Machine Learning with Python: Linear Models to Deep Learning
Why data science using Python?
Python language is multifaceted and flexible and has easy readability, also it is an obvious language of choice in the field like Data Science and Data Analytics, and thus is widely termed along with data science as “Data Science using Python” or with the subsets of Data Science like “Machine learning with Python”.
The Python libraries such as Pandas help individuals clean up data and perform advanced manipulation. The growth of Python in data science has gone hand in hand with that of Pandas, which opened the use of Python for data analysis to a broader audience by enabling it to deal with row-and-column datasets, import CSV files, and much more.
About Machine Learning
While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine on how to learn. Machine learning is a data analytics technique that teaches computers to do what comes naturally to humans and animals. Machine learning algorithms use computational methods to learn information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases. Deep learning is a specialized form of machine learning.
What is big data analytics?
It is the process of examining large data sets containing a variety of data types – i.e., Big Data to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information. Companies and enterprises that implement Big Data Analytics often reap several business benefits, including more effective marketing campaigns, the discovery of new revenue opportunities, improved customer service delivery, more efficient operations, and competitive advantages.
Companies implement Big Data Analytics because they want to make more informed business decisions. Also, it gives analytics professionals, such as data scientists and predictive modelers, the ability to analyze Big Data from multiple and varied sources, including transactional data and other structured data.
“Information is the oil of the 21st century, and analytics is the combustion engine”
–(Peter Sondergaard, Senior Vice President, Gartner)
About this course
Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk. As a discipline, machine learning tries to design and understand computer programs that learn from the experience for the purpose of prediction or control.
In this machine learning with python course from Linear Models to Deep Learning, you will learn about principles and algorithms for turning training data into effective automated predictions. This course will cover the following details such as:
- Representation, over-fitting, regularization, generalization, VC dimension;
- Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning;
- On-line algorithms, support vector machines, and neural networks/deep learning.
Machine learning with Python course is part of the MITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT Ph.D. or a Master’s at other universities. (For more visit: https://micromasters.mit.edu/ds/).
What you will learn from the Machine learning with Python course?
- You will be able to understand the principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning.
- Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models.
- Choose suitable models for different applications.
- Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering.
About the instructors:
Delta Electronics Professor in the Department of Electrical Engineering and Computer Science at MIT.
Regina Barzilay is a Delta Electronics Professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. Her research interests are in natural language processing, applications of deep learning to chemistry and oncology.
Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society at MIT.
Tommi S. Jaakkola received M.Sc. in theoretical physics from Helsinki University of Technology and Ph.D. from MIT in computational neuroscience. He joined MIT faculty 1998 and he is now the Thomas Siebel Professor in EECS and IDSS at MIT.
Lecturer and Research Scientist at Massachusetts Institute of Technology.
Karene Chu received her Ph.D. in mathematics from the University of Toronto in 2012. Since then she has been a postdoctoral fellow first at the University of Toronto/Fields Institute, and then at MIT, with research focus on knot theory.
Syllabus on Machine learning with Python course
- Introduction to Linear classifiers, separability, perceptron algorithms.
- Maximum margin hyperplane, loss, regularization, and Stochastic gradient descent, over-fitting, generalization.
- Linear regression and Recommender problems, collaborative filtering.
- Non-linear classification, kernels with Learning features, Neural networks.
- Deep learning, back propagation.
- Recurrent neural networks.
- Generalization, complexity, VC-dimension.
- Unsupervised learning: clustering.
- Generative models, mixtures, Mixtures, and the EM algorithm.
- Learning to control: Reinforcement learning.
- Reinforcement learning continued and Applications: Natural Language Processing.
- One should have proficiency in Python programming along with equivalent probability theory.
- Able to calculate College-level single and multi-variable calculus and vectors & matrices.
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- Massachusetts Institute of Technology
- Online Course
- 3+ Months
- Free Course (Affordable Certificate)
- Intermediate Calculus Intermediate Probability Intermediate Vectors and Matrices Proficiency in Python
- Data Analysis Data Science Data Science with 'Python' Deep learning Machine learning Natural language processing Reinforcement learning