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Complete Data Science and Machine Learning Bootcamp

Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!

Created by Andrei & Daniel

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Course Contents

Introduction

Course Outline

Join Our Online Classroom!

Exercise: Meet Your Classmates & Instructor

Asking Questions + Getting Help

Your First Day

Machine Learning 101

What Is Machine Learning?

AI/Machine Learning/Data Science

ZTM Resources

Exercise: Machine Learning Playground

How Did We Get Here?

Exercise: YouTube Recommendation Engine

Types of Machine Learning

Are You Getting It Yet?

What Is Machine Learning? Round 2

Section Review

Monthly Coding Challenges, Free Resources and Guides

Machine Learning and Data Science Framework

Section Overview

Introducing Our Framework

6 Step Machine Learning Framework

Types of Machine Learning Problems

Types of Data

Types of Evaluation

Features In Data

Modelling - Splitting Data

Modelling - Picking the Model

Modelling - Tuning

Modelling - Comparison

Overfitting and Underfitting Definitions

Experimentation

Tools We Will Use

Optional: Elements of AI

The 2 Paths

The 2 Paths

Python + Machine Learning Monthly

Endorsements On LinkedIN

Data Science Environment Setup

Section Overview

Introducing Our Tools

What is Conda?

Conda Environments

Mac Environment Setup

Mac Environment Setup 2

Windows Environment Setup

Windows Environment Setup 2

Linux Environment Setup

Sharing your Conda Environment

Jupyter Notebook Walkthrough

Jupyter Notebook Walkthrough 2

Jupyter Notebook Walkthrough 3

Pandas: Data Analysis

Section Overview

Downloading Workbooks and Assignments

Pandas Introduction

Series, Data Frames and CSVs

Data from URLs

Quick Note: Upcoming Videos

Describing Data with Pandas

Selecting and Viewing Data with Pandas

Selecting and Viewing Data with Pandas Part 2

Manipulating Data

Manipulating Data 2

Manipulating Data 3

Assignment: Pandas Practice

How To Download The Course Assignments

NumPy

Section Overview

NumPy Introduction

Quick Note: Correction In Next Video

NumPy DataTypes and Attributes

Creating NumPy Arrays

NumPy Random Seed

Viewing Arrays and Matrices

Manipulating Arrays

Manipulating Arrays 2

Standard Deviation and Variance

Reshape and Transpose

Dot Product vs Element Wise

Exercise: Nut Butter Store Sales

Comparison Operators

Sorting Arrays

Turn Images Into NumPy Arrays

Assignment: NumPy Practice

Exercise: Imposter Syndrome

Optional: Extra NumPy resources

Matplotlib: Plotting and Data Visualizations

Section Overview

Matplotlib Introduction

Importing And Using Matplotlib

Anatomy Of A Matplotlib Figure

Scatter Plot And Bar Plot

Histograms And Subplots

Subplots Option 2

Quick Tip: Data Visualizations

Plotting From Pandas Data Frames

Quick Note: Regular Expressions

Plotting From Pandas Data Frames 2

Plotting from Pandas DataFrames 3

Plotting from Pandas DataFrames 4

Plotting from Pandas DataFrames 5

Plotting from Pandas DataFrames 6

Plotting from Pandas DataFrames 7

Customizing Your Plots

Customizing Your Plots 2

Assignment: Matplotlib Practice

Scikit-learn: Creating Machine Learning Models

Section Overview

Scikit-learn Introduction

Quick Note: Upcoming Video

Refresher: What Is Machine Learning?

