# Python Machine Learning Bootcamp Canonical URL: ## Overview Machine learning goes beyond ordinary coding, which requires step-by-step instructions, by using algorithms that learn patterns and make decisions independently. The skill set is in high demand, since these algorithms now run a large share of trading and the recommendation systems behind companies like Netflix, Spotify, and Amazon. The course begins with linear and logistic regression, the most time-tested tools for approaching a machine learning problem, then moves to algorithms with a different basis such as k-nearest neighbors, decision trees, and random forests. You'll work in Python with NumPy, Pandas, and scikit-learn. Mathematical foundations are explained visually, with no formal math component. ## What you'll learn - How to clean and balance your data using the Pandas library - Applying machine learning algorithms such as logistic regression and random forest using the scikit-learn library - Choosing good features to use as input for your algorithms - Properly splitting data into training, test and cross-validation sets - Important theoretical concepts like overfitting, variance and bias - Evaluating the performance of your machine learning models ## Prerequisites This course requires students to be comfortable with Python and its data science libraries (NumPy and Pandas). If a student has not worked in Python before, we require a student to enroll in our [Python for Data Science Bootcamp](/courses/python-data-science-nyc)before taking this course. ## Curriculum #### 1. Course Kick-off & Python Refresher - Data Science tool recap - Pandas and indexing - Exploratory data analysis (EDA): standard deviations and uniform vs. normal distributions using NumPy/Pandas - Hands-on: loading CSVs, basic plotting with Matplotlib #### 2. Data Visualization & Simple Linear Regression - Crafting clear scatterplots: labels, grids, styling - Single-variable linear regression (attendance → concessions) - Train-test splitting and dealing with outliers - Evaluating models with R²; interpreting residuals - Extended example: car-sales dataset, predicting price from one feature #### 3. Binary Classification & Logistic Regression - From regression to classification: why logistic vs. linear - Implementing logistic regression on an employee “stay/leave” dataset - Classification metrics deep dive: accuracy, precision, recall, F1 score, ROC curve - Understanding variability: train-test ratios, data shuffling, sample size effects - Confusion matrix analysis #### 4. k-Nearest Neighbors & the Iris Dataset - Introduction to k-NN: distance metrics, choosing k - Dataset exploration: sepal/petal measurements, plotting clusters - Preprocessing: label encoding categorical data, feature scaling - Model training, hyperparameter tuning, evaluating with confusion matrix and classification report - Brief intro to decision-tree logic (setting up for ensembles) #### 5. Ensemble Methods & Neural Networks - Random forest classifiers on the Titanic dataset: feature engineering, importance scores - Kaggle workflow: generating predictions, submitting to competition - Neural network primer: perceptron to multilayer architectures - Hands-on MNIST digit classification with Keras/TensorFlow in Colab ## Schedule - Nov 9, 2026 – Nov 13, 2026 — NYC - Dec 7, 2026 – Dec 11, 2026 — NYC - Dec 29, 2026 – Feb 2, 2027 — NYC - Jan 17, 2027 – Feb 21, 2027 — NYC - Feb 1, 2027 – Feb 5, 2027 — NYC - Mar 29, 2027 – Apr 2, 2027 — NYC - Apr 20, 2027 – May 27, 2027 — NYC - May 4, 2027 – May 10, 2027 — NYC - May 9, 2027 – Jun 13, 2027 — NYC - Jun 1, 2027 – Jun 7, 2027 — NYC - Aug 2, 2027 – Aug 6, 2027 — NYC - Aug 17, 2027 – Sep 16, 2027 — NYC - Sep 12, 2027 – Oct 17, 2027 — NYC - Sep 20, 2027 – Sep 24, 2027 — NYC ## FAQ ### Is this course open to beginners? This course requires students to be comfortable with Python and its data science libraries (NumPy and Pandas). If a student has not worked in Python before, we require a student to enroll in our Python for Data Science Bootcamp before taking this course. ### Do you offer payment plans or student financing for this course? This course does not qualify for payment plans or student financing. See our Payment Plan FAQ to find related programs that qualify. ### Can I take this course online (remote learning)? No upcoming live online dates are currently available. Please contact us about future availability. ### What happens if I need to miss a session? You have some options: You can watch a recording of the session. We will provide any course materials. Contact us to confirm whether remote attendance is available for your session. You can use your free retake to make up any missed class time. ### What’s included with my tuition? A hands-on learning experience working on projects and exercises, which is proven to boost comprehension, retention, and engagement Expert instructors who are industry professionals and experienced educators that are driven to help you succeed Access to industry-standard software during your training Additional personalized 1-on-1 bonus training with an industry expert to reinforce concepts Top-notch curricula that have been tried and tested over many cohorts and are consistently improved for an optimal learning experience A 1-year free retake policy which allows students to retake any part of the course or program for free to refresh the materials and gain additional practice 30-day access to class recordings to review material at your own pace A certificate of completion to verify your accomplishment ### Are there any additional fees or expenses? There are no extra fees or taxes for our courses. The price you see on this page is the maximum you’ll pay us. ### Do you offer any discounts for this course? Take this course as part of a certificate program and save: Data Science & AI Certificate Price: $3,995 | Hours: 114 Hours FinTech Bootcamp Price: $4,995 | Hours: 114 Hours Python Data Science & Machine Learning Bootcamp Price: $3,495 | Hours: 96 Hours Data Analytics Certificate Price: $4,995 | Hours: 156 Hours Data Analytics & AI Certificate Price: $6,995 | Hours: 210 Hours ## Pricing **Tuition:** $1895