Professional Certificate in Python for Machine Learning and Data Science

Week 1: Introduction and Python Basics

Class 1: Introduction to Data Science and Machine Learning

Overview of Data Science:

• Definition: The field that utilizes scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
• Importance: Role in driving decisions across industries (e.g., finance, healthcare, marketing).

Key Technologies:

• Tools and frameworks: Python, R, SQL, Hadoop, Spark, TensorFlow, Scikit-learn, Keras.
• Data storage technologies: SQL databases, NoSQL databases, data lakes.

Career Opportunities:

• Roles: Data Scientist, Data Analyst, Data Engineer, Machine Learning Engineer.
• Skills: Statistical analysis, programming, data wrangling, data visualization, machine learning.

Class 2: Python for Data Science

Python Syntax:

• Data types: Integers, floats, strings, booleans.
• Control structures: Conditional statements (if-else), loops (for, while), functions and scope.

Development Environment:

• Setting up Anaconda/Miniconda: Managing Python packages and environments.
• Using Jupyter Notebook: Creating and running interactive notebooks for data analysis.

Hands-On Exercise:

• Writing Python scripts to perform basic calculations and data manipulations.

Week 2: Data Manipulation and Analysis

Class 3: Data Structures in Python

Understanding Data Structures:

• Lists: Creating, indexing, slicing, and modifying lists.
• Tuples: Immutable sequences, advantages, and use cases.
• Dictionaries: Key-value pairs, accessing and modifying data, use cases in data manipulation.
• Sets: Unique collections, operations (union, intersection), and their applications.

Practical Exercise:

• Implementing data structure manipulations to solve common problems.

Class 4: Introduction to NumPy and Pandas

NumPy Basics:

• Creating and manipulating arrays: Understanding one-dimensional, two-dimensional, and multi-dimensional arrays.
• Array operations: Mathematical operations, broadcasting, and reshaping arrays.
• Data types and type casting in NumPy.

Pandas Introduction:

• DataFrames vs. Series: Structures, indexing, and advantages.
• Loading data: Importing datasets from CSV, Excel, and SQL databases.
• Data Manipulation Techniques: Filtering, sorting, merging, and aggregating data. Handling missing values using techniques like imputation.

Hands-On Exercise:

• Practical data analysis tasks using real-world datasets.

Week 3: Data Visualization

Class 5: Data Visualization with Matplotlib and Seaborn

Basic Plotting Techniques:

• Creating various plot types: Line plots, scatter plots, bar charts, and histograms using Matplotlib.
• Customizing plots: Titles, axis labels, legends, and gridlines.

Advanced Visualizations:

• Creating advanced plots: Box plots, violin plots, heatmaps with Seaborn.
• Exploring different color palettes and styles for effective data representation.

Hands-On Project:

• Visualizing a dataset of choice to uncover insights and patterns.

Class 6: Interactive Visualizations with Plotly

Creating Interactive Plots:

• Introduction to Plotly’s interactive capabilities: Scatter plots, bar charts, and surface plots.
• Building dynamic visualizations and dashboards that allow user interaction.

Integrating Plotly in Python Notebooks:

• Creating and sharing Plotly graphs in Jupyter.

Case Study:

• Application of interactive visualizations in a business intelligence context.

Week 4: Statistical Foundations

Class 7: Descriptive Statistics and Probability

Measures of Central Tendency:

• Detailed exploration of mean, median, mode, and their significance in data analysis.
• Practical exercises: Calculating these measures using real datasets in Pandas.

Measures of Variability:

• Understanding range, variance, standard deviation, and interquartile range (IQR).
• Applications of variability measures: Identifying outliers and understanding data spread.

Basic Probability Concepts:

• Definitions of events, outcomes, sample space, and probability measures.
• Introduction to probability rules: Addition rule, multiplication rule, and conditional probability.

Class 8: Inferential Statistics

Hypothesis Testing:

• Formulating null and alternative hypotheses, understanding type I and type II errors.
• Applying z-tests and t-tests in Python for different scenarios.

Confidence Intervals:

• Understanding the concept of estimation and confidence intervals.
• Calculation and interpretation of confidence intervals for means and proportions.

Hands-On Exercise:

• Conducting hypothesis tests and calculating confidence intervals using real datasets.

Week 5: Machine Learning Basics

Class 9: Supervised vs. Unsupervised Learning

Supervised Learning:

• Classification vs. regression: Understanding the differences with examples.
• Key algorithms: Linear regression, logistic regression, decision trees, support vector machines (SVM).