Quick Note: Upcoming Videos

Scikit-learn Cheatsheet

Typical scikit-learn Workflow

Optional: Debugging Warnings In Jupyter

Getting Your Data Ready: Splitting Your Data

Quick Tip: Clean, Transform, Reduce

Getting Your Data Ready: Convert Data To Numbers

Note: Update to next video (OneHotEncoder can handle NaN/None values)

Getting Your Data Ready: Handling Missing Values With Pandas

Extension: Feature Scaling

Note: Correction in the upcoming video (splitting data)

Getting Your Data Ready: Handling Missing Values With Scikit-learn

NEW: Choosing The Right Model For Your Data

NEW: Choosing The Right Model For Your Data 2 (Regression)

Quick Note: Decision Trees

Quick Tip: How ML Algorithms Work

Choosing The Right Model For Your Data 3 (Classification)

Fitting A Model To The Data

Making Predictions With Our Model

predict() vs predict_proba()

NEW: Making Predictions With Our Model (Regression)

NEW: Evaluating A Machine Learning Model (Score) Part 1

NEW: Evaluating A Machine Learning Model (Score) Part 2

Evaluating A Machine Learning Model 2 (Cross Validation)

Evaluating A Classification Model 1 (Accuracy)

Evaluating A Classification Model 2 (ROC Curve)

Evaluating A Classification Model 3 (ROC Curve)

Reading Extension: ROC Curve + AUC

Evaluating A Classification Model 4 (Confusion Matrix)

NEW: Evaluating A Classification Model 5 (Confusion Matrix)

Evaluating A Classification Model 6 (Classification Report)

NEW: Evaluating A Regression Model 1 (R2 Score)

NEW: Evaluating A Regression Model 2 (MAE)

NEW: Evaluating A Regression Model 3 (MSE)

Machine Learning Model Evaluation

NEW: Evaluating A Model With Cross Validation and Scoring Parameter

NEW: Evaluating A Model With Scikit-learn Functions

Improving A Machine Learning Model

Tuning Hyperparameters

Tuning Hyperparameters 2

Tuning Hyperparameters 3

Note: Metric Comparison Improvement

Quick Tip: Correlation Analysis

Saving And Loading A Model

Saving And Loading A Model 2

Putting It All Together

Putting It All Together 2

Scikit-Learn Practice

Supervised Learning: Classification + Regression

Milestone Projects!

Milestone Project 1: Learning (Classification)

Section Overview

Project Overview

Project Environment Setup

Optional: Windows Project Environment Setup Getting a project environment setup on Windows (same as the previous video but tailored for Windows).

Step 1~4 Framework Setup

Note: Code update for next video

Getting Our Tools Ready

Exploring Our Data

Finding Patterns

Finding Patterns 2

Finding Patterns 3

Preparing Our Data For Machine Learning

Choosing The Right Models

Experimenting With Machine Learning Models

Tuning/Improving Our Model

Tuning Hyperparameters

Tuning Hyperparameters 2

Tuning Hyperparameters 3

Quick Note: Confusion Matrix Labels

Note: Code change in upcoming video

Evaluating Our Model

Evaluating Our Model 2

Evaluating Our Model 3

Finding The Most Important Features

Reviewing The Project

Milestone Project 2: Supervised Learning (Time Series Data)

Section Overview

Project Overview

Downloading the data for the next two projects

Project Environment Setup

Step 1~4 Framework Setup

Exploring Our Data

Exploring Our Data 2

Feature Engineering

Turning Data Into Numbers

Filling Missing Numerical Values

Filling Missing Categorical Values

Fitting A Machine Learning Model

Splitting Data

Challenge: What's wrong with splitting data after filling it?

Custom Evaluation Function

Reducing Data

Randomized Search CV

Improving Hyperparameters

Preproccessing Our Data

Making Predictions

Feature Importance

Data Engineering

Data Engineering Introduction

What Is Data?

What Is A Data Engineer?

What Is A Data Engineer 2?

What Is A Data Engineer 3?

What Is A Data Engineer 4?

