CS50’s Introduction to Programming with Python : Click Here
An introduction to programming using Python, a popular language for general-purpose programming, data science, web programming, and more.
What you’ll learn
- Functions, Variables
- Conditionals
- Loops
- Exceptions
- Libraries
- Unit Tests
- File I/O
- Regular Expressions
- Object-Oriented Programming
- Et Cetera
CS50’s Introduction to Artificial Intelligence with Python : Click Here
Learn to use machine learning in Python in this introductory course on artificial intelligence.
What you’ll learn
- graph search algorithms
- adversarial search
- knowledge representation
- logical inference
- probability theory
- Bayesian networks
- Markov models
- constraint satisfaction
- machine learning
- reinforcement learning
- neural networks
- natural language processing
Python Data Structures: Click Here
The second course in Python for Everybody explores variables that contain collections of data like string, lists, dictionaries, and tuples. Learning how to store and represent and manipulate data collections while a program is running is an important part of learning how to program.
What you’ll learn
- How to open a file and read data from a file
- How to create a list in Python
- How to create a dictionary
- Sorting data
- How to use the tuple structure in Python
Python Basics for Data Science:Click Here
This Python course provides a beginner-friendly introduction to Python for Data Science. Practice through lab exercises, and you’ll be ready to create your first Python scripts on your own!
Syllabus
Module 1 – Python Basics
Your first program
Types
Expressions and Variables
String Operations
Module 2 – Python Data Structures
Lists and Tuples
Sets
Dictionaries
Module 3 – Python Programming Fundamentals
Conditions and Branching
Loops
Functions
Objects and Classes
Module 4 – Working with Data in Python
Reading files with open
Writing files with open
Loading data with Pandas
Working with and Saving data with Pandas
Module 5 – Working with Numpy Arrays
Numpy 1d Arrays
Numpy 2d Arrays
Machine Learning with Python: A Practical Introduction: Click Here
Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.
What you’ll learn
- The difference between the two main types of machine learning methods: supervised and unsupervised
- Supervised learning algorithms, including classification and regression
- Unsupervised learning algorithms, including Clustering and Dimensionality Reduction
- How statistical modeling relates to machine learning and how to compare them
- Real-life examples of the different ways machine learning affects society
Syllabus
Module 1 – Introduction to Machine Learning
Applications of Machine Learning
Supervised vs Unsupervised Learning
Python libraries suitable for Machine Learning
Module 2 – Regression
Linear Regression
Non-linear Regression
Model evaluation methods
Module 3 – Classification
K-Nearest Neighbour
Decision Trees
Logistic Regression
Support Vector Machines
Model Evaluation
Module 4 – Unsupervised Learning
K-Means Clustering
Hierarchical Clustering
Density-Based Clustering
Module 5 – Recommender Systems
Content-based recommender systems
Collaborative Filtering
Analyzing Data with Python: Click Here
In this course, you will learn how to analyze data in Python using multi-dimensional arrays in numpy, manipulate DataFrames in pandas, use SciPy library of mathematical routines, and perform machine learning using scikit-learn!
What you’ll learn
- How to import data sets, clean and prepare data for analysis, summarize data, and build data pipelines
- Use Pandas DataFrames, Numpy multidimensional arrays, and SciPy libraries to work with various datasets
- Load, manipulate, analyze, and visualize datasets with pandas, an open-source library
- Build machine-learning models and make predictions with scikit-learn, another open-source library
It includes following parts:
Data Analysis libraries: will learn to use Pandas DataFrames, Numpy multi-dimentional arrays, and SciPy libraries to work with a various datasets. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions.
Syllabus
Module 1 – Importing Datasets
- Learning Objectives
- Understanding the Domain
- Understanding the Dataset
- Python package for data science
- Importing and Exporting Data in Python
- Basic Insights from Datasets
Module 2 – Cleaning and Preparing the Data
- Identify and Handle Missing Values
- Data Formatting
- Data Normalization Sets
- Binning
- Indicator variables
Module 3 – Summarizing the Data Frame
- Descriptive Statistics
- Basic of Grouping
- ANOVA
- Correlation
- More on Correlation
Module 4 – Model Development
- Simple and Multiple Linear Regression
- Model EvaluationUsingVisualization
- Polynomial Regression and Pipelines
- R-squared and MSE for In-Sample Evaluation
- Prediction and Decision Making
Module 5 – Model Evaluation
- Model Evaluation
- Over-fitting, Under-fitting and Model Selection
- Ridge Regression
- Grid Search
- Model Refinement
Visualizing Data with Python: Click Here
Data visualization is the graphical representation of data in order to interactively and efficiently convey insights to clients, customers, and stakeholders in general.
What you’ll learn
- How to present data using some of the data visualization libraries in Python, including Matplotlib, Seaborn, and Folium
- How to use basic visualization tools, including area plots, histograms, and bar charts
- How to use specialized visualization tools, including pie charts, box plots, scatter plots, and bubble plots
- How to use advanced visualization tools, including waffle charts, word clouds, and Seaborn and regression plots
- How to create maps and visualize geospatial data
Syllabus
Module 1 -Introduction to Visualization Tools
- Introduction to Data Visualization
- Introduction to Matplotlib
- Basic Plotting with Matplotlib
- Dataset on Immigration to Canada
- Line Plots
Module 2 -Basic Visualization Tools
- Area Plots
- Histograms
- Bar Charts
Module 3 -Specialized Visualization Tools
- Pie Charts
- Box Plots
- Scatter Plots
- Bubble Plots
Module 4 -Advanced Visualization Tools
- Waffle Charts
- Word Clouds
- Seaborn and Regression Plots
Module 5 -Creating Maps and Visualizing Geospatial Data
- Introduction to Folium
- Maps with Markers
- Choropleth Maps
Deep Learning with Python and PyTorch: Click Here
This course is the second part of a two-part course on how to develop Deep Learning models using Pytorch.
What you’ll learn
- Apply knowledge of Deep Neural Networks and related machine learning methods
- Build and Train Deep Neural Networks using PyTorch
- Build Deep learning pipelines
Syllabus
Module 1 – Classification
- Softmax Regression
- Softmax in PyTorch Regression
- Training Softmax in PyTorch Regression
Module 2 – Neural Networks
- Introduction to Networks
- Network Shape Depth vs Width
- Back Propagation
- Activation functions
Module 3 – Deep Networks
- Dropout
- Initialization
- Batch normalization
- Other optimization methods
Module 4 – Computer Vision Networks
- Convolution
- Max Polling
- Convolutional Networks
- Pre-trained Networks
Module 5 – Computer Vision Networks
- Convolution
- Max Pooling
- Convolutional Networks
- Training your model with a GPU
- Pre-trained Networks
Module 6 Dimensionality reduction and autoencoders
- Principle component analysis
- Linear autoencoders
- Autoencoders
- Transfer learning
- Deep Autoencoders
Module 7 -Independent Project