Full hands-on machine studying tutorial with knowledge science, Tensorflow, synthetic intelligence, and neural networks.
What you may be taught
- Construct synthetic neural networks with Tensorflow and Keras
- Classify photos, knowledge, and sentiments utilizing deep studying
- Make predictions utilizing linear regression, polynomial regression, and multivariate regression
- Knowledge Visualization with MatPlotLib and Seaborn
- Implement machine studying at large scale with Apache Spark’s MLLib
- Perceive reinforcement studying – and construct a Pac-Man bot
- Classify knowledge utilizing Ok-Means clustering, Assist Vector Machines (SVM), KNN, Determination Timber, Naive Bayes, and PCA
- Use prepare/take a look at and Ok-Fold cross validation to decide on and tune your fashions
- Construct a film recommender system utilizing item-based and user-based collaborative filtering
- Clear your enter knowledge to take away outliers
- Design and consider A/B checks utilizing T-Exams and P-Values
Machine Studying and synthetic intelligence (AI) is in every single place; if you wish to know the way corporations like Google, Amazon, and even Udemy extract that means and insights from large knowledge units, this knowledge science course will provide you with the basics you want. Knowledge Scientists get pleasure from one of many top-paying jobs, with a median wage of $120,000 based on Glassdoor and Certainly. That is simply the typical! And it isn’t nearly cash – it is fascinating work too!
For those who’ve acquired some programming or scripting expertise, this course will train you the strategies utilized by actual knowledge scientists and machine studying practitioners within the tech trade – and put together you for a transfer into this scorching profession path. This complete machine studying tutorial contains over 100 lectures spanning 14 hours of video, and most subjects embody hands-on Python code examples you need to use for reference and for observe. I’ll draw on my 9 years of expertise at Amazon and IMDb to information you thru what issues, and what doesn’t.
Every idea is launched in plain English, avoiding complicated mathematical notation and jargon. It’s then demonstrated utilizing Python code you may experiment with and construct upon, together with notes you may preserve for future reference. You will not discover educational, deeply mathematical protection of those algorithms on this course – the main focus is on sensible understanding and software of them. On the finish, you may be given a closing venture to use what you’ve got realized!
The subjects on this course come from an evaluation of actual necessities in knowledge scientist job listings from the most important tech employers. We’ll cowl the machine studying, AI, and knowledge mining strategies actual employers are in search of, together with:
- Deep Studying / Neural Networks (MLP’s, CNN’s, RNN’s) with TensorFlow and Keras
- Knowledge Visualization in Python with MatPlotLib and Seaborn
- Switch Studying
- Sentiment evaluation
- Picture recognition and classification
- Regression evaluation
- Ok-Means Clustering
- Principal Element Evaluation
- Prepare/Take a look at and cross validation
- Bayesian Strategies
- Determination Timber and Random Forests
- A number of Regression
- Multi-Stage Fashions
- Assist Vector Machines
- Reinforcement Studying
- Collaborative Filtering
- Ok-Nearest Neighbor
- Bias/Variance Tradeoff
- Ensemble Studying
- Time period Frequency / Inverse Doc Frequency
- Experimental Design and A/B Exams
- Function Engineering
- Hyperparameter Tuning
For those who’re new to Python, don’t fret – the course begins with a crash course. For those who’ve finished some programming earlier than, it’s best to decide it up rapidly. This course exhibits you get arrange on Microsoft Home windows-based PC’s, Linux desktops, and Macs.
For those who’re a programmer seeking to swap into an thrilling new profession observe, or an information analyst seeking to make the transition into the tech trade – this course will train you the essential strategies utilized by real-world trade knowledge scientists.
Who this course is for:
- Software program builders or programmers who need to transition into the profitable knowledge science and machine studying profession path will be taught so much from this course.
- Technologists interested by how deep studying actually works
- Knowledge analysts within the finance or different non-tech industries who need to transition into the tech trade can use this course to discover ways to analyze knowledge utilizing code as an alternative of instruments. However, you may want some prior expertise in coding or scripting to achieve success.
- When you have no prior coding or scripting expertise, it’s best to NOT take this course – but. Go take an introductory Python course first.