Become a professional Data Scientist and learn how to use NumPy, Pandas, Machine Learning and more!
☑ Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant
☑ Learn data cleaning, processing, wrangling and manipulation
☑ How to create resume and land your first job as a Data Scientist
☑ How to use Python for Data Science
☑ How to write complex Python programs for practical industry scenarios
☑ Learn Plotting in Python (graphs, charts, plots, histograms etc)
☑ Learn to use NumPy for Numerical Data
☑ Machine Learning and it’s various practical applications
☑ Supervised vs Unsupervised Machine Learning
☑ Learn Regression, Classification, Clustering and Sci-kit learn
☑ Machine Learning Concepts and Algorithms
☑ K-Means Clustering
☑ Use Python to clean, analyze, and visualize data
☑ Building Custom Data Solutions
☑ Statistics for Data Science
☑ Probability and Hypothesis Testing
Learn Python for Data Science & Machine Learning from A-Z
In this practical, hands-on course you’ll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner.
Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job.
We’ll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib +
- NumPy — A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.
- Pandas — A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work.
NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs as you’d find in Excel or Google Sheets. Blending practical work with solid theoretical training, we take you from the basics of Python Programming for Data Science to mastery.
This Machine Learning with Python course dives into the basics of machine learning using Python. You’ll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.
We understand that theory is important to build a solid foundation, we understand that theory alone isn’t going to get the job done so that’s why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the Python programming language, this course is for you!
Python coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding Python coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.
Together we’re going to give you the foundational education that you need to know not just on how to write code in Python, analyze and visualize data and utilize machine learning algorithms but also how to get paid for your newly developed programming skills.
The course covers 5 main areas:
1: PYTHON FOR DS+ML COURSE INTRO
This intro section gives you a full introduction to the Python for Data Science and Machine Learning course, data science industry, and marketplace, job opportunities and salaries, and the various data science job roles.
- Intro to Data Science + Machine Learning with Python
- Data Science Industry and Marketplace
- Data Science Job Opportunities
- How To Get a Data Science Job
- Machine Learning Concepts & Algorithms
2: PYTHON DATA ANALYSIS/VISUALIZATION
This section gives you a full introduction to the Data Analysis and Data Visualization with Python with hands-on step by step training.
- Python Crash Course
- NumPy Data Analysis
- Pandas Data Analysis
3: MATHEMATICS FOR DATA SCIENCE
This section gives you a full introduction to the mathematics for data science such as statistics and probability.
- Descriptive Statistics
- Measure of Variability
- Inferential Statistics
- Hypothesis Testing
4: MACHINE LEARNING
This section gives you a full introduction to Machine Learning including Supervised & Unsupervised ML with hands-on step-by-step training.
- Intro to Machine Learning
- Data Preprocessing
- Linear Regression
- Logistic Regression
- K-Nearest Neighbors
- Decision Trees
- Ensemble Learning
- Support Vector Machines
- K-Means Clustering
5: STARTING A DATA SCIENCE CAREER
This section gives you a full introduction to starting a career as a Data Scientist with hands-on step by step training.
- Creating a Resume
- Creating a Cover Letter
- Personal Branding
- Freelancing + Freelance websites
- Importance of Having a Website
By the end of the course you’ll be a professional Data Scientist with Python and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up.
Who is This Course For?
Data Science + Machine Learning Marketplace
Data Science Job Opportunities
Data Science Job Roles
What is a Data Scientist?
How To Get a Data Science Job
Data Science Projects Overview
Data Science & Machine Learning Concepts
Why We Use Python?
What is Data Science?
What is Machine Learning?
Machine Learning Concepts & Algorithms
What is Deep Learning?
Machine Learning vs Deep Learning
Python For Data Science
What is Programming?
Why Python for Data Science?
What is Jupyter?
What is Google Colab?
Python Variables, Booleans and None
Getting Started with Google Colab
Python Numbers & Booleans
Python Conditional Statements
Python For Loops and While Loops
More about Lists
Compound Data Types & When to use each one?
Object Oriented Programming in Python
Statistics for Data Science
Intro To Statistics
Measure of Variability
Measure of Variability Continued
Measures of Variable Relationship
Measure of Asymmetry
Probability & Hypothesis Testing
What is Exactly is Probability?
Hypothesis Testing Overview
NumPy Data Analysis
Intro NumPy Array Data Types
NumPy Arrays Basics
NumPy Array Indexing
NumPy Array Computations
Pandas Data Analysis
Introduction to Pandas
Introduction to Pandas Continued
Python Data Visualization
Data Visualization Overview
Different Data Visualization Libraries in Python
Python Data Visualization Implementation
Introduction To Machine Learning
Data Loading & Exploration
Exploratory Data Analysis
Feature Selecting and Engineering
Linear and Logistic Regression
Linear Regression Intro
Linear Regression + Correlation Methods
Linear Regression Implementation
K Nearest Neighbors
parametric vs non-parametric models
EDA on Iris Dataset
The KNN Intuition
Implement the KNN algorithm from scratch
Compare the result with the sklearn library
Hyperparameter tuning using the cross-validation
The decision boundary visualization
Manhattan vs Euclidean Distance
Feature scaling in KNN
Curse of dimensionality
KNN use cases
KNN pros and cons
Decision Trees Section Overview
EDA on Adult Dataset
What is Entropy and Information Gain?
The Decision Tree ID3 algorithm from scratch Part 1
The Decision Tree ID3 algorithm from scratch Part 2
The Decision Tree ID3 algorithm from scratch Part 3
ID3 – Putting Everything Together
Evaluating our ID3 implementation
Compare with Sklearn implementation
Visualizing the tree
Plot the features importance
Decision Trees Hyper-parameters
[Optional] Gain Ration
Decision Trees Pros and Cons
[Project] Predict whether income exceeds $50K/yr – Overview
Ensemble Learning and Random Forests
Ensemble Learning Section Overview
What is Ensemble Learning?
What is Bootstrap Sampling?
What is Bagging?
Out-of-Bag Error (OOB Error)
Implementing Random Forests from scratch Part 1
Implementing Random Forests from scratch Part 2
Compare with sklearn implementation
Random Forests Hyper-Parameters
Random Forests Pros and Cons
What is Boosting?
AdaBoost Part 1
AdaBoost Part 2
Support Vector Machines
Hard vs Soft Margins
SVM – Kernel Types
SVM with Linear Dataset (Iris)
SVM with Non-linear Dataset
SVM with Regression
SMV – Project Overview
Unsupervised Machine Learning Intro
Unsupervised Machine Learning Continued
PCA Section Overview
What is PCA?
PCA Algorithm Steps (Mathematics)
Covariance Matrix vs SVD
PCA – Main Applications
PCA – Image Compression
PCA Data Preprocessing
PCA – Biplot and the Screen Plot
PCA – Feature Scaling and Screen Plot
PCA – Supervised vs Unsupervised
PCA – Visualization
Data Science Career
Creating A Data Science Resume
Data Science Cover Letter
How to Contact Recruiters
Getting Started with Freelancing
Top Freelance Websites
Networking Do’s and Don’ts
Importance of a Website