Complete Python Course: Data Science, Artificial Intelligence, and Machine Learning from basics to advanced
A warm welcome to the Data Science, Artificial Intelligence, and Machine Learning with Python course by Uplatz.
Data Science
Data Science is an interdisciplinary field focused on extracting knowledge and insights from structured and unstructured data. It involves various techniques from statistics, computer science, and information theory to analyze and interpret complex data.
Key Components:
Data Collection: Gathering data from various sources.
Data Cleaning: Preparing data for analysis by handling missing values, outliers, etc.
Data Exploration: Analyzing data to understand its structure and characteristics.
Data Analysis: Applying statistical and machine learning techniques to extract insights.
Data Visualization: Presenting data in a visual context to make the analysis results understandable.
Python in Data Science
Python is widely used in Data Science because of its simplicity and the availability of powerful libraries:
Pandas: For data manipulation and analysis.
NumPy: For numerical computations.
Matplotlib and Seaborn: For data visualization.
SciPy: For advanced statistical operations.
Jupyter Notebooks: For interactive data analysis and sharing code and results.
Artificial Intelligence (AI)
Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart.” It includes anything from a computer program playing a game of chess to voice recognition systems like Siri and Alexa.
Key Components:
Expert Systems: Computer programs that emulate the decision-making ability of a human expert.
Natural Language Processing (NLP): Understanding and generating human language.
Robotics: Designing and programming robots to perform tasks.
Computer Vision: Interpreting and understanding visual information from the world.
Python in AI
Python is preferred in AI for its ease of use and the extensive support it provides through various libraries:
TensorFlow and PyTorch: For deep learning and neural networks.
OpenCV: For computer vision tasks.
NLTK and spaCy: For natural language processing.
Scikit-learn: For general machine learning tasks.
Keras: For simplifying the creation of neural networks.
Machine Learning (ML)
Machine Learning is a subset of AI that involves the development of algorithms that allow computers to learn from and make predictions or decisions based on data. It can be divided into supervised learning, unsupervised learning, and reinforcement learning.
Key Components:
Supervised Learning: Algorithms are trained on labeled data.
Unsupervised Learning: Algorithms find patterns in unlabeled data.
Reinforcement Learning: Algorithms learn by interacting with an environment to maximize some notion of cumulative reward.
Python in Machine Learning
Python is highly utilized in ML due to its powerful libraries and community support:
Scikit-learn: For implementing basic machine learning algorithms.
TensorFlow and PyTorch: For building and training complex neural networks.
Keras: For simplifying neural network creation.
XGBoost: For gradient boosting framework.
LightGBM: For gradient boosting framework optimized for speed and performance.
Python serves as a unifying language across these domains due to:
Ease of Learning and Use: Python's syntax is clear and readable, making it accessible for beginners and efficient for experienced developers.
Extensive Libraries and Frameworks: Python has a rich ecosystem of libraries that simplify various tasks in data science, AI, and ML.
Community and Support: A large and active community contributes to a wealth of resources, tutorials, and forums for problem-solving.
Integration Capabilities: Python can easily integrate with other languages and technologies, making it versatile for various applications.
