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Next-Level Python for Data Science and Machine Learning (Intermediate)
Description
Next-Level Python for Data Science and Machine Learning (Intermediate) Introduction
Unlock the full potential of Python for data science and machine learning through our intermediate course, “Next-Level Python for Data Science and Machine Learning.” Geared towards professionals equipped with foundational knowledge in data analytics and basic Python skills, this course is your gateway to mastering advanced techniques in data science and machine learning.
Dive into the depths of exploratory data analysis, sophisticated data visualization, and intricate data processing on large-scale datasets, commonly referred to as “Big Data.” Gain expertise in handling complex data scenarios with ease.
Throughout this comprehensive course, you will sharpen your skills in leveraging indispensable Python libraries and tools. From NumPy for efficient numerical processing to Pandas for seamless data manipulation, and TensorFlow for cutting-edge deep learning, you’ll harness a toolkit of essential resources. Additionally, you’ll explore vital tools like SciPy, SciKit-Learn, matplotlib, PIL, and Seaborn, expanding your repertoire for tackling diverse data challenges.
Structured around immersive, hands-on sessions, our curriculum ensures that every concept learned is immediately put into practice. Engage with real-world data scenarios, refining your techniques and honing your problem-solving abilities. By the course’s end, you’ll be equipped with the expertise needed to tackle advanced applications in data science and machine learning confidently. Embark on this journey to elevate your Python proficiency and unlock new possibilities in the realm of data analysis.
Next-Level Python for Data Science and Machine Learning (Intermediate) Course Objectives
- Practical Application: Engage in a hands-on learning environment where approximately 50% of the course is dedicated to practical labs and exercises, supplemented by expert lectures and real-world case studies.
- Python in Data Science: Gain proficiency in using Python effectively within the realm of data science, focusing on real-world applications.
- Utilize Core Libraries: Master the use of essential Python libraries such as NumPy for numerical data manipulation, Pandas for data structuring and analysis, and Matplotlib for creating data visualizations.
- Image Processing with PIL: Learn to create and process images using the Python Imaging Library (PIL), enhancing your skills in handling multimedia data.
- Advanced Data Visualization: Develop advanced visualization techniques with Seaborn to make informative and compelling graphical representations of data.
- Explore SciPy and Scikit-Learn: Dive deep into the functionalities of SciPy for scientific computing and Scikit-Learn for machine learning, focusing on their key features and applications in data analysis and predictive modeling.
Prerequisites
Attending students are required to have a background in basic Python.
Take Before: Students should have attended or have incoming skills equivalent to those in the following courses:
- Fast Track to Python for Data Science
- Applied Python for Data Science & Engineering
Audience
- This course is geared for experienced data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics or eventual machine learning tasks.
Next-Level Python for Data Science and Machine Learning (Intermediate) Outline
Python Review
- Why Python?
- Python syntax compared to other programming languages
- Python interpreter
- Strings
- Understanding lists
- Tuples and Sets
- Dictionaries
- Parsing command-line arguments
- Decision making
- Loops
- Iterators
- Generators
- Functions & Modules
NumPy Arrays and Vectorized Computation
- NumPy arrays
- Array functions
- Data processing using arrays
- Linear algebra with NumPy
- NumPy random numbers
SciPy
- Cluster
- Constants
- FFTpack
- Integrate
- Interpolate
- Linalg
- Ndimage
- Spatial
Introducing Pandas
- Data in the 21st century
- Introducing pandas
- A tour of pandas
- Summary
The DataFrame Object
- Overview of a DataFrame
- Similarities between Series and DataFrames
- Sorting by index
- Setting a new index
- Selecting columns and rows from a DataFrame
- Selecting rows from a DataFrame
- Extracting values from Series
- Renaming columns or rows
- Resetting an index
Filtering a DataFrame
- Optimizing a data set for memory use
- Filtering by a single condition
- Filtering by multiple conditions
- Filtering by condition
- Dealing with duplicates
- Coding challenge
Merging, Joining, and Concatenating
- Introducing the data sets
- Concatenating data sets
- Missing values in concatenated DataFrames
- Left joins
- Inner joins
- Outer joins
- Merging on index labels
- Coding challenge
Visualization Using Matplotlib
- A crash course in Matplotlib
- Covariance and correlation
- Conditional probability
- Bayes’ theorem
Using PIL/Pillow
- Overview
- How to Install Pillow
- How to Load and Display Images
- How to Convert Images to NumPy Arrays and Back
- How to Save Images to File
- How to Resize Images
- How to Flip, Rotate, and Crop Images
- Extensions
Visualization Using Seaborn
- Introduction
- Handling Data with pandas DataFrame
- Plotting with pandas and seaborn
- Tweaking Plot Parameters
Machine Learning with scikit-learn
- An overview of machine learning models
- The scikit-learn modules for different models
- Data representation in scikit-learn
- Supervised learning – classification and regression
- Unsupervised learning – clustering and dimensionality reduction
- Measuring prediction performance
Bonus Content / Time Permitting
TensorFlow Overview
- Introduction
- What are Neural Networks?
- Why Do Neural Networks Work So Well?
- Configuring a Deep Learning Environment
- Exploring a Trained Neural Network
$2595.00
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5 Days Course |