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Last week, I wrapped up Training a batch of learners in a Six-month long Data Science, ML, and AI program. This was definitely not the first time I taught a group of learners/professionals, but this experience stood out due to the consistency required over six months and the minimal breaks that I was required to take. It has been a rewarding journey from many aspects and I am delighted to share the experience and learning. 🙏 Teaching live sessions consistently for such a long period made me realize how tough a teacher's job truly is. This experience has deepened my respect for regular teachers who not only teach multiple classes/batches and long hours but also contribute to student evaluations, their overall development, and continuously upgrade their own knowledge through research. ⭕ One of the most satisfying moments for me as a Trainer was connecting the learning from one topic and applying it to another, especially the basic and seemingly mundane Python programming concepts taught in the beginning to advanced Deep Learning topics, showcasing their relevance and importance. ⭕ One of the biggest challenges I foresaw was to not only make core concepts like Programming (Functions, Vectorization, OOPs, etc.), Machine Learning Mathematics, and the First Principles working of ML/DL algorithms easy to follow for learners from non-STEM backgrounds but also teaching them to the levels required for industry and on-the-job application. ⭕ Handling a large batch of 180+ learners and consistently receiving positive feedback was an accomplishment I'm particularly proud of. This feedback reinforced my belief in the importance of patience and consistent effort, especially when interacting with learners of varied experience. In this cohort, I engaged with several senior learners who undoubtedly realized that with patience and consistent effort, tangible results emerge, even when the initial learning curve appears daunting ⭕ This experience has made me believe even more strongly that as a teacher, you develop a profound responsibility for your students' progress, particularly for those beginning with no prior background. This responsibility serves as a driving force, compelling you to ensure that every learner not only grasps but also effectively applies the concepts being taught. I want to reach out to my fellow trainers and teachers and urge them to help build a community where we can exchange thoughts, share experiences, and collaborate on similar engagements. #DataScience #MachineLearning #AI #Teaching #LearningAndDevelopment #CorporateTraining #Education

Having spent over two years navigating the realms of data science and machine learning, both in academic settings and practical applications, I've encountered some thought-provoking patterns that merit discussion. Let's delve into them. One recurring theme across various master's programs, not just in data science, is the tendency to revisit foundational concepts in the initial year. While revisiting basics can be beneficial for reinforcing knowledge, it can also feel like a step backward. After all, one would expect a master's program to dive into more advanced and stimulating topics from the get-go. Furthermore, there's often a disconnect between expectations and reality when it comes to the curriculum. For instance, in the field of data science, one might anticipate in-depth coverage of statistics, machine learning, and data analysis. However, the curriculum sometimes leans towards introductory material, leaving students yearning for more substantial content. Boosting algorithms, such as XGBoost and LightGBM, have demonstrated superior performance, particularly in tabular data analysis—a critical aspect of modern data science practice. However, the unfortunate reality is that many master's programs fail to include comprehensive coverage of these algorithms, leaving students ill-prepared to tackle real-world challenges. This oversight becomes even more pronounced when juxtaposed with the undue emphasis placed on other methodologies, such as deep learning techniques. While undoubtedly valuable in certain contexts, deep learning is not a one-size-fits-all solution. Yet, its prominence in academia often eclipses the practical utility of other algorithms, including boosting techniques. Moreover, there's a common misconception that mastery of a subject is synonymous with the completion of a master's program. However, the reality is more nuanced. A degree is undoubtedly valuable, but true mastery often requires ongoing learning and practical application beyond the classroom. This begs the question: Are traditional master's programs adequately preparing students for the realities of their respective fields? Should there be a greater emphasis on practical skills and real-world applications? And how can we bridge the gap between academic theory and practical proficiency? Thinking about these questions shows that there's space to make master's programs better. We can look at changing how we organize the curriculum, giving more chances for hands-on learning, and encouraging lifelong learning. The main aim is to create a learning environment where students learn not just theory, but also practical skills and critical thinking. It's something we all need to work on together by talking, trying new things, and working together—teachers, students, and people in industry. #datascience #machinelearning #education #mastersdegree