Types Of Databases

Quick Note: Upcoming Video

Optional: OLTP Databases

Optional: Learn SQL

Hadoop, HDFS and MapReduce

Apache Spark and Apache Flink

Kafka and Stream Processing

Neural Networks: Deep Learning, Transfer Learning and TensorFlow 2

Section Overview

Deep Learning and Unstructured Data

Setting Up With Google

Setting Up Google Colab

Google Colab Workspace

Uploading Project Data

Setting Up Our Data

Setting Up Our Data 2

Importing TensorFlow 2

Optional: TensorFlow 2.0 Default Issue

Using A GPU

Optional: GPU and Google Colab

Optional: Reloading Colab Notebook

Loading Our Data Labels

Preparing The Images

Turning Data Labels Into Numbers

Creating Our Own Validation Set

Preprocess Images

Preprocess Images 2

Turning Data Into Batches

Turning Data Into Batches 2

Visualizing Our Data

Preparing Our Inputs and Outputs

Optional: How machines learn and what's going on behind the scenes?

Building A Deep Learning Model

Building A Deep Learning Model 2

Building A Deep Learning Model 3

Building A Deep Learning Model 4

Summarizing Our Model

Evaluating Our Model

Preventing Overfitting

Training Your Deep Neural Network

Evaluating Performance With TensorBoard

Make And Transform Predictions

Transform Predictions To Text

Visualizing Model Predictions

Visualizing And Evaluate Model Predictions 2

Visualizing And Evaluate Model Predictions 3

Saving And Loading A Trained Model

Training Model On Full Dataset

Making Predictions On Test Images

Submitting Model to Kaggle

Making Predictions On Our Images

Finishing Dog Vision: Where to next?

Storytelling + Communication: How To Present Your Work

Section Overview

Communicating Your Work

Communicating With Managers

Communicating With Co-Workers

Weekend Project Principle

Communicating With Outside World

Storytelling

Communicating and sharing your work: Further reading

Career Advice + Extra Bits

Endorsements On LinkedIn

Quick Note: Upcoming Video

What If I Don't Have Enough Experience?

Learning Guideline

Quick Note: Upcoming Videos

JTS: Learn to Learn

JTS: Start With Why

CWD: Git + Github

CWD: Git + Github 2

Contributing To Open Source

Contributing To Open Source 2

Exercise: Contribute To Open Source

Coding Challenges

Learn Python

What Is A Programming Language

Python Interpreter

How To Run Python Code

Latest Version Of Python

Our First Python Program

Python 2 vs Python 3

Exercise: How Does Python Work?

Learning Python

Python Data Types

How To Succeed

Numbers

Math Functions

DEVELOPER FUNDAMENTALS: I

Operator Precedence

Exercise: Operator Precedence

Optional: bin() and complex

Variables

Expressions vs Statements

Augmented Assignment Operator

Strings

String Concatenation

Type Conversion

Escape Sequences

Formatted Strings

String Indexes

Immutability

Built-In Functions + Methods

Booleans

Exercise: Type Conversion

DEVELOPER FUNDAMENTALS: II

Exercise: Password Checker

Lists

List Slicing

Matrix

List Methods

List Methods 2

List Methods 3

Common List Patterns

None

Dictionaries

DEVELOPER FUNDAMENTALS: III

Dictionary Keys

Dictionary Methods

Dictionary Methods 2

Tuples

Tuples 2

Sets

Sets 2

Learn Python Part 2

Breaking The Flow

Conditional Logic

Indentation In Python

Truthy vs Falsey

Ternary Operator

Short Circuiting

Logical Operators

Exercise: Logical Operators

is vs ==

For Loops

Iterables

Exercise: Tricky Counter

range()

enumerate()

While Loops

While Loops 2

break, continue, pass

Our First GUI

DEVELOPER FUNDAMENTALS: IV

Exercise: Find Duplicates

Functions

Parameters and Arguments

Default Parameters and Keyword Arguments

return

Exercise: Tesla

Methods vs Functions

Docstrings

Clean Code

*args and **kwargs

Exercise: Functions

Scope

Scope Rules

global Keyword

nonlocal Keyword

Why Do We Need Scope?

