CL Data School

Hands-on Machine Learning

Unlocking a bright career in machine learning

If the world of Machine learning, Artificial intelligence and Data science excites you, you have come to the right place. Take this course to acquire all the necessary skills to become a certified Data scientist. We would take you through all the techniques to build and deploy advanced machine learning models, exciting and employment relevant hands-on projects. At the end of the course a machine learning internship is guaranteed at CL or a partner company to ensure you get meaningful industry experience.

The CL Advantage

  • Industry relevant internships and placements at CL Educate and other partner companies is guaranteed.

  • Employment relevant interview preparations

  • Mock interviews

  • Deploying machine learning models in production as APIs

  • Hands-on projects on real world business problems (closely correlated with what a data science engineer is expected to deliver at a reputed firm)

  • Advanced techniques for building state of the art models

  • Extensive online and offline support by highly qualified CL professionals

What you'll master:

  1. Industry relevant skills such as understanding how algorithms for Classification, Regression, Artificial Neural Networks, time series forecasting work
  2. Exploratory Data analysis (find insights by plotting graphs from the given data)
  3. Cracking interviews for a data scientist, data analyst profile
  4. Probability and Statistics for Machine Learning
  5. Advanced techniques to win Hackathons and Kaggle competitions
  6. Become an advanced expert in machine learning

Course Content

  • What is ML, AI, Data Science?

  • Installing python and setting up Anaconda environment.

  • Introduction to Kaggle

  • Introduction to python for machine learning

  • Introduction to Numpy, Pandas, Matplotlib

  • Main challenges of Machine Learning

  • An end to end machine learning project

  • Generalised Linear regression Models and other regression techniques

  • Model evaluation techniques for regression

  • Basic classification algorithms like logistic regression, Naive Bayes

  • Model Evaluation metrics for classification

  • Dimensionality reduction (PCA, LDA)

  • Probability and Statistics and mathematics for machine learning

  • Identifying outliers in your dataset

  • Anomaly Detection

  • Advanced Data Visualisation/Exploratory data analysis techniques

  • Advanced Data Preprocessing techniques

  • Support Vector Machines

  • Classification and Regression trees (CART models)

  • Boosting algorithms for winning Hackathons and Kaggle competitions

  • Techniques for build state of the art models

  • Unsupervised learning

  • Feature engineering

  • Artificial neural networks

  • Deep Learning

  • Convolution neural networks

  • Time Series forecast

  • Natural Language Processing

  • Building your own smart search engine

  • Deploying machine learning models in production as APIs using Flask

  • Mock interviews

  • Interview preparations

  • Hands on problem solving

  • Hackathon


  • Graduation party !


Thanks to the media, most of us are aware of the promise and the potential of AI. We even have a somewhat good idea of what it is being used for today. But as the development of artificial intelligence progresses it is no longer something that we can just appreciate from afar. While AI is expected to one day be able to replace all of our jobs, the more immediate impact is in its ability to analyse and bring meaningful insights and predictions from the vast stockpiles of data available to companies today. The sub domain of artificial intelligence that deals with this field is machine learning. The biggest differentiator going forward for businesses will be in their mastery of the possibilities and limitations of machine learning.

Unlike other innovations in the past, being on top of the machine learning revolution is not as straightforward as having machine learning specialists in every company. Everyone will need to be well versed in the application of machine learning. Just as one cannot in this day and age function without being able to read and write, in tomorrow’s world machine learning will become the new literacy. According to the World Economic Forum1, over half of the jobs in the world will be impacted by artificial intelligence in the next five years and many of those people will need to be reskilled.

Along with the great advances of machine learning came the democratization of machine learning. A vast array of powerful tools are available for everyone and they are free to use free to take apart, learn from and improve upon. It is now easier than ever before to get your toes wet in machine learning. No longer is it solely the domain of the upper echelons of mathematicians and computer scientists. Not only can anyone learn machine learning, everyone should learn it.

The bar of entry to become proficient in machine learning is surprisingly low. You can start today with a knowledge of grade school mathematics, a computer and a willingness to learn.

Should I learn Online ? While online learning is the dominant platform to learn machine learning today, it’s not for everyone. While many, including our in house data science team have learnt from online courses, we feel it is inferior to the experience of classroom coaching. One needs a very strong drive to complete an online course as evidenced by the very high dropout rates among the viewers of Andrew Ng’s videos. There is also little to no recourse for doubt clarification if you are stuck with something.

There are many applications of machine learning to sales roles. For example machine learning can be used to interpret and gain meaningful insights from customer data. Having a data driven understanding of your customer base is gives your company an edge in making marketing decisions as you can know what your customers want with data rather than intuition. Forecasting is another sales task that is falls squarely in the domain of things machine learning excels at. Given enough data, machine learning models can make very accurate sales forecasts with little human intervention or effort. Sales communication is another forte of machine learning as many simple to moderately complex enquiries about sales or promotions can be handled by AI chatbots or machine learning enabled email reply bots leaving the humans to handle the more creative and complex tasks. Machine learning can also be employed to track the discussions and opinions of your brand on social media and even make intelligent responses to maintain a positive atmosphere around the brand.

There are a number of HR functions that can either be enhanced or done entirely by AI. Attrition detection is one of the most important functions of HR, understanding why employees stay at or leave a job is a good job for machine learning. Given the data, machine learning algorithms may find patterns and insights into employees decisions that HR professionals may overlook. This data can then be used to make corrections in HR policies. Processing prospective employees is another major task. It requires HR professionals to dig through a vast number of CVs to find the right candidate. Often times the best candidates get offers elsewhere before you can get a chance to see his or her resume, so speedy turnaround times are also necessary. Machine learning algorithms make combing through large number of resumes an easy task so you can get the perfect candidate before everyone else.

Definitely! While a background in mathematics or programming will no doubt be an invaluable asset to a data scientist, one does not need it to apply machine learning to their businesses. The most essential ingredients in becoming a good data scientist are creativity and a mastery of the data.

Contact Us

Have questions? Our data science team is more than happy to take them:-

Tathagat Jha -
Mythreya Lingala -

At a glance

  • 3 months
  • ₹50,000
  • Classroom + Online
  • Hands-on & project oriented