Code, Train, Deploy: The AI Engineer’s Path to Success
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Welcome to the AI Mastery Bootcamp, a comprehensive, hands-on program designed to transform beginners into skilled AI engineers. Over the course of 16 weeks, you will learn how to build, train, and deploy machine learning models, step by step, using the latest tools and techniques. This bootcamp focuses on practical skills, empowering you to apply artificial intelligence to solve real-world problems and create innovative solutions.
The course starts with the fundamentals, covering essential topics like Python programming, data preprocessing, and an introduction to machine learning. As you progress, you’ll dive deeper into advanced concepts such as neural networks, deep learning, and natural language processing. You will also explore powerful AI frameworks like TensorFlow, PyTorch, and Hugging Face, which are essential for modern AI development.
This bootcamp is ideal for anyone passionate about artificial intelligence, whether you’re starting from scratch or looking to deepen your expertise. You don’t need any prior experience with AI—just a willingness to learn and explore. By the end of the program, you’ll have the skills and confidence to build AI solutions from the ground up, making you ready to take on industry challenges or pursue advanced AI research.
Join us on this exciting journey and become a part of the future of technology!
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1
Introduction to Week 1 Python Programming Basics -
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Day 1: Introduction to Python and Development Setup
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3
Day 2: Control Flow in Python
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4
Day 3: Functions and Modules
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5
Day 4: Data Structures (Lists, Tuples, Dictionaries, Sets)
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6
Day 5: Working with Strings
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7
Day 6: File Handling
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8
Day 7: Pythonic Code and Project Work
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9
Coding Exercise 1
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10
Introduction to Week 2 Data Science Essentials
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11
Day 1: Introduction to NumPy for Numerical Computing
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12
Day 2: Advanced NumPy Operations
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13
Day 3: Introduction to Pandas for Data Manipulation
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14
Day 4: Data Cleaning and Preparation with Pandas
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15
Day 5: Data Aggregation and Grouping in Pandas
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16
Day 6: Data Visualization with Matplotlib and Seaborn
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17
Day 7: Exploratory Data Analysis (EDA) Project
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18
Introduction to Week 3 Mathematics for Machine Learning
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Day 1: Linear Algebra Fundamentals
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Day 2: Advanced Linear Algebra Concepts
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Day 3: Calculus for Machine Learning (Derivatives)
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22
Day 4: Calculus for Machine Learning (Integrals and Optimization)
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23
Day 5: Probability Theory and Distributions
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24
Day 6: Statistics Fundamentals
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25
Day 7: Math-Driven Mini Project – Linear Regression from Scratch
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26
Introduction to Week 4 Probability and Statistics for Machine Learning
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27
Day 1: Probability Theory and Random Variables
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28
Day 2: Probability Distributions in Machine Learning
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29
Day 3: Statistical Inference - Estimation and Confidence Intervals
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30
Day 4: Hypothesis Testing and P-Values
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31
Day 5: Types of Hypothesis Tests
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32
Day 6: Correlation and Regression Analysis
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Day 7: Statistical Analysis Project – Analyzing Real-World Data
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34
Introduction to Week 5 Introduction to Machine Learning
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35
Day 1: Machine Learning Basics and Terminology
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36
Day 2: Introduction to Supervised Learning and Regression Models
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37
Day 3: Advanced Regression Models – Polynomial Regression and Regularization
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38
Day 4: Introduction to Classification and Logistic Regression
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Day 5: Model Evaluation and Cross-Validation
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40
Day 6: k-Nearest Neighbors (k-NN) Algorithm
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Day 7: Supervised Learning Mini Project
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42
Introduction to Week 6 Feature Engineering and Model Evaluation
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Day 1: Introduction to Feature Engineering
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Day 2: Data Scaling and Normalization
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Day 3: Encoding Categorical Variables
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Day 4: Feature Selection Techniques
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Day 5: Creating and Transforming Features
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Day 6: Model Evaluation Techniques
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49
Day 7: Cross-Validation and Hyperparameter Tuning
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50
Introduction to Week 7 Advanced Machine Learning Algorithms
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51
Day 1: Introduction to Ensemble Learning
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Day 2: Bagging and Random Forests
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53
Day 3: Boosting and Gradient Boosting
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Day 4: Introduction to XGBoost
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Day 5: LightGBM and CatBoost
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Day 6: Handling Imbalanced Data
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Day 7: Ensemble Learning Project – Comparing Models on a Real Dataset
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Introduction to Week 8 Model Tuning and Optimization
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59
Day 1: Introduction to Hyperparameter Tuning
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Day 2: Grid Search and Random Search
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Day 3: Advanced Hyperparameter Tuning with Bayesian Optimization
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Day 4: Regularization Techniques for Model Optimization
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Day 5: Cross-Validation and Model Evaluation Techniques
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Day 6: Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearchCV
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Day 7: Optimization Project – Building and Tuning a Final Model
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Introduction to Week 9 Neural Networks and Deep Learning Fundamentals
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67
Day 1: Introduction to Deep Learning and Neural Networks
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Day 2: Forward Propagation and Activation Functions
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69
Day 3: Loss Functions and Backpropagation
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70
Day 4: Gradient Descent and Optimization Techniques
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Day 5: Building Neural Networks with TensorFlow and Keras
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72
Day 6: Building Neural Networks with PyTorch
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Day 7: Neural Network Project – Image Classification on CIFAR-10
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74
Introduction to Week 10 Convolutional Neural Networks (CNNs)
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75
Day 1: Introduction to Convolutional Neural Networks
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76
Day 2: Convolutional Layers and Filters
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Day 3: Pooling Layers and Dimensionality Reduction
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78
Day 4: Building CNN Architectures with Keras and TensorFlow
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Day 5: Building CNN Architectures with PyTorch
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Day 6: Regularization and Data Augmentation for CNNs
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81
Day 7: CNN Project – Image Classification on Fashion MNIST or CIFAR-10