Pure Functions

map()

filter()

zip()

reduce()

List Comprehensions

Set Comprehensions

Exercise: Comprehensions

Python Exam: Testing Your Understanding

Modules in Python

Quick Note: Upcoming Videos

Optional: PyCharm

Packages in Python

Different Ways To Import

Next Steps

Bonus Resource: Python Cheatsheet

Extra: Learn Advanced Statistics and Mathematics

Statistics and Mathematics

Where To Go From Here

Become An Alumni

Thank You

Thank You Part 2

Course Review

The Final Challenge

BONUS SECTION

Special Bonus Lecture

What you will learn and gain

  • Become a Data Scientist and get hired
  • Master Machine Learning and use it on the job
  • Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
  • Use modern tools that big tech companies like Google, Apple, Amazon and Meta use
  • Present Data Science projects to management and stakeholders
  • Learn which Machine Learning model to choose for each type of problem
  • Real life case studies and projects to understand how things are done in the real world
  • Learn best practices when it comes to Data Science Workflow
  • Implement Machine Learning algorithms
  • Learn how to program in Python using the latest Python 3
  • How to improve your Machine Learning Models
  • Learn to pre process data, clean data, and analyze large data.
  • Build a portfolio of work to have on your resume
  • Developer Environment setup for Data Science and Machine Learning
  • Supervised and Unsupervised Learning
  • Machine Learning on Time Series data
  • Explore large datasets using data visualization tools like Matplotlib and Seaborn
  • Explore large datasets and wrangle data using Pandas
  • Learn NumPy and how it is used in Machine Learning
  • A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided
  • Learn to use the popular library Scikit-learn in your projects
  • Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry
  • Learn to perform Classification and Regression modelling
  • Learn how to apply Transfer Learning

What our students are saying

This is a top selling Machine Learning and Data Science course just updated this month with the latest trends and skills for 2023! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 900,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Meta, + other top tech companies. 

You will go from zero to mastery!Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).

This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks.

If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects.

Don't worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world (We show you fully fledged Data Science and Machine Learning projects and give you programming Resources and Cheatsheets)!

The topics covered in this course are:- Data Exploration and Visualizations- Neural Networks and Deep Learning- Model Evaluation and Analysis- Python 3- Tensorflow 2.0- Numpy- Scikit-Learn- Data Science and Machine Learning Projects and Workflows- Data Visualization in Python with MatPlotLib and Seaborn- Transfer Learning- Image recognition and classification- Train/Test and cross validation- Supervised Learning: Classification, Regression and Time Series- Decision Trees and Random Forests- Ensemble Learning- Hyperparameter Tuning- Using Pandas Data Frames to solve complex tasks- Use Pandas to handle CSV Files- Deep Learning / Neural Networks with TensorFlow 2.0 and Keras- Using Kaggle and entering Machine Learning competitions- How to present your findings and impress your boss- How to clean and prepare your data for analysis- K Nearest Neighbours- Support Vector Machines- Regression analysis (Linear Regression/Polynomial Regression)- How Hadoop, Apache Spark, Kafka, and Apache Flink are used- Setting up your environment with Conda, MiniConda, and Jupyter Notebooks- Using GPUs with Google ColabBy the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more.

By the end, you will have a stack of projects you have built that you can show off to others.Here's the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you don't know where to go from there or how to build your own projects. Or they show you a lot of code and complex math on the screen, but they don't really explain things well enough for you to go off on your own and solve real life machine learning problems.Whether you are new to programming, or want to level up your Data Science skills, or are coming from a different industry, this course is for you.

This course is not about making you just code along without understanding the principles so that when you are done with the course you don't know what to do other than watch another tutorial. No! This course will push you and challenge you to go from an absolute beginner with no Data Science experience, to someone that can go off, forget about Daniel and Andrei, and build their own Data Science and Machine learning workflows.

Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more.

The skills learned in this course are going to give you a lot of options for your career.You hear statements like Artificial Neural Network, or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean!

