# Python Machine Learning Immersive Canonical URL: ## Overview This skillset is in high demand, as machine learning algorithms now run the majority of trading on Wall Street and the product recommendations at big companies like Amazon, Spotify, and Netflix. This course will begin with linear and logistic regression, the most time-tested and reliable tools for approaching a machine learning problem. The course will then progress to algorithms with a very different theoretical basis, such as k-nearest neighbors, decision trees, and random forest. This will bring important statistical concepts to the forefront, such as bias, variance, and overfitting. You’ll also learn how to measure the accuracy of your models, as well as tips for choosing effective features and algorithms. The course will be focused on the practical skills needed to solve real-world problems with machine learning. The mathematical foundations for each machine learning algorithm will be explained visually, but there will not be a formal math component. Entering students are expected to be comfortable with writing Python programs, as well as the NumPy and pandas libraries. ## 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 Immersive](/classes/python-for-data-science-immersive-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 ### Who is the target audience for this course? Confident Python developers who would like to explore machine learning hands-on Developers with strong skills in another language, and some background working with data looking to build machine learning models ### How is this class structured? This class is a 30-hour class that starts by teaching forms of regression analysis and moves on to more industry-used algorithms such as k-nearest neighbors, decision trees, and random forest. Additionally, students will learn how to determine the accuracy of a predictive model. ### How many students are in a given class? Our typical class ranges from 8-12 students, but we allow up to 20 students to register for our course. ### How does this class prepare me for the job market? The classes will allow students to learn advanced topics in data science used by the most cutting edge companies such as Google, Meta, and more. These topics will allow students to build, evaluate, and reassess forecasting models on all forms of data. ### Is there mandatory work outside of the classroom? Students are not required to complete any work outside of class. However, we provide students with bonus materials if they would like extra practice. ### What tangible skills do students leave with after the class? Students will leave with the ability to learn how to build a model from start to finish. Students will learn how to clean and balance data, apply a form of learning algorithm on the data, perform a bias test, and finally evaluate the accuracy of their model. ### 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 Immersive 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 Certificate Price: $3,995 | Hours: 114 Hours ## Pricing **Tuition:** $1895