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Machine Learning, Data Science and Deep Learning with Python

Machine Learning, Data Science and Deep Learning with Python

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₹12,160  ₹450  97% OFF

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Course Highlights

  • Compatible on Mobile and TV

  • Earn a Cerificate on successful completion

  • Get Full Lifetime Access

  • Course Instructor:Sundog Education by Frank Kane

Mode of learning: Online self study

Course Overview

Who should do this course ?
  • Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course.

  • Technologists curious about how deep learning really works

  • Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you'll need some prior experience in coding or scripting to be successful.

  • If you have no prior coding or scripting experience, you should NOT take this course - yet. Go take an introductory Python course first.

What are the course deliverables ?
  • Build artificial neural networks with Tensorflow and Keras

  • Classify images, data, and sentiments using deep learning

  • Make predictions using linear regression, polynomial regression, and multivariate regression

  • Data Visualization with MatPlotLib and Seaborn

  • Implement machine learning at massive scale with Apache Spark's MLLib

  • Understand reinforcement learning - and how to build a Pac-Man bot

  • Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA

  • Use train/test and K-Fold cross validation to choose and tune your models

  • Build a movie recommender system using item-based and user-based collaborative filtering

  • Clean your input data to remove outliers

  • Design and evaluate A/B tests using T-Tests and P-Values

More about this course
  • New! Updated for Winter 2019 with extra content on feature engineering, regularization techniques, and tuning neural networks - as well as Tensorflow 2.0 support! Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too! If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 14 hours of video , and most topics include hands-on Python code examples you can use for reference and for practice. I?? ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn??. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It?? s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned! The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including: Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras Data Visualization in Python with MatPlotLib and Seaborn Transfer Learning Sentiment analysis Image recognition and classification Regression analysis K-Means Clustering Principal Component Analysis Train/Test and cross validation Bayesian Methods Decision Trees and Random Forests Multiple Regression Multi-Level Models Support Vector Machines Reinforcement Learning Collaborative Filtering K-Nearest Neighbor Bias/Variance Tradeoff Ensemble Learning Term Frequency / Inverse Document Frequency Experimental Design and A/B Tests Feature Engineering Hyperparameter Tuning ... And much more! There's also an entire section on machine learning with Apache Spark , which lets you scale up these techniques to "big data" analyzed on a computing cluster. And you'll also get access to this course's Facebook Group , where you can stay in touch with your classmates. If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's, Linux desktops, and Macs. If you?? re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry ?? this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now! "I started doing your course in 2015... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD

Curriculum (129 Chapters)

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    On completing the course, you will get a course completion certificate from Udemy.
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