Data Science & Deep Learning for Business™ 20 Case Studies

h264, yuv420p, 1280x720 |ENGLISH, aac, 44100 Hz, 2 channels | 20h 31 mn | 2.6 GBInstructor: Rajeev D. RatanLearn to use Python, Pandas, Matplotlib & Seaborn, SkLearn, Keras, Tensorflow, NLTK, Prophet, PySpark, MLLib and more!

Use Python for Data Analysis, Data Science in Marketing & Retail, Recommendations, Forecasts, Customer Clustering & NLP What you'll learnUnderstand the value of data for businessesApply Data Science in Marketing to improve Conversion Rates, Predict Engagement and Customer Life Time ValueMachine Learning from Linear Regressions (polynomial & multivariate), K-NNs, Logistic Regressions, SVMs, Decision Trees & Random ForestsUnsupervised Machine Learning with K-Means, Mean-Shift, DBSCAN, EM with GMMs, PCA and t-SNEBuild a Product Recommendation Tool using collaborative & item/content basedHypothesis Testing and A/B Testing - Understand t-tests and p valuesNatural Langauge Processing - Summarize Reviews, Sentiment Analysis on Airline Tweets & Spam DetectionTo use Google Colab's iPython notebooks for fast, relaible cloud based data science workDeploy your Machine Learning Models on the cloud using AWSAdvanced Pandas techniques from Vectorizing to Parallel ProcesssngStatistical Theory, Probability Theory, Distributions, Exploratory Data AnalysisPredicting Employee Churn, Insurance Premiums, Airbnb prices, credit card fraud and who to target for donationsBig Data skills using PySpark for Data Manipulation and Machine LearningCluster customers based on Exploratory Data Analysis, then using K-Means to detect customer segmentsBuild a Stock Trading Bot using re-inforement learningApply Data Science & Analytics to Retail, performing segementation, analyzing trends, determining valuable customers and more!RequirementsFamiliar with basic programming conceptsHighschool level math knowledgeBroadband Internet connectionDescriptionWelcome to the course on Data Science & Deep Learning for Business 20 Case Studies!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.However, Data Science has a difficult learning curve - How does one even get started in this industry awash with mystique, confusion, impossible-looking mathematics, and code? Even if you get your feet wet, applying your newfound Data Science knowledge to a real-world problem is even more confusing.This course seeks to fill all those gaps in knowledge that scare off beginners and simultaneously apply your knowledge of Data Science and Deep Learning to real-world business problems.This course has a comprehensive syllabus that tackles all the major components of Data Science knowledge.Our Learning path includes:How Data Science and Solve Many Common Business ProblemsThe Modern Tools of a Data Scientist - Python, Pandas, Scikit-learn, Seaborn, Matplotlib & Plotly (Manipulate Data and Create Information Captivating Visualizations and Plots).Statistics for Data Science in Detail - Sampling, Distributions, Normal Distribution, Descriptive Statistics, Correlation and Covariance, Probability Significance Testing and Hypothesis Testing.Machine Learning Theory - Linear Regressions, Logistic Regressions, Decision Trees, Random Forests, KNN, SVMs, Model Assessment, Outlier Detection, ROC & AUC and RegularizationDeep Learning Theory and Tools - TensorFlow 2.0 and Keras (Neural Nets, CNNs, RNNs & LSTMs)Solving problems using Predictive Modeling, Classification, and Deep LearningData Science in Marketing - Modeling Engagement Rates and perform A/B TestingData Science in Retail - Customer Segmentation, Lifetime Value, and Customer/Product AnalyticsUnsupervised Learning - K-Means Clustering, PCA, t-SNE, Agglomerative Hierarchical, Mean Shift, DBSCAN and E-M GMM ClusteringRecommendation Systems - Collaborative Filtering and Content-based filtering + Learn to use LiteFM