Requirements

  • No prior experience is needed (not even Math and Statistics). We start from the very basics.
  • A computer (Linux/Windows/Mac) with internet connection.
  • Two paths for those that know programming and those that don't.
  • All tools used in this course are free for you to use.

Who this course is for:

  • Anyone with zero experience (or beginner/junior) who wants to learn Machine Learning, Data Science and Python  
  • You are a programmer that wants to extend their skills into Data Science and Machine Learning to make yourself more valuable
  • Anyone who wants to learn these topics from industry experts that don't only teach, but have actually worked in the field
  • You're looking for one single course to teach you about Machine learning and Data Science and get you caught up to speed with the industry
  • You want to learn the fundamentals and be able to truly understand the topics instead of just watching somebody code on your screen for hours without really "getting it"
  • You want to learn to use Deep learning and Neural Networks with your projects
  • You want to add value to your own business or company you work for, by using powerful Machine Learning tools.

Frequently Asked Questions

What is Heroic Universal Concept All About?

Heroic Universal Concept International is an ICT firm and a vision-driven movement aimed at harnessing the ingenuity of young people with the use of motivational and ICT tools, thereby helping them achieve their ultimate goal.

We offer ICT services and champion talent development programmes for young people.

Is your training online or physical?

Our training is online. We have hundreds of downloadable tutorial videos on different ICT courses hosted in our website.

As soon as you pay and register, you will be given access to the tutorial videos. You can download them and commence training, offline.

While you're learning, I will be guiding you from here whenever you need my assistance.

Where is your location?

We're based in Kasoa, Ghana and Lagos, Nigeria but our training is online. So, you can access our courses from anywhere you are while we will be coaching you from here.

Do you have a physical class?

No, we don't.

Our training is 100% online.

What is your mode of teaching?

We teach with tutorial videos.

We have hundreds of downloadable tutorial videos on various ICT courses in our website.

You will have access to download them and learn with them as soon as you register for your desired course.

We made all the tutorials downloadable to help you save data and be free from challenges of poor internet connection.

Do you have online community where your students converge to exchange ideas?

We have Heroic Students WhatsApp group where our students converge and exchange ideas.

Information about Heroic Academy and other Heroic innovations are discussed in the group.

Do you mentor your students?

Sure, I do absolutely!

I'm always available Whenever my students need my attention.

I supervise their projects and tackle their challenges.

Can ICT courses be learnt online effectively?

Of course! The more convenient way to learn ICT skills these days is online.

There are some courses you may never have the opportunity of learning if you're waiting for opportunity to learn them physically.

Professionals are fast moving online. Very few of them will still have time to teach physically.

Programming is one of those skills you can easily learn online.

All you need is a good and experienced coach, and I'm here to help.

Is programming difficult?

It depends on who is coaching you and the time you dedicate to learning it.

If you're being coached by a very experienced and gifted coach, if you give it enough time, programming will be fun for you.

However, if you're struggling to learn by yourself or you're being coached by an inexperienced coach, you will find programming difficult.

Can I learn coding with my phone?

Yes, you can start learning with your phone if you don't have a laptop. Just buy a phone external keyboard, I will direct you on how to use it to start programming.

How do you mentor your students?

I mentor my students by; answering their questions and helping them solve some problems whenever they call my attention.

If my student need my attention, I usually ask them to send me a screenshot of the issue or a zip file of their work to enable me analyse their job and debug the error.

I usually make a new video to point out their errors if the situation is complex.

How can you convince me that you're genuine?

Chat me on Whatsapp via +2348037747461 and express your concern.

I will try my best to clear all your doubt.

I'm not used to online training, I'm afraid it won't work for me.

Online training is great when a gifted teacher is handling it. Look for a gifted teacher. You will be missing opportunity to acquire great skills if you ignore online training.

Some skills are not common. Online training make them accessible.

If you have tried online training before and it didn't work for you, try again, this time, concentrate on getting a good teacher.

I tried my best to simply my lectures. You can chat me to see my students testimonials. They're testifying that I made my courses very simple.

Don't you think that poor internet connection can disrupt online training?

