Welcome to the best Machine Learning and Data Science with Python course in the planet. Are you ready to start your journey to becoming a Data Scientist?

In this comprehensive course, you’ll begin your journey with installation and learning the basics of Python. Once you are ready, the introduction to Machine Learning section will give you an overview of what Machine Learning is all about, covering all the nitty gritty details before landing on your very first algorithm. You'll learn a variety of supervised and unsupervised machine learning algorithms, ranging from linear regression to the famous boosting algorithms. You’ll also learn text classification using Natural Language processing where you’ll deal with an interesting problem.

Data science has been recognized as one of the best jobs in the world and it’s on fire right now. Not only it has a very good earning potential, but also it facilitates the freedom to work with top companies globally. Data scientists also gets the opportunity to deal with interesting problems, while being invaluable to the organization and enjoy the satisfaction of transforming the way how businesses make decisions. Machine learning and data science is one of the fastest growing and most in demand skills globally and the demand is growing rapidly. Parallel to that, Python is the easiest and most used programming language right now and that’s the first language choice when it comes to the machine learning. So, there is no better time to learn machine learning using python than today.

I designed this course keeping the beginners and those who with some programming experience in mind. You may be coming from the Finance, Marketing, Engineering, Medical or even a fresher, as long as you have the passion to learn, this course will be your first step to become a Data Scientist.

I have over 19 hours of best quality video contents. There are over 90 HD video lectures each ranging from 5 to 20 minutes on average. I’ve included Quizzes to test your knowledge after each topic to ensure you only leave the chapter after gaining the full knowledge. Not only that, I’ve given you many exercises to practice what you learn and solution to the exercise videos to compare the results. I’ve included all the exercise notebooks, solution notebooks, data files and any other information in the resource folder.

Now, I'm gonna answer the most important question. Why should you choose this course over the other courses?

1. I cover all the important machine learning concepts in this course and beyond.

2. When it comes to machine learning, learning theory is the key to understanding the concepts well. We’ve given the equal importance to the theory section which most of the other courses don’t.

3. We’ve used the graphical tools and the best possible animations to explain the concepts which we believe to be a key factor that would make you enjoy the course.

4. Most importantly, I’ve a dedicated section covering all the practical issues you’d face when solving machine learning problems. This is something that other courses tend to ignore.

5. I’ve set the course price to the lowest possible amount so that anyone can afford the course.

Here a just a few of the topics we will be learning:

• Install Python and setup the virtual environment

• Learn the basics of Python programming including variables, lists, tuples, sets, dictionaries, if statements, for loop, while loop, construct a custom function, Python comprehensions, Python built-in functions, Lambda functions and dealing with external libraries.

• Use Python for Data Science and Machine Learning

• Learn in-dept theoretical aspects of all the machine learning models

• Open the data, perform pre-processing activities, build and evaluate the performance of the machine learning models Implement Machine Learning Algorithms

• Learn, Visualization techniques like Matplotlib and Seaborn

• Use SciKit-Learn for Machine Learning Tasks

• K-Means Clustering

• DBSCAN Clustering

• K-Nearest Neighbors

• Logistic Regression

• Linear Regression

• Lasso and Ridge - Regularization techniques

• Random Forest and Decision Trees and Extra Tree

• Naïve Bayes Classifier

• Support Vector Machines

• PCA - Principal Component Analysis

• Boosting Techniques - Adaboost, Gradient boost, XGBoost, Catboost and LightGBM

• Natural Language Processing

• How to deal with the practical problems when dealing with Machine learning