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Coding fundamentals & Introduction

Welcome to the
Ai Engineer: Data Science & AI and Machine Learning Engineering Bootcamp!
Review of Key Concepts
Let’s talk about git..
Introduction to Data Science & ML
Introduction to Databases
Introduction to SQL
Gestalt ~~Laws~~ Principles and Visualisation
Exploratory Data Analysis
EDA & Presenting your Results

Machine Learning Basics

Linear Regression
Bias Variance Tradeoff
Regularization
Gradient Descent
Evaluation Metrics
Logistic Regression
KNN & Distance Metrics
Decision Trees

More Machine Learning

Ensemble Methods - Part 1
Ensemble Methods - Part 2
Designing Data Products
Artificial Neural Networks
Image Modeling
Capstone Phase
Natural Language Processing
Prompt Engineering
RAG Systems
Ai Agents
Distributions
Hypothesis Testing And A/B Testing
Time Series Analysis
Unsupervised Learning - Dimensionality Reduction
Clustering
Recommender Systems, an Introduction

Rapid Assessment Test

Git and Github
Intro to Machine Learning
Python Part 1
Python Part 2
pandas & NumPy
Exploratory Data Analysis
Linear Regression
Bias Variance Trade-Off
Gradient Descent
Evaluation Metrics
Logistic Regression
K-nearest Neighbors
Decision Trees
Ensemble Methods
Convolutional Neural Network
Clustering

Job Interview Questions

Bias-Variance Tradeoff & Regularization
Supervised vs Unsupervised ML & Data Cleaning
Data Roles & Gradient Descent
Favorite ML model
Data Science Lifecycle & Missing data
Train-Test split & Scaling
Dimensionality reduction & PCA
SQL & Relational Databases
Evaluation-Metrics & ROC-Curve
Random Forest & Black-box vs White-box
Deep Learning & Backpropagation
Cross-Validation & Hyperparameter tuning
Time-Series Analysis
Unbalanced data & Outlier
Categorical Features & Data Leakage
Normal Distribution & Skewness
Success Metrics & Confusion Matrix
Correlation

Success Metrics & Confusion Matrix

Success Metrics & Confusion Matrix#

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Normal Distribution & Skewness

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Correlation

By The DS Coach Team

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