Flex AI & Machine Learning Microtrack

Become a AI & Machine Learning expert using the most sought-after curriculum in the industry!

  • 6 months | 100% online | Live group sessions 6 months | 100% online | Live group sessions
  • Beginner & advanced friendly Beginner & advanced friendly

Get the Flex Software Engineering coursebook

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300+

PLACEMENT PARTNERS

86%

GRADUATION RATE

$81,310

AVERAGE SALARY

$23k

SALARY INCREASE

Skills & technologies covered

Curriculum

Skills & technologies covered

1
Intro to Machine Learning and AI
2
Data Preprocessing and Exploration
3
Supervised Learning Algorithms
4
Unsupervised Learning Algorithms
5
Deep Learning Fundamentals
6
Model Evaluation and Fine-tuning
7
Real-world Applications and Case Studies
8
Ethical Considerations in AI and ML
9
Integration and Deployment
10
Capstone Project and Career Development

MODULE 1

Intro to Machine Learning and AI

This module provides an overview of machine learning (ML) and artificial intelligence (AI) concepts. It covers the role of ML and AI in various industries and introduces popular ML algorithms and their applications. The module also discusses the importance of data preprocessing and exploration in preparing data for ML models.

Technologies Used:

Python, Jupyter Notebook, scikit-learn, pandas, NumPy, Matplotlib, Seaborn,

MODULE 2

Data Preprocessing and Exploration

In this module, the focus is on data cleaning techniques such as handling missing values and detecting outliers. It also covers exploratory data analysis (EDA) and feature engineering, which involve analyzing and visualizing the data to gain insights and creating new features to improve model performance.

Technologies Used:

Python, Jupyter Notebook, pandas, NumPy, scikit-learn, Matplotlib,Seaborn

MODULE 3

Supervised Learning Algorithms

Supervised learning algorithms are introduced in this module, including linear regression and logistic regression for regression and classification tasks respectively. The module covers decision trees and random forests as well as evaluation metrics used to assess the performance of regression and classification models.

Technologies Used:

Python, Jupyter Notebook, scikit-learn, pandas, NumPy

MODULE 4

Unsupervised Learning Algorithms

This module explores unsupervised learning techniques, starting with clustering algorithms such as k-means and hierarchical clustering for grouping data based on similarities. It also covers dimensionality reduction techniques like principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) for reducing the number of features in high-dimensional data. Evaluation methods for clustering algorithms are also discussed.

Technologies Used:

Python, Jupyter Notebook, scikit-learn, pandas, NumPy, k-means, hierarchical clustering, Principal Component Analysis (PCA),

MODULE 5

Deep Learning Fundamentals

The fundamentals of deep learning are covered in this module. It introduces neural networks and explains the process of building and training them using popular frameworks such as TensorFlow or PyTorch. The module also explores convolutional neural networks (CNNs) specifically designed for image classification tasks.

Technologies Used:

Python, Jupyter Notebook, TensorFlow, PyTorch, Keras, image preprocessing, text tokenization

MODULE 6

Model Evaluation and Fine-tuning

This module focuses on techniques for evaluating and fine-tuning ML models. It covers cross-validation methods for robust model evaluation, hyperparameter tuning, and regularization techniques to optimize model performance. Strategies for handling overfitting and underfitting issues are also discussed.

Technologies Used:

Python, Jupyter Notebook, scikit-learn, pandas, NumPy

MODULE 7

Real-world Applications and Case Studies

This module delves into real-world applications of ML and AI across various industries. It explores case studies in fields like healthcare and finance, showcasing how ML is utilized to solve complex problems. The module encourages identifying opportunities for ML integration in different domains.

Technologies Used:

Jupyter Notebook, TensorFlow, scikit-learn, OpenCV, NLTK

Module 8

Ethical Considerations in AI and ML

Ethical considerations play a vital role in AI and ML development. This module addresses topics such as bias and fairness in ML models, privacy and security considerations, and responsible AI practices. It emphasizes the importance of developing ML models that are unbiased, secure, and adhere to ethical standards.

Technologies Used:

AI and ML applications

Module 9

Integration and Deployment

ML models need to be integrated into existing workflows and systems for practical use. This module explores methods for integrating ML models, including utilizing cloud services and APIs for ML deployment. It also covers model deployment considerations and best practices.

