Machine Learning Intensive

Your Path to AI Career: Python for Machine Learning

4 sessions
6 hours each
24 Hrs total
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Upcoming Enrollment

April 24th - April 30th

4 sessions 6 hours each

Online, US Eastern Time

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Become a Machine Learning Engineer! Learn how to build precise predictive models using essential machine learning algorithms.

Join our comprehensive course where you'll master both Supervised and Unsupervised Machine Learning. Discover the secrets to crafting precise predictive models that propel your projects to new heights. With Python as your canvas and scikit-learn, TensorFlow, and Keras as your brushes, you'll create intelligent systems that understand patterns, make predictions, and elevate your skills to the next level. Throughout the course, you will learn how to describe data, interpret data, and build predictive models.

In this Python for Machine Learning course Supervised Machine Learning segment, you'll explore the heart of predictive modeling. Discover the art of Linear and Polynomial Regression, navigate the realm of regularization with Lasso and Ridge techniques, harness the power of K Nearest Neighbors, and master the magic of eXtreme Gradient Boosting and Adaptive Boosting. You'll also demystify Decision Trees, uncover the mysteries of Random Forests, and grasp the fundamentals of Naive Bayes Classifiers. Along this exhilarating journey, you'll master the craft of converting categorical features into numerical values, and you'll unlock an array of preprocessing techniques to standardize and normalize dataset features.

The Unsupervised Machine Learning component will illuminate the potential within your data. Journey through K-means and Hierarchical Clustering to unveil underlying patterns, explore the transformative potential of Principal Component Analysis (PCA), and delve into the realm of Autoencoders as you uncover intricate structures and unleash the true essence of your data.

From data manipulation and visualization using Python packages like Pandas, Numpy, and Matplotlib to algorithmic mastery with the most efficient machine learning libraries such as scikit-learn, TensorFlow, and Keras, our course empowers you with Python's prowess, enabling you to wield the language of data with unwavering confidence.

In our course, which focuses on Python for Probability, Statistics, and Machine Learning, we go more in-depth into core statistical concepts than many standard Python for Machine Learning offerings. We emphasize the crucial role of statistics in machine learning, ensuring participants understand key concepts such as the Normal distribution, Variance, and the significance of outliers on the Bell Curve.

Enroll now and harness the true potential of Machine Learning. Shape the future, elevate your skills, and become the architect of transformative intelligence. Your journey awaits!

What to expect

This is an online live instructor-led course, Machine Learning Intensive, which spans eight intensive night sessions or four immersive day sessions, totaling 24 hours of immersive learning. Experience real-time interaction as the instructor addresses your questions and, if necessary, offers code review and error correction. Benefit from comprehensive course notes and training videos to reinforce your understanding of the material. Access the original recordings of the live online sessions for your convenience. Additionally, enhance your skills with carefully curated exercises for extra practice. Even after the course, our dedicated instructor offers continued support via Slack, readily assisting with any questions or clarifications you may need.

Prerequisites

The prerequisites for this course include completion of the Python Data Science Intensive course or a strong proficiency in the NumPy and Pandas libraries. You should also have a clear understanding of the distinctions between one-dimensional and two-dimensional objects, as well as a grasp of the behavior of Python's built-in data types like tuples and dictionaries. Additionally, a working knowledge of Matplotlib is expected.

Syllabus

  1. 1

    Session One

    To kickstart this course, we'll begin with a swift recap of NumPy, the fundamental Python library. This step is essential as scikit-learn builds upon NumPy's foundation. As we progress, a solid grasp of one-dimensional and two-dimensional data structures will provide a strong framework for navigating the course. Then, we will delve into both single-variable and multi-variable linear regression.

  2. 2

    Session Two

    With our solid foundation in linear regression models, we'll embark on an exploration of more advanced linear modeling techniques, including polynomial regression and logistic regression. Throughout this journey, we'll uncover the art of data splitting for training and testing purposes, as well as the transformative process of converting categorical values into numeric representations.

  3. 3

    Session Three

    Building upon a solid understanding of linear models, our focus will shift to the invaluable techniques of Lasso Regularization and Ridge Regularization in the realm of machine learning, particularly within linear regression models. These techniques serve as powerful mechanisms tailored to tackle unique challenges and elevate model performance. Lasso Regularization, also recognized as L1 Regularization, assumes a prominent role in prioritizing feature selection.

  4. 4

    Session Four

    Next, our focus shifts to the K Nearest Neighbors (KNN) algorithm. Additionally, Decision Trees and Random Forests emerge as ensemble techniques, seamlessly amalgamating the collective wisdom of multiple Decision Trees to yield dependable and robust outcomes. This ensemble trio of algorithms forms a set of indispensable tools, adeptly catering to a spectrum of complexities and scenarios within the expansive realm of machine learning.

  5. 5

    Session Five

    Embracing the MinMaxScaler technique, we'll elevate our models to new heights and delve into the art of harnessing eXtreme Gradient Boosting for the creation of remarkably precise models. Subsequently, we'll embark on a journey through Naive Bayes Classifiers, expanding our repertoire of sophisticated techniques.

  6. 6

    Session Six

    Upon achieving proficiency in supervised learning, we will venture into the realm of unsupervised learning, where we will delve into the mastery of K-means clustering and Hierarchical Clustering techniques. These approaches serve as powerful tools for data grouping and segmentation, each with its distinct methodology.

  7. 7

    Session Seven

    Our journey will progress to exploring PCA and Autoencoders, powerful techniques employed to diminish data dimensionality, extract pivotal features, and capture vital insights from complex, high-dimensional datasets. While PCA orchestrates a linear transformation strategy grounded in orthogonal axes, Autoencoders embark on a distinct path as neural network architectures, adeptly crafting hierarchical data representations.

  8. 8

    Session Eight

    Upon mastering the core machine learning algorithms, we will delve into an illuminating project that equips us with the art of algorithm selection. Through this endeavor, we will navigate a comprehensive real-time project, immersing ourselves in its entirety. Drawing insights from this project, we will discern which ML algorithms exhibit superior performance.

Schedule & Enrollment

Instructors

Art Yudin

Testimonials

I loved Art! He’s super patient and answers all questions! I recommend this class for all python beginners!! I had no knowledge of python prior to this class

Audrey T.

Always enjoy taking a class with Art. He is knowledgeable and on top of his game. His class is quick paced but easy to follow... is patient with questions

Ping Feng

I took Introduction to Python and Web Scrapping with Art and the class was great!

Bryndee Carlson

Took Python for Data Science Immersive with Art. Great instructor! Boost my knowledge within very short time frame!

Rahim Yakubjanov

Art is very thorough, helpful and able to break down content in a way it is easily digestible by those in attendance.

Maximilian K.

Art is great. Very generous with time and knowledge and truly helpful. Great if you have no programming knowledge or if you're a more advanced student.

Adriana Rodriguez

Art is super helpful and attentive to every question.

Hasan Hachem

Art teaches Python in a very understandable way.

Ray Shah

Art has a ton of experience in teaching the basics of python to people who have no previous coding experience. The class format is great - we start with lectures and go right into practice problems to use what we just learned. 100% would recommended!

Helen Li

Art does a great job meeting everyone at their level and making sure that everyone feels challenged.

Tibisay Salrno

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