Explain machine learning and its technologies
Discuss and apply Python fundamentals
Understand the NumPy package
Use data analysis using Pandas and data visualization
Implement supervised (regression and classification) & unsupervised (clustering) machine learning
Use various analysis and visualization tools associated with Python, such as Matplotlib, Seaborn etc.
Describe the behavior of data in Python models
Understand how to use the various Python libraries to manipulate data, like Numpy, Pandas and Scikit-Learn
Use Python libraries and work on data manipulation, data preparation and data explorations
This Python for Data Science course is an introduction to Python and how to apply it in data science. The course contains ~60 lectures and 7.5 hours of content taught by Praba Santanakrishnan, a highly experienced data scientist from Microsoft.
Staring with some fundamentals about “what is data science,” and “who is a data scientist,” the program rapidly move into the specific challenges of data science. This includes the challenges of problem definitions and collecting data, to data pipelines, data preparation, data cleaning and related subjects. Data science methodologies, data analytics tools and open source tools are all covered. Model building validation, visualization and various data science applications are also covered. Discussion of the types of machine learning are covered, including supervised and unsupervised machine learning, as well as methodologies and clustering. NumPy, Pandas, Python Notebook, Git, REPL, IDS and Jupyter Notebook are also covered. Arrays, advanced arrays, and matrices are discussed in some detail to ensure you understand what it is all about and how these tools are implemented.
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Introduction to Machine Learning and It’s Technologies
Segment – 02-introduction-to-data-science-fin
Segment – 03
Segment – 04-doing-data-science
Segment – 05-problem-definitions-and-collecting-data
Segment – 06-data-pipelines-preparation-cleaning-understanding
Segment – 07-model-building-validation-visualization-data-science-applications
Segment – 08-data-science-methodology-data-analytics-tools-open-source-tools
Segment – 09-data-science-future-readings
Segment – 10-ai-primer-and-machine-learning-concepts
Segment – 11-machine-learning-applications
Segment – 12-machine-learning-supervised-unsupervised
Segment – 12-types-of-machine-learning NUMBERING ISSUE, FIX
Segment – 13-supervised-unsupervised-learning-methodology-clustering
Segment – 14-python-vs-r
Segment – 15-tools-for-scalable-machine-learning
Segment – 16-introduction-to-python
Segment – 17-more-python-details
Segment – 18-python-examples
Segment – 19-anaconda-navigator
Python Fundamentals & NumPy Package
Segment – 20-intro
Segment – 20-introduction-to-python-notebook
Segment – 21-git-and-repl
Segment – 22-introduction-ids-and-juypter-notebook
Segment – 23-lab-tutorials-learning-juypter-notebook
Segment – 24-python-loops-and-functions
Segment – 25-python-objects-introduction
Segment – 26-python-numpy
Segment – 27-arrays
Segment – 28-advanced-arrays
Segment – 29-matrices
Data Analysis using Pandas and Data Visualization
Segment – 30-numpy-lab-tutorial
Segment 31 -review-session-python-for-data-science
Segment 32 – Why Pandas
Segment 33 – Data Series
Segment 34 – Series, Keys and Indices
Segment 35 – NumPy Array vs. Panda Series
Segment 36 – Dataframe
Segment 37 – Dataframe Operations
Segment 38 – Using Lambda
Segment 39 – Dataframe Operations (Continued)
Segment 40 – Statistical Analysis, Calculations and Operations
Segment 41 – Lab – Advanced Operations in Action
Segment 42 – Lab – Advanced Operations in Action (Continued)
Segment 43 – Pandas Visualization and Matplotlib
Segment 44 – Seaborn
Segment 45 – ggplot
Segment 46 – Statistical Graphs
Segment 47 – Lab – Visualizations
Supervised (Regression and Classification) & Unsupervised (Clustering) Machine L
Segment 48 – Introduction to Scikit-Learn
Segment 49 – Scikit-Learn Uses and Applications
Segment 50 – Scikit-Learn vs. Other Tools
Segment 51 – Scikit-Learn Classes, Utils and Data Sets
Segment 51 – Setting Up Scikit-Learn
Segment 52 – Estimators and Algorithms
Segment 53 – Preprocessing and Feature Engineering
Segment 54 – Metrics
Segment 55 – Clustering
Segment 56 – Prediction
Segment 57 – Principal Component Analysis
Segment 58 – Lab – Classification Algorithm