Technologies Used:

Python, Flask, Django

Module 10

Capstone Project and Career Development

The final module focuses on applying ML techniques to a real-world problem through a capstone project. It emphasizes building a portfolio showcasing ML projects to demonstrate skills and expertise. Additionally, the module explores various career opportunities in AI and ML, providing insights into potential paths and roles in the field.

1
Intro to Machine Learning and AI

MODULE 1

Intro to Machine Learning and AI

This module provides an overview of machine learning (ML) and artificial intelligence (AI) concepts. It covers the role of ML and AI in various industries and introduces popular ML algorithms and their applications. The module also discusses the importance of data preprocessing and exploration in preparing data for ML models.

Technologies Used:

Python, Jupyter Notebook, scikit-learn, pandas, NumPy, Matplotlib, Seaborn,

2
Data Preprocessing and Exploration

MODULE 2

Data Preprocessing and Exploration

In this module, the focus is on data cleaning techniques such as handling missing values and detecting outliers. It also covers exploratory data analysis (EDA) and feature engineering, which involve analyzing and visualizing the data to gain insights and creating new features to improve model performance.

Technologies Used:

Python, Jupyter Notebook, pandas, NumPy, scikit-learn, Matplotlib,Seaborn

3
Supervised Learning Algorithms

MODULE 3

Supervised Learning Algorithms

Supervised learning algorithms are introduced in this module, including linear regression and logistic regression for regression and classification tasks respectively. The module covers decision trees and random forests as well as evaluation metrics used to assess the performance of regression and classification models.

Technologies Used:

Python, Jupyter Notebook, scikit-learn, pandas, NumPy

4
Unsupervised Learning Algorithms

MODULE 4

Unsupervised Learning Algorithms

This module explores unsupervised learning techniques, starting with clustering algorithms such as k-means and hierarchical clustering for grouping data based on similarities. It also covers dimensionality reduction techniques like principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) for reducing the number of features in high-dimensional data. Evaluation methods for clustering algorithms are also discussed.

Technologies Used:

Python, Jupyter Notebook, scikit-learn, pandas, NumPy, k-means, hierarchical clustering, Principal Component Analysis (PCA),

5
Deep Learning Fundamentals

MODULE 5

Deep Learning Fundamentals

The fundamentals of deep learning are covered in this module. It introduces neural networks and explains the process of building and training them using popular frameworks such as TensorFlow or PyTorch. The module also explores convolutional neural networks (CNNs) specifically designed for image classification tasks.

Technologies Used:

Python, Jupyter Notebook, TensorFlow, PyTorch, Keras, image preprocessing, text tokenization

6
Model Evaluation and Fine-tuning

MODULE 6

Model Evaluation and Fine-tuning

This module focuses on techniques for evaluating and fine-tuning ML models. It covers cross-validation methods for robust model evaluation, hyperparameter tuning, and regularization techniques to optimize model performance. Strategies for handling overfitting and underfitting issues are also discussed.

Technologies Used:

Python, Jupyter Notebook, scikit-learn, pandas, NumPy

7
Real-world Applications and Case Studies

MODULE 7

Real-world Applications and Case Studies

This module delves into real-world applications of ML and AI across various industries. It explores case studies in fields like healthcare and finance, showcasing how ML is utilized to solve complex problems. The module encourages identifying opportunities for ML integration in different domains.

Technologies Used:

Jupyter Notebook, TensorFlow, scikit-learn, OpenCV, NLTK

8
Ethical Considerations in AI and ML

Module 8

Ethical Considerations in AI and ML

Ethical considerations play a vital role in AI and ML development. This module addresses topics such as bias and fairness in ML models, privacy and security considerations, and responsible AI practices. It emphasizes the importance of developing ML models that are unbiased, secure, and adhere to ethical standards.

Technologies Used:

AI and ML applications

9
Integration and Deployment

Module 9

Integration and Deployment

ML models need to be integrated into existing workflows and systems for practical use. This module explores methods for integrating ML models, including utilizing cloud services and APIs for ML deployment. It also covers model deployment considerations and best practices.

