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Microsoft Certified: Azure Data Scientist Associate
Microsoft Certified: Azure Data Scientist Associate
Microsoft Certified: Azure Data Scientist Associate
Manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Python, Azure Machine Learning and MLflow.
AWS Certified Machine Learning - Specialty
Microsoft Certified: Azure Data Scientist Associate
Microsoft Certified: Azure Data Scientist Associate
validates your expertise in building and deploying machine learning solutions in the AWS Cloud. This credential demonstrates to employers that you can architect ML/deep learning workloads, optimize model training, and implement production-ready ML systems following AWS best practices.
Professional Machine Learning Engineer
Microsoft Certified: Azure Data Scientist Associate
Professional Machine Learning Engineer
A Professional Machine Learning Engineer builds, evaluates, productionizes, and optimizes AI solutions by using Google Cloud capabilities and knowledge of conventional ML approaches. The ML Engineer handles large, complex datasets and creates repeatable, reusable code.
Introduction to Data Science
- What is Data Science?
- Data Science vs. Data Analytics vs. AI vs. Machine Learning
- Data Science Lifecycle
- Real-world Applications
- Roles in the Data Science Ecosystem
Statistics & Probability for Data Science
- Descriptive Statistics (mean, median, mode, range, std dev)
- Probability Concepts and Distributions (Normal, Binomial, Poisson)
- Inferential Statistics
- Hypothesis Testing (Z, T, Chi-Square)
- Confidence Intervals
- Hypothesis Testing (Z, T, Chi-Square)
- Bayesian Thinking
Data Wrangling and Exploration
- Data Collection and Cleaning
- Handling Missing Data and Outliers
- Encoding Categorical Variables
- Feature Engineering & Scaling
- Exploratory Data Analysis (EDA) with Python (Pandas, Seaborn, Matplotlib)
Programming for Data Science
- Python Basics
- Data Structures
- Functional Programming
- NumPy and Pandas
- Working with APIs
- SQL for Data Analysis
Math for Data Science
Linear Algebra
- Vectors, Matrices, Matrix Operations
- Eigenvalues and Eigenvectors
Calculus
- Derivatives and Gradients
- Chain Rule
- Optimization (Gradient Descent)
Machine Learning
Supervised Learning
- Linear & Logistic Regression
- Decision Trees and Random Forests
- Support Vector Machines (SVM)
- Naive Bayes
- k-NN
Unsupervised Learning
- K-Means, DBSCAN
- Hierarchical Clustering
- PCA, t-SNE (Dimensionality Reduction)
Model Evaluation
- Train-Test Split, Cross-Validation
- Accuracy, Precision, Recall, F1-Score, AUC-ROC
- Overfitting/Underfitting
Time Series Analysis
- Components of Time Series
- Autocorrelation and Lag
- Moving Averages
- ARIMA/SARIMA Models
- Forecasting Techniques
Natural Language Processing (NLP)
- Text Cleaning (Tokenization, Lemmatization, etc.)
- Bag-of-Words, TF-IDF
- Word Embeddings (Word2Vec, GloVe, FastText)
- Named Entity Recognition (NER)
- Topic Modeling (LDA)
- Sentiment Analysis
Deep Learning
- Basics of Neural Networks
- Activation Functions
- Forward and Backpropagation
- Loss Functions & Optimizers
- Frameworks: TensorFlow, PyTorch, Keras
Recurrent Neural Networks (RNNs) & Sequence Modeling
- What are RNNs? Architecture and Challenges (vanishing gradient)
- LSTM (Long Short-Term Memory)
- GRU (Gated Recurrent Units)
- Applications: Text Generation, Sentiment Classification, Time Series Prediction
- Sequence-to-Sequence (Seq2Seq) Models
- Attention Mechanism
Advanced AI Concepts
- What is Artificial Intelligence?
- Symbolic AI vs. Machine Learning
- Knowledge Graphs
- Reinforcement Learning (RL)
- Markov Decision Processes
- Q-Learning
- Deep Q-Networks (DQN)
- Computer Vision Basics (CNNs, Image Classification, Object Detection)
Transformers and Large Language Models (LLMs)
- Limitations of RNNs and LSTMs
- Attention Mechanism
- Transformer Architecture (Encoder-Decoder, Self-Attention)
- BERT, GPT, T5, BLOOM, LLaMA
- Prompt Engineering Basics
- Fine-tuning and Transfer Learning
- Using APIs: OpenAI, HuggingFace Transformers
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