Projects and Case Studies on Machine Learning with Python

There are lots of Python courses and lectures out there. However, Python has a very steep learning curve and students often get overwhelmed. This course is different. This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward. After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples. This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises. In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course. Apply Data Science using Python, Statistical Techniques, EDA, Numpy, Pandas, Scikit Learn, Statsmodel Libraries.

Are you aspiring to become a Data Scientist or Machine Learning Engineer? if yes, then this course is for you. In this course, you will learn about core concepts of Data Science, Exploratory Data Analysis, Statistical Methods, role of Data, Python Language, challenges of Bias, Variance and Overfitting, choosing the right Performance Metrics, Model Evaluation Techniques, Model Optmization using Hyperparameter Tuning and Grid Search Cross Validation techniques, etc. You will learn how to perform detailed Data Analysis using Pythin, Statistical Techniques, Exploratory Data Analysis, using various Predictive Modelling Techniques such as a range of Classification Algorithms, Regression Models and Clustering Models. You will learn the scenarios and use cases of deploying Predictive models. This course covers Python for Data Science and Machine Learning in great detail and is absolutely essential for the beginner in Python. Most of this course is hands-on, through completely worked out projects and examples taking you through the Exploratory Data Analysis, Model development, Model Optimization and Model Evaluation techniques. This course covers the use of Numpy and Pandas Libraries extensively for teaching Exploratory Data Analysis. In addition, it also covers Marplotlib and Seaborn Libraries for creating Visualizations.