Data Science and Machine Learning using Python - A Bootcamp

Greetings, I am so excited to learn that you have started your path to becoming a Data Scientist with my course. Data Scientist is in-demand and most satisfying career, where you will solve the most interesting problems and challenges in the world. Not only, you will earn average salary of over $100,000 p.

a. , you will also see the impact of your work around your, is not is amazing. This is one of the most comprehensive course on any e-learning platform (including Udemy marketplace) which uses the power of Python to learn exploratory data analysis and machine learning algorithms.

You will learn the skills to dive deep into the data and present solid conclusions for decision making. Data Science Bootcamps are costly, in thousands of dollars. However, this course is only a fraction of the cost of any such Bootcamp and includes HD lectures along with detailed code notebooks for every lecture.

The course also includes practice exercises on real data for each topic you cover, because the goal is "Learn by Doing". For your satisfaction, I would like to mention few topics that we will be learning in this course:Basis Python programming for Data ScienceData Types, Comparisons Operators, if, else, elif statement, Loops, List Comprehension, Functions, Lambda Expression, Map and FilterNumPyArrays, built-in methods, array methods and attributes, Indexing, slicing, broadcasting & boolean masking, Arithmetic Operations & Universal FunctionsPandasPandas Data Structures - Series, DataFrame, Hierarchical Indexing, Handling Missing Data, Data Wrangling - Combining, merging, joining, Groupby, Other Useful Methods and Operations, Pandas Built-in Data VisualizationMatplotlibBasic Plotting & Object Oriented ApproachSeabornDistribution & Categorical Plots, Axis Grids, Matrix Plots, Regression Plots, Controlling Figure Aesthetics Plotly and CufflinksInteractive & Geographical plottingSciKit-Learn (one of the world's best machine learning Python library) including:Liner RegressionOver fitting , Under fitting Bias Variance Trade-off, saving and loading your trained Machine Learning ModelsLogistic RegressionConfusion Matrix, True Negatives/Positives, False Negatives/Positives, Accuracy, Misclassification Rate / Error Rate, Specificity, PrecisionK Nearest Neighbour (KNN)Curse of Dimensionality, Model PerformanceDecision TreesTree Depth, Splitting at Nodes, Entropy, Information Gain Random ForestsBootstrap, Bagging (Bootstrap Aggregation)K Mean ClusteringElbow Method Principle Component Analysis (PCA)Support Vector MachineRecommender SystemsNatural Language Processing (NLP) Tokenization, Text Normalization, Vectorization, Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Pipeline feature. and MUCH MORE.