Machine Learning
Best Machine Learning Certification for Free
Invest only 2 Hours/Day to learn Python and get Certified in Machine Learning!
Machine learning is the science of getting computers to learn without being explicitly programmed.
— Sebastian Thrun
Are you,
💡 A beginner in Machine Learning or even data science? 💡 Looking for the mathematics behind machine learning algorithms? 💡 Curious about machine learning? 💡 Looking for refreshing your machine learning knowledge and improve it further?
This is your one-stop solution!!
Analytics Vidhya brings you a comprehensive course in Machine Learning for beginners.
Recently, I finished Machine Learning Certification Course for Beginners. Hence, writing down my experiences and learnings for everyone.
Read this story to know more about the course—
- What concepts you can learn. 🏆
- Which tools you can master. 🛠️
- Which projects you will do. 📈📊
- A detailed overview and flow of the course. 📚
- How to read all the Medium articles and stories. 💡
What did I learn from this course?
✅ To analyze the data efficiently using Python libraries Numpy and Pandas.
✅ Importance of Statistics and Exploratory Data Analysis (EDA) in the data science field.
✅ To build Machine Learning models using Linear Regression, Logistics Regression, and Decision Trees.
✅ To solve Classification and Regression problems using machine learning
✅ To evaluate the machine learning models using the right evaluation metrics
✅ To improve and enhance machine learning model’s accuracy through feature engineering
What tools did I learn from this course?
Python libraries for data manipulation 🎯
Python libraries for data visualization 🎯
Python libraries for machine learning 🎯
Python library for estimation of statistical models & for conducting statistical data exploration 🎯
What projects did I complete in this course?
Customer Churn Prediction
In this project, I was provided with customers' information such as age, gender, demographics along their transactions with the bank. The bank wanted me to identify customers likely to churn balances below the minimum balance in the next quarter.
So, my main task was to predict the tendency to churn the bank balance for each customer.
NYC Taxi Trip Duration Prediction
This project gave me chance to implement the skills gained throughout the course. The data about the taxi trips are obtained from the TLC (Taxi and Limousine Commission) in New York.
My main task was to extract important features and accurately predict trip durations for taxi trips in New York.
Did I get a certificate for this course? for Free?
Yes! Absolutely Free! 💯 Investing a few hours of my schedule 📆, I refreshed my machine learning concepts and received a lifetime valid certificate from Analytics Vidhya!
The complete course can be found here. 🔗
Looking for a Detailed Overview 📚 of the Course?
The course begins with a quick overview of machine learning, common terminologies in data science, and application fields for ML.
Moreover, the instructions for setting the system, whether it is Linux, Windows, or Mac, are clearly provided.
Once everything is set, it starts with an introduction to Python. Here you can download course handouts for Free. This 9 MB download 🎁 gives all the learning material for beginners in Python as well as specific modules needed for data science using Python.
The introduction is followed by subsequent topics in Python. Every new topic is first explained with a video followed by a quick quiz 📊 to evaluate your understanding. You can also download a Jupyter-Notebook for a specific topic.
After finishing 25% of the course, there will be an introduction to the Python libraries for Data Science. This introduction is then followed by relevant topics such as Reading Data Files in Python, Pandas DataFrames, and Data Visualization. 📊
Completing an additional 13% of the course, there is an introduction to predictive modeling which explains the 6 steps of the Machine Learning lifecycle. This session is followed by an interesting discussion 💭 about inferential and descriptive statistics and their importance in data science.
In the very next session, you will be creating your First Predictive Model! This marks the completion of 50% of your ML course. 🏆
There is a brief introduction to different types of evaluation matrices for regression as well as classification models, followed by data preprocessing. Multiple ways of dealing with missing values and outliers are highlighted in this section.
The remaining 40% of the course focuses on building Machine Learning Models and feature engineering. The topics discussed in this last and important part of the course include —
🔹 Implementation of k-Nearest Neighbours algorithm 🔹 Overfitting and underfitting models 🔹 Implementing Validation methods: Hold-Out, Stratified Hold-Out, Leave One Out, and k-fold Cross-Validation 🔹 Bias Variance Tradeoff 🔹 Implementing Linear Regression, Logistics Regression 🔹 Implementing Regularization 🔹 Decision Tree 🔹 Feature transformation, scaling, encoding
Thank you for your time!
I am always open to sharing my experiences about this course and my data analytics journey so far. We can always connect through LinkedIn and the comments section.
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❓ Still, have questions — how to sign up for this course?
Machine Learning Certification Course for Beginners by Analytics Vidhya!