Greeting connections, This post brings to you one of my recent article, 'Machine Learning Starters for College Students', which is intended to help college students who want to explore Data Science and Machine Learning domain. This post is a followup for many queries which I have got through around 2 years of mentoring of students with diverse backgrounds, technical skills and interests in different technical clubs. I have always tried to give a suggestion which considers the individual goals of the person, but this article presents to you a wide view of topics, which you can tune as per your own personal background and goals. I expect this article to reach students of first, second and in cases third year for planning out their roadmaps, as well to get insights from what I have understood in last 3 years of learning and working in ML. Some other articles are in my drafts, so whenever I get time, I will publish them, stay tuned or follow me on Medium. I know this article is bit lengthy and has barely any graphic content but do read it. I will try to improve it with time, with suggestions in comments. Feel free to reach out to me, I will try my best to help you out. Also, Share it with your friends who are looking up to explore ML. Link for the article: https://lnkd.in/gViAJ6uj Tags: #machinelearning #datascience #dataanalysis #dataanalytics #deeplearning #dataengineering #data #fresher #collegestudents #roadmap #ml #artificialintelligence #ai #community #technicalskills #collegeplanning

Strongly feel that "Data Science, AI & ML" courses are the latest "White Hat Junior" in the market. Was speaking to a younger acquaintance who has a humanities degree currently pursuing an online data science course. Was curios to know how can anyone with no mathematical background (nor interest) can become an expert in Data Science. Apparently, the maths behind the science is an "optional" component of these courses. The people taking the course happily ignore it and are taught dry coding, which is nothing but learning syntax commands. Courses also offer free internship. Its a win-win for the start ups as they can get their grunt work done for free. Students are made to believe that they are learning cutting edge technology. Having decent amount of experience in DS and ML, would like to share some harsh realities: 1. DS/AI/ML is HARD because it is supposed to be HARD. Remember, cartesian geometry in 12th class. Dumbing it down, but it was the easiest mathematical representation of physical world. When a computer reads an image, it is reading a mathematical representation of multiple variables in multiple dimensions. If you found basic 12th class mathematics tough, you should probably think twice before trying your hand at ML/AI. 2. Without getting into the Maths, all you can do is code a problem which can be stated in plain English. By design, if the problem can be quoted in plain english, it can be automated. Which means your job can be automated. Hence, you are only relevant till the point you are cheaper than the technology that is required to automate your job. Coding standalone is a useless skill. Because it keeps getting easier and easier. And as more and more people learn coding, the value keeps decreasing (Simple economics) 3. There are very very few jobs globally which require cutting edge ML/AI skills. You will have some standalone anecdotal stories where people with no Maths/science background get into AI. But i find it highly unlikely, that short online courses are creating ML/AI experts which are being hired by Amazon, Microsoft. Its quite possible that the number of people enrolled in ML courses are more than the total number of jobs available in the world. DO NOT want to discourage anyone who want to learn a new skill. But we all know what happened with White Hat Junior. People should do proper research and make an informed choice.

I am thrilled to announce the successful completion of my Postgraduate Program in Data Science, Machine Learning, and Neural Networks! 🚀📊 This journey has been incredibly rewarding, and I am excited to share the highlights with you: 1️⃣ Skills Developed: During this intensive program, I have acquired a diverse range of skills essential to the field of data science and machine learning: Data Analysis and Visualization: I've mastered techniques to extract meaningful insights from complex datasets, using tools like Python, R, SQL, and Power BI. Machine Learning Algorithms: I am proficient in implementing supervised and unsupervised learning algorithms, including decision trees, random forests, clustering methods, and more. 2️⃣ Deepened Knowledge: My understanding of statistical methods and their application in data science has been enriched: Statistical Analysis: I have applied advanced statistical techniques to validate models and interpret data patterns effectively. Model Optimization: Through rigorous experimentation, I've learned to fine-tune model parameters and improve performance metrics like accuracy and precision. 3️⃣ Gratitude: I extend my heartfelt gratitude to DataTrained Education Pvt. Ltd. for providing a comprehensive curriculum led by experienced faculty members. Their guidance and mentorship have been invaluable throughout this educational journey. I also want to thank my peers for their collaboration and the stimulating discussions that enriched my learning experience. 4️⃣ Future Aspirations: Armed with these skills, I am eager to make meaningful contributions in the field of Data Science: Impactful Solutions: I aspire to leverage data-driven insights to solve complex challenges and drive innovation within organizations. Continuous Learning: My journey doesn’t end here. I am committed to staying updated with the latest advancements in data science and machine learning to deliver cutting-edge solutions. 5️⃣ Connect with Me: I invite you to connect with me to explore potential collaborations, share insights, or discuss opportunities where I can contribute my skills and knowledge. Let's connect and explore how we can create a positive impact together! #DataScience #MachineLearning #NeuralNetworks #Graduation #AI #DataDrivenDecisions #Innovation Thank you for being a part of my journey. I look forward to connecting with fellow professionals who share a passion for data science and machine learning. Here's to new beginnings and exciting opportunities ahead! 🚀