Unsupervised Learning:

• Overview of clustering techniques: K-means, hierarchical clustering, DBSCAN.
• Dimensionality reduction techniques: Principal Component Analysis (PCA), t-SNE.

Case Study:

• Comparing outcomes of supervised and unsupervised learning on a dataset.

Class 10: Machine Learning Workflow

Data Preprocessing:

• Importance of data cleaning: Handling missing values, encoding categorical variables.
• Feature engineering techniques: Creating new features, feature selection methods.

Model Selection and Evaluation:

• Overview of model evaluation metrics: Accuracy, precision, recall, F1-score, ROC-AUC, confusion matrix.
• Understanding train-test splits and cross-validation techniques: K-fold cross-validation, stratified sampling.

Weeks 6-7: Advanced Machine Learning

Class 11-14: Algorithms Deep Dive

Linear Regression:

• Understanding the algorithm: Simple and multiple linear regression.
• Evaluating model performance: Mean Squared Error (MSE), R-squared.
• Implementation using Scikit-learn with practical examples.

Logistic Regression:

• Application in binary classification problems, understanding the logistic function.
• Evaluating model performance and interpreting coefficients using confusion matrices.

Decision Trees and Random Forests:

• Decision tree algorithm: Splitting criteria (Gini impurity, information gain), overfitting, and pruning techniques.
• Random forests: Understanding ensemble methods, feature importance, and model robustness.

Support Vector Machines (SVM) and K-Nearest Neighbors (KNN):

• Understanding SVM for classification: Margin optimization, kernel tricks.
• Implementing KNN: Understanding distance metrics, model evaluation.

Week 8: Deep Learning

Class 15-16: Introduction to Neural Networks

Neural Network Architecture:

• Components: Neurons, layers (input, hidden, output), activation functions (ReLU, sigmoid, softmax).
• Backpropagation: Understanding how neural networks learn and adjust weights.

Hands-On with TensorFlow and Keras:

• Building and training simple neural networks for classification tasks using Keras.
• Understanding loss functions (binary crossentropy, categorical crossentropy) and optimizers (SGD, Adam).

Project:

• Creating a neural network model to classify images or text data.

Weeks 9-10: Special Topics

Class 17-18: Natural Language Processing (NLP)

Text Preprocessing:

• Techniques: Tokenization, stop-word removal, stemming, lemmatization.
• Vectorization: Bag of Words, TF-IDF, word embeddings (Word2Vec, GloVe).

Sentiment Analysis and Text Classification:

• Building models for sentiment analysis using libraries like NLTK, SpaCy, and Hugging Face Transformers.
• Case studies showcasing NLP applications in sentiment analysis and chatbots.

Class 19-20: Time Series Analysis

Time Series Data Structures:

• Understanding time series components: trend, seasonality, cycles.
• Time series data visualization techniques: Line plots, seasonal decomposition.

Forecasting Models:

• Introduction to ARIMA models: Understanding autoregressive, integrated, and moving average components.
• Practical forecasting: Building models using Python libraries like statsmodels.
• Evaluating forecasting performance using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).

Week 11: Reinforcement Learning and Model Deployment

Class 21: Basics of Reinforcement Learning

Understanding Environment-Agent Interaction:

• Key concepts: States, actions, rewards, policies, and value functions.
• Overview of Markov Decision Processes (MDP).

Q-Learning Basics:

• Understanding the Q-learning algorithm: Exploration vs. exploitation.
• Implementing a simple reinforcement learning environment in Python.

Class 22: Deploying Machine Learning Models

Introduction to Model Deployment:

• Importance and challenges of deploying machine learning models in production.
• Overview of deployment options: On-premises, cloud-based, and edge deployment.

Using Flask to Create API Endpoints:

• Step-by-step guide to deploying a machine learning model as a RESTful API.
• Hands-on exercise: Creating an API for a machine learning model and testing it.

Week 12: Capstone Project and Career Support

Class 23-25: Capstone Project

Project Application:

• Students choose a real-world data problem to apply their skills.
• Guidance on project scope, methodologies, and data sources.

Presentations:

• Students present their projects to peers and instructors for feedback.
• Focus on presenting results, methodologies, and learning outcomes clearly.

Career Guidance:

• Workshops on resume building, interview preparation, and networking strategies.
• Discussion on industry trends, certifications, and continuing education opportunities.