Online Courses Udemy - Data science techniques for professionals and students - learn the theory behind logistic regression and code in Python BESTSELLER Created by Lazy Programmer Inc English [Auto-generated], Portuguese [Auto-generated], 1 more Students also bought Data Science: Deep Learning in Python Natural Language Processing with Deep Learning in Python Advanced AI: Deep Reinforcement Learning in Python Deep Learning: Advanced NLP and RNNs Deep Learning A-Z: Hands-On Artificial Neural Networks Preview this course GET COUPON CODE Description This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.
Facial emotion detection is a common issue focused on in the field of cognitive science. An attempt to understand what exactly we as humans see in each other that gives us insight into other emotions is a challenge we can approach from an artificial intelligence side. While I don't have enough experience in psychology or even artificial intelligence to determine these factors, we can always start off by building a model to determine at least the start of this question. Fer2013 is a dataset with pictures of individuals labeled with the emotions of anger, happiness, surprise, disgust, and sadness. When testing humans on the dataset to correctly identify the facial expression of a set of pictures within the set, the accuracy is 65%.
Training-serving skew: The offline numbers may look great, but what if your model will be evaluated on a different or broader set of examples than those found in the training set? This phenomenon, more generally referred to as "dataset shift" or "distribution shift", happens all the time in real-world situations. Models are trained on a curated set of examples, or clicks on top-ranked recommendations, or a specific geographical region, and then applied to every user or use case. Curiosities and anomalies in your training and testing data become genuine and sustained loss patterns. Bad individual errors: Models are often judged by their worst behavior --- a single egregious outcome can damage the faith that important stakeholders have in the model and even cause serious reputational harm to your business or institution.
Knowing the mathematics behind machine learning algorithms is a superpower. If you have ever built a model for a real-life problem, you probably experienced that being familiar with the details can go a long way if you want to move beyond baseline performance. This is especially true when you want to push the boundaries of state of the art. However, most of this knowledge is hidden behind layers of advanced mathematics. Understanding methods like stochastic gradient descent might seem difficult since it is built on top of multivariable calculus and probability theory.
"Machine Learning foners Second Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home. This major new edition features many topics not covered in the First Edition, including Cross Validation, Data Scrubbing and Ensemble Modeling."
All MBA's will preach that Data-Driven Methods udemy discount Understand the value of data for businesses Learn to use Python, Pandas, Matplotlib & Seaborn, SkLearn, Keras, Tensorflow, NLTK, Prophet, PySpark, MLLib and more! Apply Data Science in Marketing to improve Conversion Rates, Predict Engagement and Customer Life Time Value Machine Learning from Linear Regressions (polynomial & multivariate), K-NNs, Logistic Regressions, SVMs, Decision Trees & Random Forests Unsupervised Machine Learning with K-Means, Mean-Shift, DBSCAN, EM with GMMs, PCA and t-SNE Build a Product Recommendation Tool using collaborative & item/content based Hypothesis Testing and A/B Testing - Understand t-tests and p values Natural Langauge Processing - Summarize Reviews, Sentiment Analysis on Airline Tweets & Spam Detection To use Google Colab's iPython notebooks for fast, relaible cloud based data science work Deploy your Machine Learning Models on the cloud using AWS This course takes on Machine Learning and Statistical theory and teaches you to use it in solving 20 real-world Business problems. Data Scientist is the buzz of the 21st century for good reason! The tech revolution is just starting and Data Science is at the forefront. As a result, "Data Scientist has become the top job in the US for the last 4 years running!" according to Harvard Business Review & Glassdoor.
Machine Learning, Artificial intelligence (AI) and Deep Learning are taking the world by storm, dominating conversations about how machines can replace humans by providing a competitive advantage to businesses. The World is currently preparing to enter the fourth industrial revolution -- the rise of the "intelligent machine." At the heart of this revolution is Artificial Intelligence (AI), Mimicking human cognitive functions like problem-solving, learning and decision making using algorithms. From speed to efficiency, AI offers an abundance of benefits. Numerous sectors, including healthcare, automotive, defence and retail have already witnessed the game-changing impact of AI.
The challenge of speeding up AI systems typically means adding more processing elements and pruning the algorithms, but those approaches aren't the only path forward. Almost all commercial machine learning applications depend on artificial neural networks, which are trained using large datasets with a back-propagation algorithm. This result is compared to the known "correct" answer, and the difference between the two is used to adjust the weights applied to the network nodes. The process repeats for as many training examples as needed to (hopefully) converge to a stable set of weights that gives acceptable accuracy. This standard algorithm requires two distinct computational paths -- a forward "inference" path to analyze the data, and a backward "gradient descent" path to correct node weights.
If you're an AI professional or aspire to be one, one thing you must be aware of is: machine learning algorithms are your closest aid and ally. These algorithms can also be annoying. Given that there is a multitude of algorithms. The knowledge of algorithms is essential to be an effective AI engineer, data scientist, and machine learning engineer. To give you a gist of how these algorithms work, let's get down to know these algorithms.
When I was beginning my way in data science, I often faced the problem of choosing the most appropriate algorithm for my specific problem. If you're like me, when you open some article about machine learning algorithms, you see dozens of detailed descriptions. The paradox is that they don't ease the choice. Remember, the list of Machine Learning Algorithms I mentioned are the ones that are mandatory to have a good knowledge of, while you are a beginner in Machine/Deep Learning! Now that we have some intuition about types of machine learning tasks, let's explore the most popular algorithms with their applications in real life, based on their problem statements!