Career Pivot: From Any Field to Data Science & AI 🎯 Thinking of switching gears to a career in Data Science or AI? It's absolutely doable, and I'm here to share a few stepping stones on that path. 🚀 Before Python or R: Building a Strong Foundation While Python and R are the go-to languages, a solid foundation in core concepts is KEY. - Mathematics: Brush up on linear algebra, calculus, and statistics. These are the backbone of many data science and AI algorithms. - Programming Logic: Get comfortable with the fundamentals of programming - variables, loops, functions, etc. This helps you grasp coding concepts faster, even if you start with a language other than Python or R. - Problem-Solving: Hone your ability to break down complex problems into smaller, manageable parts. This is crucial for analyzing data and developing AI models. Soft Skills: The Unsung Heroes Technical skills are important, but don't underestimate the power of these: - Communication: Be able to explain complex findings in simple terms to both technical and non-technical audiences. - Curiosity: A thirst for knowledge and a drive to uncover insights from data are essential. - Adaptability: The field evolves rapidly. Be ready to learn new tools and techniques continuously. Resources to Kickstart Your Journey: - Online Courses: Platforms like Coursera, edX, and Udemy offer a wealth of courses to get you started. - Books: "Python for Data Analysis" by Wes McKinney and "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" are great reads. - Communities: Join online forums and groups to connect with like-minded individuals and learn from their experiences. Remember, the journey may be challenging, but it's incredibly rewarding. If you're passionate about data and problem-solving, take the leap! 💪 #DataScience #AI #CareerChange #Upskilling #Learning P.S. If you're considering this transition or have any questions, feel free to drop a comment below. Happy to share more insights!

The rise of Large Language Models (LLMs) heralds a transformative period for data science, necessitating a reevaluation and evolution in Data Science Education. These recent developments present new opportunities for integrating and enhancing teaching efficacy in data science, revolutionizing the way educators and students approach the field. LLMs offer a wealth of possibilities for enriching the teaching and learning experience. One of the key benefits is the ability to design engaging and dynamic curricula. Instructors can now create courses in Deep Learning and Machine Learning with an industry-specific approach, such as Deep Learning for Biology, providing students with practical and relevant knowledge. Another potential breakthrough is the use of AI tutors to respond to student questions and provide tailored study recommendations. Tools like Colab AI or Github Copilot can act as coding assistants, offering real-time feedback and guidance to students as they learn to code. By embracing LLMs in data science education, we can enhance the learning experience, better prepare students for real-world applications, and ultimately, advance the field of data science as a whole. Let's embrace these innovations and transform the future of data science education together! #datascience #datascienceeducation #llms #generativeai

Many of you have been asking me to share the Google Interview experience. So, since, I’ve mentored 100s of students till now, sharing the major topics from which questions were asked 🎉 → 📌 Machine Learning & Algorithms: Questions covered a broad range, including comparing neural networks with traditional models like decision trees, explaining the intuition behind convolutional neural networks (CNNs), and discussing the biases in model predictions. They also delved into practical applications, such as designing an A/B testing framework and optimizing a search ranking algorithm. 📌 Python & Coding: Tasks involved writing functions for data manipulation using Pandas, optimizing code for large datasets, and implementing machine learning models from scratch. One challenging question asked was to efficiently calculate the moving average for a time series—demonstrating the importance of both algorithmic thinking and coding proficiency. 📌 SQL & Data Analysis: The technical interviews included SQL-intensive questions, such as constructing complex queries to identify trends in user engagement data and performing multi-table joins to calculate lifetime value metrics. A deep understanding of window functions, subqueries, and performance tuning was essential. 📌 Behavioral Questions: Google's emphasis on "Googliness" and problem-solving approaches were critical, with questions exploring how the candidate collaborates, leads in ambiguity, and innovates in the face of challenges. Now, if you're targetting for Data roles and struggling to prepare better, I’d suggest taking the help of industry veterans at Bosscoder Academy. Check them here: https://bit.ly/4cm8eux They offer: ✅ A structured curriculum covering Data Visualization, Deep Learning, Machine Learning, and more ✅ Job-ready training for top companies ✅ Personal mentorship from experienced Data professionals ✅ Live classes, project-based learning, and access to a community of like-minded individuals #google #collab #interviewpreparation #datascience