I made all my tutorials downloadable to win against poor internet connection.

You can download all your tutorials when the internet connection is ok and learn offline without worrying about network fluctuations.

What if I insist on one-on-one training. What's your advice?

Online training would have made learning a lot easier for you but if you insist on physical training, you have to take the pain of looking for someone or centers where the skills are taught.

The centers might not be close to you, so be ready to spent heavily on transportation.

I wrote an article where I compared online and physical training. You can chat me on Whatsapp to request for it.

I'm interested, how do I get started?

Go ahead and order for the course. If you need assistance, chat me on Whatsapp via +2348037747461.

Will I get certificate after the training?

Of course. Our organization is registered. We give our students certificate on completion.

I'm interested in the courses but I don't have money

If you can't afford the expensive courses, there are some cheap courses you can start from.

Just pay for the ones you can afford and get started instead of waiting.

You will gradually raise money to pay for the rest.

Are all your tutorial videos downloadable?

All the tutorials the Admin of this platform, Uche Joe created by himself are downloadable but the tutorials created by other expert partners created might not be downloadable.

However, you will surely enjoy the learning process.

About the Instructor

Daniel Bourke:

A self-taught Machine Learning Engineer who lives on the internet with an uncurable desire to take long walks and fill up blank pages.

My experience in machine learning comes from working at one of Australia's fastest-growing artificial intelligence agencies, Max Kelsen.

I've worked on machine learning and data problems across a wide range of industries including healthcare, eCommerce, finance, retail and more.Two of my favourite projects include building a machine learning model to extract information from doctors notes for one of Australia's leading medical research facilities, as well as building a natural language model to assess insurance claims for one of Australia's largest insurance groups.Due to the performance of the natural language model (a model which reads insurance claims and decides which party is at fault), the insurance company were able to reduce their daily assessment load by up to 2,500 claims.

My long-term goal is to combine my knowledge of machine learning and my background in nutrition to work towards answering the question "what should I eat?".Aside from building machine learning models on my own, I love writing about and making videos on the process. My articles and videos on machine learning on Medium, personal blog and YouTube have collectively received over 5-million views.

I love nothing more than a complicated topic explained in an entertaining and educative matter. I know what it's like to try and learn a new topic, online and on your own. So I pour my soul into making sure my creations are accessible as possible.My modus operandi (a fancy term for my way of doing things) is learning to create and creating to learn. If you know the Japanese word for this concept, please let me know.Questions are always welcome.

--------

Andrei Neagoie:

Andrei is the instructor of the highest rated Development courses on Udemy as well as one of the fastest growing. His graduates have moved on to work for some of the biggest tech companies around the world like Apple, Google, Amazon, JP Morgan, IBM, UNIQLO etc... 

He has been working as a senior software developer in Silicon Valley and Toronto for many years, and is now taking all that he has learned, to teach programming skills and to help you discover the amazing career opportunities that being a developer allows in life.Having been a self taught programmer, he understands that there is an overwhelming number of online courses, tutorials and books that are overly verbose and inadequate at teaching proper skills. 

Most people feel paralyzed and don't know where to start when learning a complex subject matter, or even worse, most people don't have $20,000 to spend on a coding bootcamp. Programming skills should be affordable and open to all. An education material should teach real life skills that are current and they should not waste a student's valuable time.

Having learned important lessons from working for Fortune 500 companies, tech startups, to even founding his own business, he is now dedicating 100% of his time to teaching others valuable software development skills in order to take control of their life and work in an exciting industry with infinite possibilities.Andrei promises you that there are no other courses out there as comprehensive and as well explained. 

He believes that in order to learn anything of value, you need to start with the foundation and develop the roots of the tree. Only from there will you be able to learn concepts and specific skills(leaves) that connect to the foundation. Learning becomes exponential when structured in this way.Taking his experience in educational psychology and coding, Andrei's courses will take you on an understanding of complex subjects that you never thought would be possible.

₵625

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