Machine Learning with Python from Scratch

Course From:

Udemy

Machine Learning is a hot topic! Python Developers who understand how to work with Machine Learning are in high demand.

But how do you get started?

Maybe you tried to get started with Machine Learning, but couldn’t find decent tutorials online to bring you up to speed, fast.

Maybe the information you found was too basic, and didn’t give you the real-world Machine learning skills using Python that you needed.

Or maybe the information got bogged down in complex math explanations and was too difficult to relate to.

Whatever the reason, you are in the right place if you want to progress your skills in Machine Language using Python.

This course will help you to understand the main machine learning algorithms using Python, and how to apply them in your own projects.

But what exactly is Machine Learning?

It’s a field of computer science that gives computers the ability to “learn” – e.g. continually improve performance on a specific task, with data, without being explicitly programmed.

Why is it important?

Machine learning is often used to solve tasks considered too complex for humans to solve. We create algorithms and apply a bunch of data to that algorithm and let the computer process (execute) the algorithm and search for a model (solution).

Because of the practical applications of machine learning, such as self driving cars (one example) there is huge interest from companies and government in Machine learning, and as a result, there are a a lot of opportunities for Python developers who are skilled in this field.

If you want to increase your career options, then understanding and being able to work with Machine Learning with your own Python programs should be high on your list of priorities.

What will you learn in this course?

For starters, you will learn about the main scientific libraries in Python for data analysis such as Numpy, Pandas, Matplotlib and Seaborn. You’ll then learn about artificial neural networks and how to work with machine learning models using them.

You obtain a solid background in machine learning and be able to apply that knowledge directly in your own programs.

What are the Main topics included in the course?

Data Analysis with Numpy, Pandas, Matplotlib and Seaborn.

The machine learning schema.

Overfitting and Underfitting

K Fold Cross Validation

Classification metrics

Regularization: Lasso, Ridge and ElasticNet

Logistic Regression

Support Vector Machines for Regression and Classification

Naive Bayes Classifier

Decision Trees and Random Forest

KNN classifier

Hyperparameter Optimization: GridSearchCV

Principal Component Analysis (PCA)

Linear Discriminant Analysis (LDA)

Kernel Principal Component Analysis (KPCA)

Ensemble methods: Bagging

AdaBoost

K means clustering analysis

Regression model and evaluation

Linear and Polynomial Regression

SVM, KNN, and Random Forest for Regression

RANSAC Regression

Neural Networks: Constructing our own MLP.

Perceptron and Multilayer Perceptron

And don’t worry if you do not understand some, or all of these terms. By the end of the course you will know what they are and how to use them.

Why enrolling in this course is the best decision you can make.

This course helps you to understand the difficult concepts of Machine learning in a unique way. Rather than just focusing on complex maths explanaitons, simpler explanations with charts, and info displays are included.

Many examples and genuinely useful code snippets are also included to make it even easier to learn and understand.

After completing this course, you will have the necessary skills to apply Machine learning in your own projects.

The sooner you sign up for this course, the sooner you will have the skills and knowledge you need to increase your job or consulting opportunities. Your new job or consulting opportunity awaits!

Why not get started today?

Click the Signup button to sign up for the course!

Who this course is for:

Students who wish to take their basic Python skills to the next level by mastering Pythons various scientific libraries
Students who want to understand and apply Machine Learning into their own programs
Students wanting to empower themselves with machine learning.

Course

Intermediate

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Data Science Accelerator

General Assembly’s Data Science part-time course is a practical introduction to the interdisciplinary field of data science and machine learning, which lies at the intersection of computer science, statistics, and business. You will learn to use the Python programming language to acquire, parse, and model data for informing business strategy.

This is a fast-paced course with some prerequisites. Students should be comfortable with programming fundamentals, core Python syntax, and basic statistics. There is an option to complete up to 25 hours of online preparatory lessons. Talk to the General Assembly Admissions team to discuss your background and confirm if this is the right fit for you..

What you'll accomplish

A significant portion of the course is a hands- on approach to fundamental modeling techniques and machine learning algorithms. You’ll also practice communicating your results and insights by compiling technical documentation and a stakeholder presentation. Throughout this expert-designed program, you’ll:

Perform exploratory data analysis with Python.
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from data sets.
Communicate data-driven insights to technical and non-technical audiences alike.
Apply what you’ve learned to create a portfolio project: a predictive model that addresses a real-world data problem.

Why General Assembly

Since 2011, General Assembly has graduated more than 40,000 students worldwide from the full time & part time courses. During the 2020 hiring shutdown, GA's students, instructors, and career coaches never lost focus, and the KPMG-validated numbers in their Outcomes report reflect it. *For students who graduated in 2020 — the peak of the pandemic — 74.4% of those who participated in GA's full-time Career Services program landed jobs within six months of graduation. General Assembly is proud of their grads + teams' relentless dedication and to see those numbers rising. Download the report here.

Your next step? Submit an application to talk to the General Assembly Admissions team

_{Note: reviews are referenced from Career Karma - https://careerkarma.com/schools/general-assembly}

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Data Science Bootcamp

General Assembly’s Data Science Immersive is a transformative course designed for you to get the necessary skills for a data scientist role in three months.

The Data Science bootcamp is led by instructors who are expert practitioners in their field, supported by career coaches that work with you since day one and enhanced by a career services team that is constantly in talks with employers about their tech hiring needs.

What you'll accomplish

As a graduate, you will be ready to succeed in a variety of data science and advanced analytics roles, creating predictive models that drive decision-making and strategy throughout organizations of all kinds. Throughout this expert-designed program, you’ll:

Collect, extract, query, clean, and aggregate data for analysis.
Gather, store and organize data using SQL and Git.
Perform visual and statistical analysis on data using Python and its associated libraries and tools.
Craft and share compelling narratives through data visualization.
Build and implement appropriate machine learning models and algorithms to evaluate data science problems spanning finance, public policy, and more.
Compile clear stakeholder reports to communicate the nuances of your analyses.
Apply question, modeling, and validation problem-solving processes to data sets from various industries to provide insight into real-world problems and solutions.
Prepare for the world of work, compiling a professional-grade portfolio of solo, group, and client projects.

Why General Assembly

Your next step? Submit an application to talk to the General Assembly Admissions team

_{Note: reviews are referenced from Career Karma - https://careerkarma.com/schools/general-assembly}

Udacity

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Learn cutting-edge natural language processing techniques to process speech and analyze text. Build probabilistic and deep learning models, such as hidden Markov models and recurrent neural networks, to teach the computer to do tasks such as speech recognition, machine translation, and more!

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