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Become a professional Data Scientist and learn Machine Learning, Data Analysis + Visualization, Web Apps + more!

Reviews: 1

**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
- Matplotlib
- Seaborn
- Plotly

**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
- Probability
- 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
- PCA

**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
- Networking

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 - 02mins

DS + ML Marketplace - 06mins

Data Science Job Opportunities - 04mins

Data Science Job Roles - 10mins

What is a Data Scientist - 17mins

How To Get a Data Science Job - 18mins

Data Science Projects Overview - 11mins

Why We Use Python - 03mins

What is Data Science - 13mins

What is Machine Learning - 14mins

ML Concepts & Algorithms - 14mins

Machine Learning vs Deep Learning - 11mins

What is Deep Learning - 09mins

What is Python Programming - 06mins

Why Python for Data Science - 04mins

What is Jupyter - 03mins

What is Colab - 03mins

Jupyter Notebook - 18mins

Getting Started with Colab - 09mins

Python Variables, Booleans and None - 11mins

Python Operators - 25mins

Compound Data Types and When to use each Data Type - 12mins

Python Numbers and Booleans - 07mins

Python Strings - 13mins

Python Conditional Statements - 13mins

Python For Loops and While Loops - 08mins

Python Lists - 05mins

More About Python Lists - 15mins

Python Tuples - 11mins

Python Dictionaries - 20mins

Python Sets - 09mins

Functions - 14mins

Python Object Oriented Programming - 18mins

Intro to Statistics - 07mins

Descriptive Statistics - 06mins

Measure of Variability - 12mins

Measure of Variability Continued - 09mins

Measures of Variable Relationship - 07mins

Inferential Statistics - 15mins

Measures of Asymmetry - 01mins

Sampling Distribution - 07mins

What Exactly Probability - 03mins

Expected Values - 02mins

Relative Frequency - 05mins

Hypothesis Testing Overview - 09mins

NumPy Array Data Types - 12mins

NumPy Arrays - 08mins

NumPy Array Basics - 11mins

NumPy Array Indexing - 09mins

NumPy Array Computations - 05mins

Broadcasting - 04mins

Intro to Pandas - 15mins

Intro to Panda Continued - 18mins

Data Visualization Overview - 24mins

Different Data Visualization Libraries in Python - 12mins

Python Data Visualization Implementation - 08mins

Intro to ML - 26mins

Exploratory Data Analysis - 13mins

Feature Scaling - 07mins

Data Cleaning - 07mins

Feature Engineering - 06mins

Linear Regression Intro - 08mins

Gradient Descent - 05mins

Linear Regression + Correlation Methods - 26mins

Linear Regression Implementation - 05mins

Logistic Regression - 03mins

KNN Overview - 03mins

Parametic vs Non-Parametic Models - 03mins

EDA on Iris Dataset - 22mins

KNN - Intuition - 02mins

Implement the KNN algorithm from scratch - 11mins

Compare the Result with Sklearn Library - 03mins

KNN Hyperparameter tuning using the cross-validation - 10mins

The decision boundary visualization - 04mins

KNN - Manhattan vs Euclidean Distance - 11mins

KNN Scaling in KNN - 06mins

Curse of dimensionality - 08mins

KNN use cases - 03mins

KNN pros and cons - 05mins

Decision Trees Section Overview - 04mins

EDA on Adult Dataset - 16mins

What is Entropy and Information Gain - 21mins

The Decision Tree ID3 algorithm from scratch Part 1 - 11mins

The Decision Tree ID3 algorithm from scratch Part 2 - 07mins

The Decision Tree ID3 algorithm from scratch Part 3 - 04mins

ID3 - Putting Everything Together - 21mins

Evaluating our ID3 implementation - 16mins

Compare with Sklearn implementation - 08mins

Visualizing the Tree - 10mins

Plot the features importance - 05mins

Decision Trees Hyper-parameters - 11mins

Pruning - 17mins

[Optional] Gain Ration - 02mins

Decision Trees Pros and Cons - 07mins

[Project] Predict whether income exceeds $50Kyr - Overview - 02mins

Ensemble Learning Section Overview - 03mins

What is Ensemble Learning - 13mins

What is Bootstrap Sampling - 08mins

What is Bagging - 05mins

Out-of-Bag Error - 07mins

Implementing Random Forests from scratch Part 1 - 22mins

Implementing Random Forests from scratch Part 2 - 06mins

Compare with sklearn implementation - 03mins

Random Forests Hyper-Parameters - 04mins

Random Forests Pros and Cons - 05mins

What is Boosting - 04mins

AdaBoost Part 1 - 04mins

AdaBoost Part 2 - 14mins

SVM - Outline - 05mins

SVM - SVM intuition - 11mins

SVM - Hard vs Soft Margin - 13mins

SVM - C HP - 04mins

SVM - Kernel Trick - 12mins

SVM - Kernel Types - 18mins

SVM - Linear Dataset - 13mins

SVM - Non-Linear Dataset - 12mins

SVM with Regression - 05mins

SVM - Project Overview - 04mins

Unsupervised Machine Learning Intro - 20mins

Representation of Clusters - 20mins

Data Standardization - 19mins

PCA - Section Overview - 05mins

What is PCA - 09mins

PCA - Drawbacks - 03mins

PCA - Algorithm Steps - 13mins

PCA - Cov vs SVD - 04mins

PCA - Main Applications - 02mins

PCA - Image Compression Scratch - 27mins

PCA - Data Preprocessing Scratch - 14mins

PCA - BiPlot - 17mins

PCA - Feature Scaling and Screeplot - 09mins

PCA - Supervised vs unsupervised - 04mins

PCA - Visualization - 07mins

Creating a Data Science Resume - 06mins

Data Science Cover Letter - 03mins

How To Contact Recruiters - 04mins

Getting Started with Freelancing - 04mins

Top Freelance Websites - 05mins

Personal Branding - 04mins

Networking Do's and Don'ts - 03mins

Importance of a Website - 02mins

**What you'll learn/Goals**

- 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

Hi I'm Juan. I've been an Entrepreneur since grade school. My background is in the tech space from Digital Marketing, E-commerce, Web Development to Programming. I believe in continuous education with the best of a University Degree without all the downsides of burdensome costs and inefficient methods. I look forward to helping you expand your skillsets.

Sunil Tiwari Bishwakarma

** (0.5)**

This course needs an update.

14-Sep-2023 10:50:29 PM

14-Sep-2023 10:50:29 PM

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