Technologies Used:

Python, Flask, Django

10
Capstone Project and Career Development

Module 10

Capstone Project and Career Development

The final module focuses on applying ML techniques to a real-world problem through a capstone project. It emphasizes building a portfolio showcasing ML projects to demonstrate skills and expertise. Additionally, the module explores various career opportunities in AI and ML, providing insights into potential paths and roles in the field.

Dive deeper into our Full-Time curriculum

Schedule

Daily schedule

Flex learning allows you to get the coding bootcamp experience on your own time. You are able to move at your own speed, free from deadlines and class schedules. You’ll learn to program through a mix of recorded lectures, coding exercises, and projects. Our hybrid approach to async learning interjects live 1:1 technical coaching, grading and feedback, and weekly group sessions into your learning experience

Support

We set students up for success

1:1 Live support sessions with an instructor

1:1 Live support sessions with an instructor

Weekly peer programming and code wars sessions

Weekly peer programming and code wars sessions

Real projects and graded assignments

Real projects and graded assignments

Dedicated Student Success Manager

Dedicated Student Success Manager

Slack community

Slack community

Fine-tune your communication

Fine-tune your communication

Build your personal brand

Build your personal brand

Sharpen your technical skills

Sharpen your technical skills

Leverage our employer network

Leverage our employer network

Build a meaningful community

Build a meaningful community

Utilize resources and templates

Utilize resources and templates

Careers

We offer lifetime career services

Our post-graduation services provide each student with the necessary resources, tools, and guidance to build a meaningful career. We provide 1-1 support for the entirety of your professional journey.

Learn more

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Quick Questions

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We’ll work 1-1 to get your questions answered.

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Quick Questions

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See what our program is like and learn how to get started.

Ask questions during our live Q&A.

Curriculum Reviews

Don’t just take our word for it. Hear from our

Flex graduates

Five Stars

"My overall experience at Coding Temple was super fun and insightful. As a student with zero-knowledge in programming, I was quite apprehensive about diving into the idea of this dreadful 3 month Bootcamp torture people make it out to be, but it was quite the opposite at the temple. In fact, I felt at ease the moment I did the introductory class and got to know more about the school curriculum and my classmates."

Tenzin L.

Five Stars

"Coding Temple will take you where you want to go. It is hard to see progress in yourself until its exceptionally obvious, but trust the reviews and the program. For me, the structure, and expert guidance was the allure for this camp and I was not disappointed. I highly recommend this course, for anyone looking to get into software development as a career."

Joseph M.

Five Stars

"This was the best decision I have ever made. I learned so much during the three months at Coding Temple. I can now build full stack applications!! How amazing is that!! With the skillset I acquired at Coding Temple, I was able to land a job as a Software Engineer. I could not have landed it without the amazing instructors at Coding Temple."

Aydee R.

Five Stars

“I didn't want a program that was set up as a bunch of videos to watch with no real support. I could get that from YouTube. So when I was researching the different bootcamp options out there, I liked that Coding Temple offered an instructor. Most did for full-time/in-person courses but CT also offers instructor support in their Self-Paced program. The instructor was extremely helpful when I ran into issues or needed a bit more explanation on various things we were learning. The assignments were engaging, dare I say FUN and challenged you to truly apply what you were learning in the course.”

Jennifer C.

Tuition + payment plans breakdown

Program cost

Save $500

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Save $500 on program cost by paying upfront. No additional payments required.

$ 3,495

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$ 3,495

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Admissions process

How to get started

1

Explore our career paths and courses

Ready to start your new career in tech? Explore our different career tracks and see which path interests you the most!

Explore Courses
2

Application and basic skills assessment

Submit your application – it takes less than 5 minutes. After you apply you will be sent a basic skills assessment. Our assessment is meant to test your cognitive skills. Don’t stress! We want to know if you can think like a programmer, if you can we will take it from there!

Apply Now
3

Schedule an admissions call

Talk with our admissions team so we can get to understand your career goals and answer any questions you have about our program.

Schedule a Call
4

Secure your seat and enroll

Finalize your payment plan to secure your seat! Once you’re enrolled, you will gain instant access to our preparatory work, slack channels, and 1:1 support prior to class.

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