and model selection contribute to creating accurate and reliable predictions.

See salaries and explore the career path for Predictive Modeler

AI Developer

An Artificial Intelligence Developer builds and implements AI solutions, often involving machine learning, deep learning, and natural language processing, or NLP. This role works on creating intelligent systems and applications. The Python for Data Science and Machine Learning course helps AI Developers by covering both machine learning and deep learning concepts. The course's emphasis on practical implementation using TensorFlow or PyTorch helps developers build and train neural networks to solve real-world problems.

See salaries and explore the career path for AI Developer

Data Engineer

A Data Engineer designs, builds, and maintains data pipelines and infrastructure. This role focuses on ensuring data is accessible and reliable for analysis. The Python for Data Science and Machine Learning course focuses on Python programming and data manipulation techniques. Learning how to use Pandas, NumPy, and other Python libraries for data cleaning and preprocessing can be crucial for building robust data pipelines. The course's coverage of data preparation helps in ensuring data quality and reliability.

See salaries and explore the career path for Data Engineer

Machine Learning Scientist

A Machine Learning Scientist researches and develops new machine learning algorithms and techniques. This role typically requires an advanced degree. The Python for Data Science and Machine Learning course can be very helpful as a first step. The course's thorough coverage of machine learning fundamentals, coupled with hands-on experience in implementing and optimizing models using scikit-learn, helps prepare one for advanced research in machine learning. The skills in feature engineering and model evaluation are essential for innovating in this field.

See salaries and explore the career path for Machine Learning Scientist

Machine Learning Consultant

A Machine Learning Consultant advises organizations on how to leverage machine learning to solve business problems. The Python for Data Science and Machine Learning course can be invaluable by helping build a strong understanding of machine learning principles and implementation. The course's modules on model evaluation, optimization, and feature engineering equip consultants with the knowledge to recommend effective machine learning solutions. The practical experience in building and training models provides a competitive edge.

See salaries and explore the career path for Machine Learning Consultant

Deep Learning Engineer

A Deep Learning Engineer specializes in designing, building, and deploying deep learning models using neural networks. The Python for Data Science and Machine Learning course can be useful as it explores the basics of deep learning, including neural networks and gradient descent optimization. The course's practical experience in building and training neural networks using TensorFlow or PyTorch helps to gain practical skills. This is essential for solving real-world problems using deep learning techniques.

See salaries and explore the career path for Deep Learning Engineer

Statistician

A Statistician collects, analyzes, and interprets data to draw conclusions and make predictions. This role typically requires an advanced degree. The Python for Data Science and Machine Learning course helps statisticians by developing proficiency in statistical analysis and applying statistical methods to interpret and draw conclusions from data. The course's coverage of Python libraries such as NumPy and Pandas helps in data manipulation and cleaning, while the knowledge of machine learning algorithms allows creating predictive models.

See salaries and explore the career path for Statistician

Quantitative Analyst

A Quantitative Analyst develops and implements mathematical models for financial analysis and risk management. This role typically requires an advanced degree. The Python for Data Science and Machine Learning course may be useful as it provides a foundation in Python and statistical analysis techniques. The course's coverage of NumPy, Pandas, and statistical methods helps students develop quantitative skills, and the use of machine learning algorithms helps in predictive modeling, which is essential for success in quantitative finance.

See salaries and explore the career path for Quantitative Analyst

Business Intelligence Analyst

A Business Intelligence Analyst uses data to understand business trends and opportunities. This role turns data insights into actionable business strategies. The Python for Data Science and Machine Learning course may be useful by building a foundation in Python programming and data analysis techniques. With a focus on exploratory data analysis, statistical methods, and data visualization using libraries such as NumPy, Pandas, and Matplotlib, this course provides a strong starting point for understanding and interpreting complex business data.

See salaries and explore the career path for Business Intelligence Analyst

Research Scientist

A Research Scientist conducts experiments and analyzes data to advance scientific knowledge. This role typically requires an advanced degree. The Python for Data Science and Machine Learning course may be useful by providing a foundation in statistical analysis and data manipulation. The course's focus on Python programming and libraries such as NumPy and Pandas helps in handling and analyzing large datasets. The knowledge of machine learning algorithms gained in this course helps in discovering patterns and insights from data.

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Data Architect

A Data Architect designs and oversees the implementation of data management systems. The Python for Data Science and Machine Learning course may be useful by offering comprehensive knowledge of Python programming and data science techniques. The course's coverage of data cleaning, preprocessing, and statistical analysis can equip learners with the skills to design efficient and reliable data architectures. The knowledge of machine learning algorithms helps in optimizing data storage and retrieval.

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Bioinformatician

A Bioinformatician analyzes biological data using computational tools and statistical methods. This role often requires an advanced degree. The Python for Data Science and Machine Learning course may be useful by providing a foundation in Python programming, data manipulation, and statistical analysis. The course's thorough coverage of NumPy and Pandas allows for efficient data handling, while knowledge of machine learning helps in identifying patterns in biological datasets. These are essential for making discoveries in bioinformatics.

See salaries and explore the career path for Bioinformatician

Data Science Mastery

Journey into Machine Learning

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