Skills & technologies covered
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.
Python, Jupyter Notebook, scikit-learn, pandas, NumPy, Matplotlib, Seaborn,
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.
Python, Jupyter Notebook, pandas, NumPy, scikit-learn, Matplotlib,Seaborn
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.
Python, Jupyter Notebook, scikit-learn, pandas, NumPy
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.
Python, Jupyter Notebook, scikit-learn, pandas, NumPy, k-means, hierarchical clustering, Principal Component Analysis (PCA),
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.
Python, Jupyter Notebook, TensorFlow, PyTorch, Keras, image preprocessing, text tokenization
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.
Python, Jupyter Notebook, scikit-learn, pandas, NumPy
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.
Jupyter Notebook, TensorFlow, scikit-learn, OpenCV, NLTK
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.
AI and ML applications
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.
Python, Flask, Django
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
Immersive Data Analytics bootcamp
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
We set students up for success
1:1 Live support sessions with an instructor
Weekly peer programming and code wars sessions
Real projects and graded assignments
Dedicated Student Success Manager
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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"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."
"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."
"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."
“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.”
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