Artificial Intelligence, Data Science, and Machine Learning with Python - Course Curriculum
1. Overview of Artificial Intelligence, and Python Environment Setup
Essential concepts of Artificial Intelligence, data science, Python with Anaconda environment setup
2. Introduction to Python Programming for AI, DS and ML
Basic concepts of python programming
3. Data Importing
Effective ways of handling various file types and importing techniques
4. Exploratory Data Analysis & Descriptive Statistics
Understanding patterns, summarizing data
5. Probability Theory & Inferential Statistics
Core concepts of mastering statistical thinking and probability theory
6. Data Visualization
Presentation of data using charts, graphs, and interactive visualizations
7. Data Cleaning, Data Manipulation & Pre-processing
Garbage in - Garbage out (Wrangling/Munging): Making the data ready to use in statistical models
8. Predictive Modeling & Machine Learning
Set of algorithms that use data to learn, generalize, and predict
1. Overview of Data Science and Python Environment Setup
Overview of Data Science
Introduction to Data Science
Components of Data Science
Verticals influenced by Data Science
Data Science Use cases and Business Applications
Lifecycle of Data Science Project
Python Environment Setup
Introduction to Anaconda Distribution
Installation of Anaconda for Python
Anaconda Navigator and Jupyter Notebook
Markdown Introduction and Scripting
Spyder IDE Introduction and Features
2. Introduction to Python Programming
Variables, Identifiers, and Operators
Variable Types
Statements, Assignments, and Expressions
Arithmetic Operators and Precedence
Relational Operators
Logical Operators
Membership Operators
Iterables / Containers
Strings
Lists
Tuples
Sets
Dictionaries
Conditionals and Loops
if else
While Loop
For Loop
Continue, Break and Pass
Nested Loops
List comprehensions
Functions
Built-in Functions
User-defined function
Namespaces and Scope
Recursive Functions
Nested function
Default and flexible arguments
Lambda function
Anonymous function
3. Data Importing
Flat-files data
Excel data
Databases (MySQL, SQLite...etc)
Statistical software data (SAS, SPSS, Stata...etc)
web-based data (HTML, XML, JSON...etc)
Cloud hosted data (Google Sheets)
social media networks (Facebook Twitter Google sheets APIs)
4. Data Cleaning, Data Manipulation & Pre-processing
Handling errors, missing values, and outliers
Irrelevant and inconsistent data
Reshape data (adding, filtering, and merging)
Rename columns and data type conversion
Feature selection and feature scaling
useful Python packages
Numpy
Pandas
Scipy
5. Exploratory Data Analysis & Descriptive Statistics
Types of Variables & Scales of Measurement
Qualitative/Categorical
Nominal
Ordinal
Quantitative/Numerical
Discrete
Continuous
Interval
Ratio
Measures of Central Tendency
Mean, median, mode,
Measures of Variability & Shape
Standard deviation, variance, and Range, IQR
Skewness & Kurtosis
Univariate data analysis
Bivariate data analysis
Multivariate Data analysis
6. Probability Theory & Inferential Statistics
Probability & Probability Distributions
Introduction to probability
Relative Frequency and Cumulative Frequency
Frequencies of cross-tabulation or Contingency Tables
Probabilities of 2 or more Events
Conditional Probability
Independent and Dependent Events
Mutually Exclusive Events
Bayes’ Theorem
binomial distribution
uniform distribution
chi-squared distribution
F distribution
Poisson distribution
Student's t distribution
normal distribution
Sampling, Parameter Estimation & Statistical Tests
Sampling Distribution
Central Limit Theorem
Confidence Interval
Hypothesis Testing
z-test, t-test, chi-squared test, ANOVA
Z scores & P-Values
Correlation & Covariance
7. Data Visualization
Plotting Charts and Graphics
Scatterplots
Bar Plots / Stacked bar chart
Pie Charts
Box Plots
Histograms
Line Graphs
ggplot2, lattice packages
Matplotlib & Seaborn packages
Interactive Data Visualization
Plot ly
8. Statistical Modeling & Machine Learning
Regression
Simple Linear Regression
Multiple Linear Regression
Polynomial regression
Classification
Logistic Regression
K-Nearest Neighbors (KNN)
Support Vector Machines
Decision Trees, Random Forest
Naive Bayes Classifier
Clustering
K-Means Clustering
Hierarchical clustering
DBSCAN clustering
Association Rule Mining
Apriori
Market Basket Analysis
Dimensionality Reduction
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Ensemble Methods
Bagging
Boosting
9. End to End Capstone Project
Debes tener en cuenta que los cupones duran maximo 4 dias o hasta agotar 1000 inscripciones,pero puede vencer en cualquier momento. Obten el curso con cupon haciendo clic en el siguiente boton:
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