𝟑𝟎𝟎 𝐡𝐨𝐮𝐫𝐬 𝐨𝐟 𝟏𝟓 𝐜𝐮𝐫𝐚𝐭𝐞𝐝 𝐜𝐨𝐮𝐫𝐬𝐞𝐬 𝐨𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝕗𝕠𝕣 𝕗𝕣𝕖𝕖 - December 31st, 2023 These are all from Google, totally free, 𝐟𝐫𝐨𝐦 𝐛𝐞𝐠𝐢𝐧𝐧𝐞𝐫 𝐥𝐞𝐯𝐞𝐥 𝐭𝐨 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐥𝐞𝐯𝐞𝐥. Some of the topics covered include: - Fundamentals of Machine Learning - Feature Engineering - Production Machine Learning Systems - Computer Vision and Natural Language - Recommendation Systems - MLOps - TensorFlow, Google Cloud, VertexAI The courses are well structured. They aren't just links to YouTube videos. You have to join the course, and they have an interface that takes you through every module. This is a great content. And it's free. This is your best bet to level up with ML knowledge and skills in 2024. https://lnkd.in/eSHUuF3y Credits: Santiago Valdarrama Follow DataGlobal Hub for more… We cover free online courses, workshops, book releases, job postings, events, summits, the latest and trending news, innovations, hackathons and competitions, job market insights, nuggets from Thought Leaders, and other resources in the data niche. #DataEngineering #DataObservability #DataQuality #DataPipeline #DataTeam #Data #Analytics #AI #Career #Future #technology #datascience #machinelearning #llm #chatgpt #faang #openai #google #facebook #microsoft #dataanalysis #sql #python #project #tech #statistics #portfolio #projects #queries #free #dataanalytics #dataanalyst #learning #optimization #innovation

Many of you have been asking me to share the Google Interview experience. So, since, I’ve mentored 100s of students till now, sharing the major topics from which questions were asked 🎉 → 📌 Machine Learning & Algorithms: Questions covered a broad range, including comparing neural networks with traditional models like decision trees, explaining the intuition behind convolutional neural networks (CNNs), and discussing the biases in model predictions. They also delved into practical applications, such as designing an A/B testing framework and optimizing a search ranking algorithm. 📌 Python & Coding: Tasks involved writing functions for data manipulation using Pandas, optimizing code for large datasets, and implementing machine learning models from scratch. One challenging question asked was to efficiently calculate the moving average for a time series—demonstrating the importance of both algorithmic thinking and coding proficiency. 📌 SQL & Data Analysis: The technical interviews included SQL-intensive questions, such as constructing complex queries to identify trends in user engagement data and performing multi-table joins to calculate lifetime value metrics. A deep understanding of window functions, subqueries, and performance tuning was essential. 📌 Behavioral Questions: Google's emphasis on "Googliness" and problem-solving approaches were critical, with questions exploring how the candidate collaborates, leads in ambiguity, and innovates in the face of challenges. Now, if you're targetting for Data roles and struggling to prepare better, I’d suggest taking the help of industry veterans at Bosscoder Academy. Check them here: https://bit.ly/4dr79mz They offer: ✅ A structured curriculum covering Data Visualization, Deep Learning, Machine Learning, and more ✅ Job-ready training for top companies ✅ Personal mentorship from experienced Data professionals ✅ Live classes, project-based learning, and access to a community of like-minded individuals #google #collab #interviewpreparation #datascience